critical success factor research methods

• Corpus ID: 67793266

The Critical Success Factor Method: A review and practical example

• Vanessa A. Cooper
• Published 2008
• Computer Science, Business

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Planning, Critical Success Factors, and Management's Information Requirements

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CONF-IRM 2008 Proceedings

The critical success factor method: a review and practical example.

Vanessa A. Cooper , RMIT University Follow

Since the CSF method was first proposed by John Rockart in 1979, the method has been adopted for numerous research studies in Information Systems (IS). Like many research methods, the CSF method has both its supporters and critics. Almost thirty years on, this paper provides a comprehensive review of the original CSF method and of subsequent adaptations. The primary contributions and criticisms of the method are synthesized. The paper then discusses insights gained from the application of an adaptation of the CSF method in a large study involving six multi-national IT services organisations, thereby providing guidance to researchers who may consider using the method in future research.

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Cooper, Vanessa A., "The Critical Success Factor Method: A review and practical example" (2008). CONF-IRM 2008 Proceedings . 53. https://aisel.aisnet.org/confirm2008/53

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What are critical success factors? Examples, definition, overview

Many of the techniques, frameworks, processes, and tools in wide use today were invented during the golden era of project management in the late 1990s. With these new methodologies came a need for project stakeholders to identify key areas and actions that were required for a project to succeed.

This is where critical success factors — the key areas a product or a project need to execute or consider for a project/product to be successful — enter the picture.

What are critical success factors?

A critical success factor (CSF) is a specific element or activity that is deemed essential for an organization to achieve its mission or goal. In product management, critical success factors are the key actions a product team takes to deliver successful products that solve user problems.

Critical success factors are different from critical success criteria and key performance indicators (KPIs) . Critical success factors are action-based statements that can be assigned to an owner. Think of a CSF as a task that needs to be executed for the product to be successful.

The term critical success factors was coined in a 1961 Harvard Business Review article by Ronald Daniel titled “ Management Information Crisis .” CSFs evolved more during the late 90s to help project managers identify what needed to be done to achieve product and business goals on a bigger scale.

Who sets critical success factors?

Critical success factors are often established by product leaders, such as the VP of product or chief product officer (CPO), who own the product development process in the organization.

In product-centric organizations, CSFs are embedded into the product development process, sometimes without product managers even noticing it.

What is the outcomes hierarchy?

CSFs are the second layer of the outcomes hierarchy. The outcomes hierarchy flows as follows:

Deliverable

Critical success factors (csfs), critical success criteria (csc), key performance indicators (kpis).

A deliverable could be any product, feature, or enhancement you are building. For a product team to build an effective feature, there should be three more layers: critical success factors (CSFs), critical success criteria (CSC), and key performance indicators (KPIs).

The critical success factor, as mentioned before, is what needs to be done to build a successful product. But it doesn’t make sense to have action steps without understanding the baseline of what success looks like. This is where CSCs and KPIs come in.

Critical success criteria are the benchmarks by which you measure the success of the feature or initiative you are pursuing. Think of it as the ultimate outcome you want to reach.

Examples of critical success criteria are increasing the number of monthly registered users, decreasing the time from searching to placing order for an ecommerce product, etc.

KPIs are a way to track your CSC quantitatively. So what signals might tell us that we achieved our CSC?

Referring back to our previous example, can we say we achieved success if only one new customer registers? Of course not.

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Coupling your CSC with quantifiable business goals will generate a KPI — for example, increasing the number of monthly registered users by 30 percent or reducing the time from searching to placing an order by three minutes.

Benefits of using critical success factors

Setting CSFs can help you:

Promote cross-functional collaboration

Streamline product and project management processes, align stakeholders.

When the entire company — not only the product team — embraces CSFs, cross-functional teams are much better equipped to work together with minimal friction. The list of CSFs serves as a compass to help these disparate teams navigate the broader roadmap of activities required to achieve product objectives.

By embedding CSFs into the development process, the company can establish a template for success in any initiative — e.g., building features, introducing enhancements. CSFs derived from past experience will increase the likelihood of the product being successful and achieving the objective.

Having established steps to achieve success will significantly result in better stakeholder alignment. This will help the product team encounter less noise from the outside departments and will allow the outside departments and divisions to have more clarity around the holistic success process taken to maximize the feature success chances.

How to identify critical success factors

Identifying critical success factors isn’t a one-off task; it’s an ongoing initiative. While there’s no one-size-fits-all approach, the general process for establishing critical success factors is as follows:

• Start with the product strategy
• Analyze old projects
• Collaborate with product leaders

1. Start with the product strategy

Critical success factors differ from organization to organization and from product to product. Your product strategy will answer most of your questions.

The product strategy should outline your product aspirations and goals. It’s essential to keep your product strategy in mind while building your first CSFs. This will help you and your product team stay aligned.

2. Analyze old projects

Have you built a feature or product that positively impacted all your success metrics? If so, then start there.

In a workshop, identify with your stakeholders what has impacted success, what went right, and what went wrong. Also any additional insights and opinions you can collect from other stakeholders.

3. Collaborate with product leaders

Now that you have synthesized stakeholders’ insights, you can start collaborating with product leaders to develop a first draft of your critical success factors. The draft should include tailored steps or key focus areas for your product team to deliver the perfect feature or enhancement.

Start executing based on your process and your CSFs. Validate the effectiveness of those success factors and measure whether the features or enhancements delivered generate the expected results.

Based on the results, you should always tweak your CSFs to accommodate new insights and changing conditions. Treat your CSFs as an ongoing project that you will continuously refine and improve with your stakeholders and, most importantly, your product team.

Critical success factors: Examples

There are many product development process components that could serve as a critical success factor. Below are four success factors I commonly see across startups and product organizations:

Build a clear product strategy

Understand customer pain points, analyze product performance regularly, create value continuously.

A clear product strategy is the first step for any product. Your strategy will guide the product team and help them identify their customers, introduce enhancements and features, and, more importantly, prioritize them based on the strategic goals.

Building shiny new features that don’t solve customer problems is unprofitable for the business. All it does is burn resources with no real business return on investment (ROI).

However, understanding customer pain points through surveys, customer interviews, observations, contextual inquiry, and focus groups will increase your chances of delivering real value to the user and, thus, positively impacting metrics such as retention and activation.

Analyzing your performance using product analytics tools or through qualitative methods such as interviews will help you and your team create a top-notch product. Analyzing the product performance enables the product team to ease critical flows for the customer, helping them achieve their tasks and solve their problems efficiently.

Creating value in a continuous manner and delighting the user will help establish your product in the market. By solving more and more problems your users face, you increase the chances they’ll stick around.

Final thoughts

Critical success factors (CSFs) — also called key results areas (KRAs) — are crucial for any product team to achieve success. CSFs help the product team define the areas, actions, and steps that are absolutely necessary to achieve success.

CSFs also help product teams assess the areas in which they excel and areas that need improvement. By holistically detecting the faulty key success areas, they can introduce tweaks to the process and build better products during subsequent stages.

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Understanding Critical Success Factors (CSFs) in Strategic Planning

Every business needs a roadmap for success. Without one, distinguishing victories from setbacks becomes a daunting task, casting uncertainty over the attainment of goals. Delving into the realm of Critical Success Factors (CSFs) unveils their pivotal role in steering the course of businesses and projects toward triumph.These factors serve as the guiding lights, ensuring teams and departments are synchronized, and efforts are channeled towards common objectives.

What Are Critical Success Factors?

Critical Success Factors (CSFs) are the essential elements that must be achieved to ensure success for a company or project. Understanding these factors is crucial as they help focus efforts on the most impactful areas. CSFs are not just about identifying what to do but also clarifying what not to waste resources on. They are tailored to specific industries and business models, making them unique and vital for strategic alignment.

These factors are crucial for the success of a project, initiative, or business strategy. CSFs vary depending on the industry, organization, and specific objectives, but they generally encompass the following characteristics:

Key Goals and Objectives: CSFs are directly linked to the primary goals and objectives of an organization or project. They represent the most critical aspects that must be achieved to consider the endeavor successful.

Measurability: CSFs should be measurable so that progress can be tracked effectively. They often have associated key performance indicators (KPIs) or metrics that indicate whether the factors are being met.

Strategic Alignment: CSFs align with the overall strategy and vision of the organization. They reflect the areas where the organization must excel to fulfill its strategic objectives.

Criticality : CSFs are essential for success. Failure to achieve these factors significantly increases the risk of failure for the project or organization as a whole.

Focus: CSFs help prioritize resources and efforts by highlighting the most critical areas that require attention and investment.

The Role of CSFs in Strategic Success

CSFs play a pivotal role in strategic planning by providing a clear roadmap for success. They help organizations prioritize their goals and allocate resources effectively. By defining critical success factors, companies can:

• Ensure that all team members are aligned with the strategic objectives.
• Measure progress quantitatively, as each CSF can be associated with specific performance metrics.
• Adapt to changing market dynamics by regularly reviewing and updating the CSFs.

For instance, in a technology company, a CSF might be the development of a new patentable technology, whereas, in a retail business, a CSF could be customer satisfaction ratings. This specificity ensures that strategic efforts are concentrated on the most critical areas.

Moreover, tools like Visual Strategic Planning Tools can significantly enhance the ability to visualize and manage these critical success factors, ensuring that they are not just defined but actively monitored and achieved.

Types of Critical Success Factors:

In his seminal work, Rockart outlined four distinct categories of Critical Success Factors (CSFs), each serving as a cornerstone in the foundation of organizational triumph

Industry Factors: These stem from the unique dynamics of your industry, dictating the essential actions required to maintain competitiveness. For instance, in the realm of technology startups, innovation emerges as a pivotal CSF, driving evolution and differentiation amidst fierce competition.

Environmental Factors: Arising from broader macro-environmental forces, these factors encompass elements such as the business climate, economic fluctuations, competitor landscapes, and technological advancements. Conducting a thorough PEST Analysis unveils the intricacies of these factors, empowering organizations to navigate uncertainties with foresight and adaptability.

Strategic Factors : Tailored to the specific competitive strategy adopted by your organization, these factors delineate the strategic choices guiding positioning and marketing endeavors. Whether pursuing a strategy of high-volume, low-cost production or opting for a niche, high-value approach, strategic CSFs illuminate the pathway to sustained relevance and profitability.

Temporal Factors: Reflecting the internal flux and evolution within your organization, temporal CSFs are transient in nature, responding to short-lived barriers, challenges, and opportunities. For instance, amidst rapid expansion, a critical imperative might revolve around scaling international sales operations, highlighting the dynamic interplay between internal growth trajectories and external market demands.

Critical Success Factors (CSFs) VS Key Performance Indicators (KPIs)

Understanding the distinction between Critical Success Factors (CSFs) and Key Performance Indicators (KPIs) is crucial for effective strategic planning. While both are essential metrics in business strategy, they serve different purposes and are used in different contexts.

Critical Success Factors are the essential areas of activity that must be performed well to achieve the strategic goals of an organization. These are the elements that are critical for success in achieving the strategic objectives. On the other hand, Key Performance Indicators are quantifiable measurements that reflect the critical success factors of an organization. They are used to gauge the performance and success of an initiative, often linked directly to strategic objectives.

For instance, if a critical success factor for a tech company is ‘innovation,’ a corresponding KPI might be the number of new patents filed per year or the percentage of revenue from new products.

Using KPIs to Measure CSFs

Effectively measuring CSFs through KPIs requires a clear understanding of the strategic goals and the critical factors that drive them. Here are some ways KPIs can be used to measure the effectiveness of CSFs:

• Alignment of KPIs with strategic goals to ensure they reflect the critical success factors.
• Regular review and adjustment of KPIs to adapt to changing circumstances and ensure they remain relevant to the CSFs.
• Utilization of tools like Balanced Scorecard Templates to visualize and track these indicators effectively.
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It’s important to avoid the pitfall of confusing CSFs with KPIs. While KPIs are indicators of performance, CSFs are the areas that determine whether the organization will achieve its strategic goals. Understanding this distinction helps organizations focus on what truly matters and allocate resources accordingly.

Building an Organizational Strategy Around Critical Success Factors

Integrating critical success factors (CSFs) into your business planning isn’t just about identifying what’s important; it’s about embedding these factors into the very fabric of your organizational strategy. This integration ensures that every decision and action aligns with your overarching goals, propelling your business towards success.

Integrating CSFs into Business Planning

Leadership plays a pivotal role in fostering a culture that prioritizes CSFs. It starts with a clear communication of what these factors are and how they tie into the daily operations and long-term objectives of the company. Here are some steps to effectively integrate CSFs into your business planning:

• Define and Align: Clearly define your CSFs and ensure they are in harmony with your organizational values and strategic goals. This alignment is crucial for maintaining focus and direction.
• Communicate: Use every opportunity to communicate the defined CSFs across all levels of the organization. This ensures everyone is on the same page and pulling in the same direction.
• Embed: Integrate CSFs into all planning documents and tools. Use frameworks like Impact Mapping Templates to visualize how individual actions and strategies connect back to these critical factors.
• Review: Regularly review and adjust CSFs to respond to changing market conditions or internal company shifts. This agility allows your business to remain relevant and competitive.

Identifying and Setting Critical Success Factors for Your Business

Identifying and setting the right critical success factors (CSFs) is pivotal for any business aiming to achieve its strategic goals. This process requires a structured approach and keen insight into both the market and internal capabilities. Here, we outline a five-step process to effectively pinpoint and refine CSFs that align with your business objectives.

• Step 1: Gather Stakeholder Input - Engage with key stakeholders from various departments to get a comprehensive view of the strategic needs and expectations. This collaborative approach ensures that the CSFs developed are inclusive and representative of the entire organization.
• Step 2: Conduct Market Analysis - Utilize resources like Scenario Planning Guide to understand market trends and competitor strategies. This analysis helps in setting CSFs that are not only relevant but also competitive.
• Step 3: Define CSFs - Based on the insights gathered, define clear and measurable CSFs. Ensure they are specific, achievable, and directly tied to strategic objectives.
• Step 4: Refine and Adjust - CSFs should not be static. Regularly review and refine them based on ongoing feedback and changing market conditions to keep them relevant and impactful.
• Step 5: Implement and Monitor - Implement the CSFs across the organization and monitor their progress. Use visual project management tools to track these factors and make adjustments as necessary.

By following these steps, businesses can ensure that their CSFs are not only defined but are also aligned with the overall strategic vision, thereby enhancing the likelihood of achieving desired outcomes. Remember, the key to successful strategic planning is not just identifying CSFs but continuously adapting them to fit the evolving business landscape.

Practical Tips for Creating Effective Critical Success Factors

Creating effective critical success factors (CSFs) is pivotal for any organization aiming to achieve its strategic objectives. Here are some practical tips to ensure your CSFs are clear, specific, and aligned with your business goals.

• Clarity and Specificity: Each CSF should be distinctly defined to avoid ambiguity. This clarity helps team members understand exactly what is expected and how it contributes to the organization’s success.
• Alignment with Strategic Objectives: CSFs should directly support the strategic goals of your organization. This alignment ensures that every effort contributes towards the overarching objectives.

Avoiding Common Pitfalls: One common mistake is setting too many CSFs, which can dilute focus and resources. Prioritize CSFs that have the most significant impact on your strategic goals.

Involving Cross-Functional Teams: CSFs should be developed with input from various departments to ensure they are comprehensive and inclusive. Engage teams through platforms that foster collaboration, such as Retrospective Meetings for Cross-Functional Teams to gather diverse insights and drive collective commitment.

Regular reviews and updates to CSFs are crucial. The business landscape is dynamic, and your CSFs should evolve to reflect changes in the market and internal business processes. Leveraging a centralized platform like Creately can facilitate the continuous monitoring and updating of CSFs, ensuring they remain relevant and impactful.

How Creately Supports Setting and Achieving Organizational Goals through CSFs

Setting and achieving organizational goals hinge significantly on identifying and leveraging critical success factors (CSFs). Creately, with its advanced visual collaboration platform, provides an array of tools designed to enhance strategic planning and execution. Here’s how Creately’s features align with the needs of organizations aiming to master their strategic objectives through effective use of CSFs.

Creately’s Tools for Strategic Planning

• Visual Canvas: Creately’s visual canvas offers a dynamic space for teams to brainstorm, map out strategies, and visualize the relationships between different CSFs. This is crucial for understanding how various factors interlink and influence overall strategic success.
• Multiple Visual Frameworks: With access to various frameworks such as Business Model Canvas Template and Strategic Planning Tools , teams can effectively define and align their organizational goals with the identified CSFs, ensuring that every action taken is strategically oriented.

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Chiraag George is a communication specialist here at Creately. He is a marketing junkie that is fascinated by how brands occupy consumer mind space. A lover of all things tech, he writes a lot about the intersection of technology, branding and culture at large.

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Original research

Success and limiting factors in health service innovation: a theory-generating mixed methods evaluation of uk projects, kathleen leedham-green.

1 Medical Education Research Unit, Faculty of Medicine, Imperial College London, London, UK

Alec Knight

2 School of Population Health and Environmental Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK

Gabriel B Reedy

3 Centre for Education, Faculty of Life Sciences and Medicine, King’s College London, London, UK

Associated Data

bmjopen-2020-047943supp001.pdf

Data are available upon reasonable request. Due to the highly individual nature of healthcare innovations and the limited geographic area of this study, we are unable to provide our raw data. We undertake to provide a redacted data set upon reasonable request.

To explore and explain success and limiting factors in UK health service innovation.

Mixed methods evaluation of a series of health service innovations involving a survey and interviews, with theory-generating analysis.

The research explored innovations supported by one of the UK’s Academic Health Science Networks which provides small grants, awards and structural support to health service innovators including clinical academics, health and social care professionals and third-sector organisations.

Participants

All recipients of funding or support 2014–2018 were invited to participate. We analysed survey responses relating to 56 innovation projects.

Responses were used to conceptualise success along two axes: value creation for the intended beneficiaries and expansion beyond its original pilot. An analysis of variance between categories of success indicated that participation, motivation and evaluation were critical to value generation; organisational, educational and administrative support were critical to expansion; and leadership and collaborative expertise were critical to both value creation and expansion. Additional limiting factors derived from qualitative responses included difficulties navigating the boundaries and intersections between organisations, professions, sectors and cultures; a lack of support for innovation beyond the start-up phase; a lack of protected time; and staff burn-out and turnover.

Conclusions

A nested hierarchy of innovation needs has been derived via an analysis of these factors, providing targeted suggestions to enhance the success of future innovations.

Strengths and limitations of this study

• A strength of this research is that it compares many innovations in a consistent way, and that it provides insights across a range of categories of success.
• A limitation of this research is that it is situated in a single geographical context; however, repeating these methods in different contexts should produce locally relevant results.
• Few mid-level theories relating to innovation are grounded in data that include projects that have not achieved their intended outcomes; therefore, we may have identified novel insights.
• Many of the success factors we have identified are not unique to this study; however, they have been subjected to further statistical analysis and found to differentiate significantly across categories of success in this context.
• More research is needed to examine whether addressing these factors prospectively enhances the success of future innovations.

Healthcare systems worldwide are faced with increasing demand linked to the rising burden of disease within a resource-constrained environment. 1 This has led to a pressing need to find and disseminate innovative ways of meeting the healthcare needs of patients and communities in ways that are more sustainable. 2 The WHO characterises health service innovation as ‘a novel set of behaviours, routines, and ways of working that are discontinuous with previous practice, are directed at improving health outcomes, administrative efficiency, cost-effectiveness, or users’ experience and that are implemented by planned and coordinated actions’ 3 (p 7).

Academic efforts in the health sciences continue to sharpen the focus on impact, rather than the creation of ‘new knowledge’ as the primary goal of research activity. At the vanguard are implementation scientists who work to translate research and innovation into clinical practice, navigating institutional, organisational, structural and cultural complexities to improve services. 4 New support structures have emerged, such as the 15 Academic Health Science Networks set up in 2013 by the National Health Service (NHS) England, with funding streams that aim to support and encourage innovation at various levels. 5 After more than half a decade of programme grants, the impact of these innovation programmes is a legitimate subject of enquiry: how and why have certain innovations become normalised, sustained or spread, and why have others struggled or stopped?

The knowledge created through an individual innovation is likely to be complex and context dependent, providing insights that may not necessarily be generalisable. 6 Meta-analyses are faced with the complexity of synthesising multiple project evaluations that may be reported in different ways. The published literature on health service innovation contains few analyses of unsuccessful innovations, despite attempts to encourage negative reporting. 7 By evaluating a large corpus of projects across one of these academic health sciences networks in a systematic way, we have an opportunity to directly compare innovations including those that may have struggled or stopped and not reached the literature.

This study thus sets out to explore a large number of innovations, both as individual projects in their unique local contexts, and as part of a larger integrative study. By isolating the factors that differentiate between categories of success, our aim is to produce an empirically derived explanatory model, and thereby to inform and enhance the success of future innovations.

Research aim

To explore and explain success and limiting factors in health service innovation.

Methodological orientation and theory

This study is situated at the intersection of policy, social sciences and organisational research. Our philosophical assumptions are that there are real differences in the success of innovations, but also that success is fundamentally a subjective construct. Any research will only produce an approximation of the truth, and findings must be interpreted with an appreciation for context. We therefore position this research at the boundary of critical realism and constructivism. 8

Adopting Varpio et al ’s terminology on the philosophy of research, we are taking an inductive approach that works towards a theoretical framework, rather than applying a pre-existing theoretical framework to this study. 9

We have adopted what Creswell et al refer to as a sequential mixed methods design. 10 According to Creswell, insight can emerge from exploring first through qualitative methods (in our case a published qualitative review and interviews) the types of factors that might be important, and then designing questionnaires to explore their salience to a population (called an ‘exploratory sequential design’). Insight can also emerge from collecting survey data initially and then following up with interviews to help explain the survey results in more detail: an ‘explanatory sequential design’. Where both qualitative and quantitative data are collected simultaneously, one set of data can be used to triangulate the other (eg, where the meaning of one is unclear), or they can be used in complementary ways to illuminate each other (eg, one determining which factors are important, the other illuminating why that might be). Our research process involves both exploratory and explanatory aspects as well as triangulation and illumination. It is summarised in figure 1 .

Research overview. A mixed methods sequential research process to explore and explain success factors and limiting factors in health service innovation.

The Health Innovation Network (HIN) is one of the nationally funded Academic Health Science Networks set up by NHS England in 2013. It provides small grants, awards and structural support to academics, health and social care professionals and third-sector organisations, supporting service-level innovations to improve outcomes and value, including the sustainable use of resources. In addition, in the years 2014–2017, Health Education England (South London) provided investment in training and education innovation projects across healthcare settings in South London, through its strategic investment programme.

Participants and sampling

All recipients of HIN funding and Health Education England (South London) strategic investment programme funding and support during the years 2014–2018 were invited to participate. As our sample size was moderate, we aimed to analyse all responses and retrospectively determine whether the sample size was sufficient for thematic saturation and statistical inference. We achieved a priori thematic saturation for success factors (exemplar comments for each significant factor that we found) and inductive thematic saturation for limiting factors (content coded until no new themes arose). 11

Research team and ethics

The research was commissioned by HIN in collaboration with Health Education England and conducted by an independent research team at King’s College London. The research team comprised a postdoctoral educational psychologist/learning scientist (GBR), a postdoctoral occupational psychologist/health services researcher (AK) and a medical education research fellow (KLG). None were in a position of power or influence over participants, and the research was carefully designed to be conducted at arm’s length from the funding agency. Survey responses were collected anonymously and decontextualised by the research team to encourage innovators to comment critically and safely about their projects. Innovation funding was not conditional on taking part in this research. Ethical approval was granted on 26 March 2019 by the Research Ethics Committee of King’s College London (LRS-18/19–10432). Written informed consent was obtained from interviewees. Consent was implied from participation in the survey.

Patient and public involvement

No patient was involved. The primary stakeholders in this research were health service innovators who were involved in the survey design and in checking back and refining our interpretation.

Data generation methods

Survey design.

The survey design began with the extraction of potential success factors for health service innovation from a recently published qualitative systematic review. 12 This review aimed to identify all the factors and theories associated with sustainability and scale-up (capacity building) of innovations in health services research. KLG validated and expanded these factors through scoping interviews with five experienced health service innovators. The interviews started with an open exploration of what the innovator felt had impacted on the success of their project, followed by discussion on the factors identified through the literature. Personal factors were mentioned by all stakeholders in addition to the factors from the review, suggesting these may be under-reported. An additional theme (personal factors) with related subfactors was therefore included, based on these interviews. Themes and factors are listed in figure 2 . These were used to create a mixed methods nested design survey 13 using Qualtrics software (full text in online supplemental data ).

Survey scope. The survey scope was based on a qualitative review of theories and findings relating to the sustainability and scale-up of health service innovations 12 supplemented by five scoping interviews. The questions are listed in table 1 , and the full-text survey is in the online supplemental material .

Supplementary data

The survey asked respondents to:

• Categorise and describe their project’s current status (no longer running/likely to finish soon/stable at the level of the original pilot/scaled up beyond the original pilot/too early to say/other).

Analysis of variance of potential success factors across categories of success

Analysis of variance of potential success factors across categories of success.

*Respondents on average ↑ =agree, ↗ =somewhat agree, → =neither agree nor disagree, ↘ =somewhat disagree, ↓ =disagree.

†Asymptotic significances are displayed. The significance level is 0.05. The darker the shading, the safer it is to reject the null hypothesis. Significance <0.05 indicates >95% certainty that the difference between categories is not random.

‡Secondary analysis examined the direction of the association and the strength of effect across categories of success.

• Describe the status of their project and provide qualitative insights into each of the nine themes.

Our five stakeholders helped to improve the clarity, acceptability and usability of the survey questions and instructions.

Survey distribution

A neutral administrator from Health Education England distributed the survey by email in August and September 2019 to all 176 named recipients of HIN and Health Education England (South London) funding awards, grants and bursaries. A reminder was distributed 4 weeks later to participants who had not responded. Projects that had received more than one award were sent a single survey, and participants who had run more than one project were sent a separate survey for each project.

Stakeholder follow-up interviews

KLG checked back our results and interpretation with five stakeholders identified by HIN as experienced innovators, one of whom was also involved in the original scoping interviews. Interviews lasted 30–45 min and transcription was facilitated by automated software (otter.ai). These stakeholders helped to refine the model and confirmed its applicability and utility in their context. No new themes arose; however, quotes were used to enrich our survey data.

Data analysis methods

Development of categories of success.

KLG and AK categorised projects into grades of success based on how the respondent self-categorised their project, triangulated against their qualitative survey responses. The categories of success were derived through an iterative process, involving both researchers agreeing a descriptive summary of the status of each project (eg, scaled down despite achieving better than expected patient outcomes; scaled down because the intervention did not achieve its aims). We grouped projects with similar project outcomes together, and through a process of constant comparison 14 constructed a categorisation framework that accounted for all the cases in the set.

Determination of salience of success factors

We adopted an exploratory approach to data analysis, which aims to generate rather than test theory. 15 KLG conducted an analysis of variance (ANOVA) for each of the scored factors (Kruskal-Wallis non-parametric ANOVA on rank using IBM SPSS V.25) to see whether there were significant differences between categories of success. The Kruskal-Wallis test does not assume a normal distribution in the data and can be used when the data are ordinal, for example, Likert scores. For asymmetric group sizes, the non-parametric Kruskal-Wallis test performs better than the parametric equivalent ANOVA method. 16

For each factor that was identified as being significantly different between categories of success, we conducted a secondary analysis (box plot for each category) to confirm the direction and consistency of the association. This is generated automatically by SPSS after a Kruskal-Wallis test. A graded ‘exposure-response’ relationship across all grades of success would be expected if a factor genuinely drives success. 17 Where a graded relationship was not present, this is discussed in table 1 .

Illumination of success factors

KLG and AK extracted quotes from the survey and interviews relating to each significant success factor to generate a rich description within each theme.

Inductive analysis of limiting factors

KLG coded the content of all qualitative data relating to challenges within projects that had not achieved their intended outcomes or that had scaled down or stopped (n=21) facilitated by NVivo V.12 software. GBR and KLG refined the codes and both authors worked together to inductively arrange the content into themes. 18

Development of final model

We mapped significant factors onto a 2×2 grid using a natural logarithmic scatter plot so that factors that were significant to one dimension of success were mapped to the right half of the grid, factors that were significant to a second dimension of success were mapped to the top half and factors that were significant to both were mapped to the top right quadrant. We grouped success factors into themes through a process of collaborative discussion, and we explored which themes predominated in each quadrant to generate our model which was checked back with stakeholders.

Descriptive summary

We received 63 responses, but seven were incomplete or duplicate so a total of 56 responses (31.8% of 176) were included in the analysis. Each response related to a different innovation project. Survey respondents self-identified within one or more of the following groups: the project leadership team (n=54); service delivery team (n=9); training team (n=9); administrative team (n=6); service lead (n=2); and patient/service user (n=1). Several respondents identified within multiple groups.

Projects were situated in secondary care (n=19); community care (n=14); academic sector (n=5); mental health sector (n=4); online (n=4); primary care (n=3); and the hospice sector (n=2), with the remainder working at the interfaces between services, or across sectors. Their scope ranged from national programmes at hundreds of sites, local programmes supporting tens of thousands of patients, to small intensive innovations working in new ways with a few dozen complex patients, and their duration ranged from 1 to 5 years. The innovation areas related to new ways of working in end-of-life care; disability enablement; support for complex or vulnerable patients; discharge support; pain management; patient safety innovations; recovery and rehabilitation; personalised care; chronic conditions; new models of integrated health and social care; health promotion; and novel simulation and workforce development strategies. Typical projects can be explored at the HIN website 19 ; however, for reasons of confidentiality, we cannot specify which were included in this study.

Categories of success

Our emergent framework categorised each project’s success across two dimensions: the first relating to whether the innovation was reported as generating more or less than its anticipated value for patients/carers (‘value creation axis’), the second according to whether the project became sustained or scaled up beyond the initial pilot, or whether it was scaled down or stopped (‘expansion axis’). Innovations that were within the scope and intentions of the original pilot were positioned centrally. We initially scored projects into five categorisations across the expansion axis, as some projects expanded locally and some nationally; however, there were not enough projects in each group and statistics became unreliable, so we made a pragmatic decision to adopt relative rather than absolute categories.

The resulting categorical framework is illustrated in figure 3 , with the number of innovations in each category shown in brackets.

Framework for categorisation of success within health service innovation Health service innovations were categorised across two dimensions of success through an inductive classification process. The horizontal axis relates to how successful innovations were in realising their intended value: ‘did it create more or less than its intended value for beneficiaries?’ The vertical axis relates to expansion: ‘was the innovation scaled up or scaled down from the original pilot?’ Numbers in brackets indicate how many innovations were found in each category.

Success factors

Our analysis compared the variance of success factors across innovations that had demonstrated lower than expected value (n=11), value as expected (n=25) and higher than expected value (n=20). Next, we compared variance across innovations that had diminished in scope or stopped (n=17), innovations that were running as expected (n=16) and innovations that had scaled up (n=23). Finally, we excluded low-value projects and analysed again across the expansion axis (n=10, n=12 and n=23, respectively), seeking to explore why innovations with proven value had not been scaled up.

Our analysis is presented in table 1 with significant results (p<0.05) shaded in green. At this level of significance, there is a 1 in 20 probability that a result is in fact random. We have used lighter shading to indicate factors that might potentially be significant, or which could be found not to be significant if the power of the study was increased. The final column gives our interpretation of the more significant findings (p<0.05) that takes into consideration our secondary analysis.

Many factors were similarly scored across all categories of success, for example, information technology (IT) infrastructure. This does not mean that these factors are not important, only that they were experienced similarly across all categories of success and are therefore unlikely to be the underlying cause of the relative success or failure of a project.

We have collated the significant factors together in table 2 with illuminative quotes, and we discuss both positive and negative findings within each of the nine survey themes below.

Factors that are significantly associated with innovation success with illustrative quotes

The nine themes and factors were derived from a qualitative review of the literature supplemented by stakeholder interviews. Significant factors were derived through a survey that explored salience of factors to outcomes. Survey respondents are indicated by R(n) and follow-up interview respondents by FI(n).

Project-related factors

Interestingly, the aims of the project did not appear to be critical to success. Both successful and unsuccessful innovations were similarly reported as being designed to address an important healthcare need that was concerning to the public. All funded projects were required to articulate a credible evidence base arguing that stated benefits could be achieved through the project plan.

Resourcing and expertise

All significant resource-related success factors were associated with the workforce. Non-critical factors included infrastructure (such as buildings, materials and supplies), which were reported as sufficient; IT, which was moderately good across all categories of success; and funding issues, which were also similar across all categories of success. However, having the right numbers of staff with the right skills appeared to be highly significant, both in terms of the project being able to realise its intended value, as well as for it to become scaled up beyond the original pilot. Staff with time and energy appeared critical to whether successful innovations became scaled up, as were administrative and educational support, including the availability of ongoing educational support (eg, orientation and training for new staff, or to build capacity). Expertise appeared to be critical across all categories of success, both in terms of the innovator feeling they had the right skills, experience or training; the project having access to staff with the right skills; and having external expert input where needed.

External factors

Alignment to societal needs appeared to correlate with whether a project was able to realise its intended value, but less so with its expansion. However, as the effect size did not grow consistently across categories of value creation, we cannot necessarily infer a causal relationship. 17 Qualitative comments indicated that projects that were able to align themselves to current political or societal agendas, such as mental health, were more successful. Conversely, those attempting to work in relatively less topical areas of practice described difficulty securing strategic funding, so we have tentatively included this factor in our model.

Organisational factors

Our analysis of organisational factors indicated that the ability of an innovation to integrate into existing organisational structures, programmes or policies may be critical to whether it scales up, and possibly also to its ability to create value (p=0.059). Successful projects described adapting where necessary to achieve a good fit within organisational priorities. For the most part, host organisations were described as having a positive learning culture and were ready and able to undertake innovative initiatives; however, even innovations with proven value were unable to survive if there was opposition within the host organisation.

Personal factors

Few respondents reported being released from other duties so that they could implement their initiative. However, most respondents said they benefited from a supportive peer culture. Respondents who were able to realise value were significantly more like to say they were internally motivated and found working on the project rewarding.

Project management

Most projects measured or assessed the outcomes and impacts of the project, though this appeared to be more common in successful projects (p=0.060). Projects with high value were able to demonstrate and share this success. Leadership appeared to be a highly significant success factor across all categories of success, with struggling or unsuccessful projects citing leadership failures.

The tasks of the project

Similar to theme 2, which explored the aims of the project, the tasks of the project did not appear to be significantly different across categories of success.

Collaborative and participatory practices

Valuing team members’ opinions was highly significant across all categories of success and was present in all projects that were scaled up (hence variance not calculable). Participatory approaches were significantly associated with the ability of an innovation to generate value. These participatory processes related to the staff delivering the innovation, the intended beneficiaries and the communities in which the innovations were situated.

Finally, one of the most significant differentiating factors across all categories of success was engagement with a collaborative network that helped to support and sustain the initiative.

Limiting factor analysis

In addition to the above success factors, which were quantitatively identified, the following limiting factors were identified through our qualitative analysis of failed or struggling projects. As our limiting factor analysis is qualitative and interpretive, we present our data in line with our analysis.

Boundaries between commercial, voluntary and public sectors

While UK healthcare is primarily publicly funded and provided by the NHS, social care is often commercially provided, 20 creating the potential for friction at the interfaces between these sectors.

As the care homes are private businesses, there was some lack of political will to embrace the training, as there was a view that although there was the potential to improve health outcomes for the residents, the manager did not feel there were sufficient resources to implement the required training. (R4)

Commercial organisations were reported as unwilling to release staff for training unless the value of that training was felt within the organisation. Valuable initiatives by the voluntary sector to train social care staff, but which provided benefits in the healthcare sector, fell between sectors and were potentially unviable without direct funding.

The voluntary sector is happy to participate but there is no spare capacity within it unless there is a financial package that can go with it. (R35)

There was concern that privately funded organisations were not subject to the same standards and mandates as the publicly funded bodies, and were failing to invest in training.

Because it is not mandated, organisations do not have to engage with or release staff for education. (R2)

Restructuring within the NHS has created a set of semiautonomous institutions and organisations with different and sometimes competing priorities. 21 Some participants described difficulty aligning project aims to multiple organisational goals.

There were tensions between the two boroughs in relation to approach & resourcing. There was also a tension between commissioner expectations and practice/federation expectations which have impacted on the programmes sustainability. (R16) So, this intervention has a good return on investment, for every £1 you spend you get a return of £5.20. And they’ll say, I’m the one making the investment, but he’s the one making the return here. I’ve got a budget; he’s got another budget. We might both be in the health system, but I’m not going to spend my money if he’s the one getting the return. (FI2)

Workplace cultures and priorities

Some projects reported finding non-healthcare workers receptive to health-related training; however, some failed or struggling projects found this a challenge.

Medicines delivery teams unwilling to take on additional role. (R61) There were concerns raised by care home managers that the initiative would cause undue responsibility on individuals to make clinical decisions. (R16)

Participants described differences between academic and workplace learning cultures, and variable receptiveness of front-line clinical staff to change. Some described resistance to outsiders telling healthcare workers how to improve. This may reflect the inverse of high-value projects, which were found to engage in participatory practices, engaging patients, front-line staff and communities in codesigning their innovation.

It has been difficult to embed these products due to structural issues within the staff teams. (Nursing) It was clearly not seen as a priority. (R20) I think the main insight I would have is that when working with mental health nursing teams the researcher and research team needs to be fully integrated into team(s) and seen as part of the culture. Being an outsider does not seem to work as day-to-day practice seems to regulate research. (R20)

Participants also described tensions between management priorities and the priorities of those working directly with patients.

No interest on part of management. I don’t think they have even read it. (R57) The initiative was welcomed at service level, however there was little interest at senior management level. (R52) There is such a dislocation between commissioning and what is happening on the ground. (FI2)

Lack of support beyond the start-up phase

Participants noted ongoing privileging of new innovation over sustaining or scaling up innovations that have already demonstrated their value. For example, clinical academics do not gain publications for ongoing maintenance of innovative practice:

‘Research remit probably wouldn’t cover [further dissemination] unless there was a good likelihood of REFability’ [‘REF’ refers to the Research Excellence Framework, a scoring system used to fund the university sector]. (R1)

Participants described innovation funding streams, but articulated difficulty securing funding beyond the start-up phase.

The project was resourced sufficiently for the pilot. However, once the pilot finished so did the project. (R5) The education faculty and funding is driven towards innovation and not sustainability—this de-incentives individuals from continuing with existing projects. (R8)

Burn-out, turnover and lack of protected time

Participants described projects that were limited by staff burn-out, turnover and a lack of protected time.

My commitment to the project was there however the resources I had to continue with project were limited due to competing pressures on my time. (R12) The programme required more administrative support than anticipated & this ended up being an ask over & above someone’s day job for a prolonged period of time. (R16) The most important person was our pharmacist who moved from the pharmacy a few months after we started! (R61)

Risk as integral to innovation

Finally, it is worth noting that participants felt that risk was a necessary ingredient of healthcare innovation. Innovations that fail to demonstrate value should be supported in folding without hesitation, and lessons shared.

The project demonstrated that this initiative was not a model that would work in the hospital environment hence could not be embedded. (R30)

Final framework

We created two 2×2 matrices containing all the significant success factors across each dimension of success, shown in figure 4 . The matrix on the right excluded low-value projects in the calculation of factors significant to expansion and served to support the inclusion of some marginal factors in the final model as they became more significant despite lower power.

Critical success factors plotted according to their salience to success. Critical success factors plotted according to their significance to success on a natural logarithmic scale so that factors above and to the right are probably significant (p≤0.05). The expansion axis indicates significance to whether a project is scaled up or down beyond the original pilot. The value creation axis indicates significance as to whether it creates more or less than its intended value for beneficiaries.

Figure 4 shows clearly congruent clusters of factors in each quadrant, indicating that some types of factors may be more important to expansion, while others are more important to value creation. These clusters relate to skills and expertise, leadership and motivation, organisational fit and structural support, societal alignment and participation, and evaluation.

As outlined in table 1 , there are questions as to whether evaluation and motivation are dependent rather than independent variables: does finding working on a project personally rewarding drive success or vice versa, and does a positive evaluation drive success or vice versa? Triangulation with qualitative comments (in table 2 ) suggests that evaluation and motivation may drive success, so they have been tentatively included in our final model.

Themes that were predominantly related to value creation (participation, motivation and evaluation) were labelled value creation factors. Themes that were predominantly significant to expansion (organisational fit and structural support) were labelled expansion factors. Themes that were significant to both axes (expertise, leadership and a supportive network) were labelled core success factors. We arranged success factors into a nested hierarchy, as innovations that do not generate value are unlikely to be scaled up. Our final model in figure 5 also lists potential limiting factors identified through our inductive qualitative analysis.

Nested hierarchy of success factors and limiting factors for health service innovation. Factors that may be significant to both value generation for the intended beneficiaries and to whether the innovation is scaled up beyond the original pilot are labelled as core needs. Factors that may be significant specifically to value generation are the next priority (middle layer) as innovations that do not generate value will not become embedded or spread. Finally, factors that may primarily be significant to expansion are presented in the outermost layer. Additional limiting factors were identified through an inductive analysis of qualitative responses from projects that had scaled down or failed to produce their intended value.

This analysis of 56 health service innovation projects has enabled us to propose a model for understanding success in health service innovation that has two discrete axes: one relating to whether or not the innovation created value for its intended beneficiaries; the other relating to whether or not it was scaled up beyond the original pilot. Comparing projects across these dimensions of success has enabled us to hypothesise that:

• The core drivers of success are leadership and collaborative expertise (leadership skills and commitment, expert input, sufficient staff with the right skills and expertise, and a supportive collaborative network).
• The drivers of value creation for the intended beneficiaries are participation, motivation and evaluation (involvement of patients, public, practitioners and communities, alignment to societal needs, internal motivation, finding the project work rewarding, ability to demonstrate benefits and having opportunities to share impacts).
• The drivers of sustainability and scale-up are organisation fit and structural support (organisational fit and alignment, administrative and educational support, staff with time and energy).

Additional limiting factors included difficulties at the boundaries and intersections between organisations, professions, sectors and cultures; a lack of structural support beyond the start-up phase; and staff burn-out and turnover.

Within healthcare services, the issue of diffusion and sustainability of innovation has received widespread academic attention pioneered by Greenhalgh et al 22 who drew on Rogers’ seminal text on diffusion of innovations. 23 There have been many subsequent notable academic contributions. 24–28 Nilsen proposed an overarching framework of healthcare implementation theories according to the aim of the theory. 29 Theories such as those about innovation sustainability, which include the diffusion of innovation theory, were categorised as ‘determinant frameworks’, as they posit general types of factors that can influence the success of an innovation. We believe that our findings contribute through empirical evidence to theoretical development at this level, and thus may have wider implications than programme-level data would normally allow. According to Nilsen, such theories have typically been analysed and formulated across individual studies, at the level of meta-analysis or review 29 and may therefore be one or more steps removed from the underlying data. This study is different in that we have developed mid-range theory that is empirically grounded in programme-level data, and there is a clear line between our data and the generated theory.

Conceptions of innovation success tend to focus on sustainability 30 and scale-up. 24 We suggest that both are contingent on the ability of an innovation to provide value to its intended beneficiaries in the first place. There are few theories grounded in empirical data that explain this dimension of success. Our findings highlight the importance of patient, public and practitioner involvement, alongside the core success factors of leadership and collaborative expertise. We suggest that these are fundamental preceding factors to either sustainability or scale-up.

This ‘value for intended beneficiaries’ dimension of success also allows us to conceptualise a valuable innovation that is not growing or expanding. This, we argue, is important: healthcare innovations may have parameters within which growth and expansion are constrained, perhaps because their aims have been achieved, or because the context changes. An innovation that has met its aims but has not expanded beyond its natural boundary should be properly positioned as such.

Our ability to research a set of potentially valuable projects that were scaled down or stopped, many of which never reach the literature, may have afforded novel insights. Fixsen et al suggest that sustainability can only be asserted when the funding to support implementation is withdrawn. 31 Wiltsey Stirman et al ’s systematic review suggests sustainability can be asserted after a period of 2 years. 32 Our findings suggest that continued structural support, particularly organisational, administrative and educational support, may be critical to a project’s sustainability and scalability, and that their withdrawal may destroy potentially valuable innovations.

Finally, our findings further validate the work of Dopson et al , 33 whose qualitative exploration of a similar set of health service innovations highlighted the importance of context and process over content: it is not so much what you are trying to achieve, it is how you do it and the organisational and interpersonal contexts that you work within that matter.

A limitation of this research is its highly contextual nature. Our results may not be generalisable to all contexts; however, repeating these methods may produce locally relevant results. The ANOVA depends on the universe of potential factors having been correctly identified and a large enough number of innovations to produce statistical significance. The research could be improved by more extensive validation of factors, patient and public involvement, further testing the directionality of tentative factors, a greater geographical spread and a greater number of projects to allow for finer grading across the expansion axis.

Our findings suggest that organisations and policy makers wishing to support service-level innovation in similar healthcare contexts address the factors identified through this research as critical to success.

Such strategies might include:

• Supporting innovators with the right skills and expertise, including leadership skills, implementation support and evaluation expertise.
• Innovation networks to provide opportunities to showcase success and provide a peer community of expertise and support.
• Emphasising participatory practices and collaborative approaches, so that innovations are more likely to align to societal and organisation goals and generate value for patients, communities and practitioners.
• Providing administrative and educational support during the scale-up phase, and ensuring that this support is maintained or handed over rather than withdrawn to schedule.
• Recognising and enhancing the internal motivation and drive of innovators as well as more goal-oriented motivations such as career needs.

At a structural level, the boundaries between organisations, professions and the health and social care sectors may need to be addressed as potential barriers to successful innovation.

More research is needed to confirm whether addressing these factors prospectively enhances the success of future innovations.

Supplementary Material

Acknowledgments.

We would like to thank Josh Brewster from the Health Innovation Network and Sian Kitchen from Health Education England for their commitment to this project, and Professor Sue Smith from the Medical Education Research Unit of Imperial College for ongoing support and advice.

Twitter: @doctorkayleigh

Contributors: KL-G and GBR contributed to the conception and design of the work and to the acquisition of data. KL-G, GBR and AK collaborated on the data analysis and interpretation. KL-G and AK drafted the work, all authors revised it critically for important intellectual content. All authors have approved the final version and agree to be accountable for all aspects of the work and to resolve questions relating to accuracy or integrity.

Funding: This work was funded by Health Education England (grant number XXMLIVESEY).

Disclaimer: Gabriel Reedy is affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response at King’s College London in partnership with Public Health England (PHE), in collaboration with the University of East Anglia and Newcastle University. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England. These funders had no input in the writing of and the decision to submit this article.

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

Ethics statements, patient consent for publication.

Not required.

Ethics approval

Ethical approval was granted on 26 March 2019 by the Research Ethics Committee of King’s College London (LRS-18/19-10432).

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The Critical Success Factor Method: Establishing a Foundation for Enterprise Security Management

July 1, 2004 • technical report, by richard a. caralli , james f. stevens , bradford j. willke , and william r. wilson, cmu/sei report number, doi (digital object identifier), topic or tag.

Every organization has a mission that describes why it exists (its purpose) and where it intends to go (its direction). The mission reflects the organization's unique values and vision. Achieving the mission takes the participation and skill of the entire organization. The goals and objectives of every staff member must be aimed toward the mission. However, achieving goals and objectives is not enough. The organization must perform well in key areas on a consistent basis to achieve the mission. These key areas—unique to the organization and the industry in which it competes—can be defined as the organization's critical success factors. 

The critical success factor method is a means for identifying these important elements of success. It was originally developed to align information technology planning with the strategic direction of an organization. However, in research and fieldwork undertaken by members of the Survivable Enterprise Management (SEM) team at the Software Engineering Institute, it has shown promise in helping organizations guide, direct, and prioritize their activities for developing security strategies and managing security across their enterprises. This report describes the critical success factor method and presents the SEM team's theories and experience in applying it to enterprise security management.

Cite This Technical Report

Caralli, R., Stevens, J., Willke, B., & Wilson, W. (2004, July 1). The Critical Success Factor Method: Establishing a Foundation for Enterprise Security Management. (Technical Report CMU/SEI-2004-TR-010). Retrieved September 1, 2024, from https://doi.org/10.1184/R1/6585107.v1.

@techreport{caralli_2004, author={Caralli, Richard and Stevens, James and Willke, Bradford and Wilson, William}, title={The Critical Success Factor Method: Establishing a Foundation for Enterprise Security Management}, month={Jul}, year={2004}, number={CMU/SEI-2004-TR-010}, howpublished={Carnegie Mellon University, Software Engineering Institute's Digital Library}, url={https://doi.org/10.1184/R1/6585107.v1}, note={Accessed: 2024-Sep-1} }

Caralli, Richard, James Stevens, Bradford Willke, and William Wilson. "The Critical Success Factor Method: Establishing a Foundation for Enterprise Security Management." (CMU/SEI-2004-TR-010). Carnegie Mellon University, Software Engineering Institute's Digital Library . Software Engineering Institute, July 1, 2004. https://doi.org/10.1184/R1/6585107.v1.

R. Caralli, J. Stevens, B. Willke, and W. Wilson, "The Critical Success Factor Method: Establishing a Foundation for Enterprise Security Management," Carnegie Mellon University, Software Engineering Institute's Digital Library . Software Engineering Institute, Technical Report CMU/SEI-2004-TR-010, 1-Jul-2004 [Online]. Available: https://doi.org/10.1184/R1/6585107.v1. [Accessed: 1-Sep-2024].

Caralli, Richard, James Stevens, Bradford Willke, and William Wilson. "The Critical Success Factor Method: Establishing a Foundation for Enterprise Security Management." (Technical Report CMU/SEI-2004-TR-010). Carnegie Mellon University, Software Engineering Institute's Digital Library , Software Engineering Institute, 1 Jul. 2004. https://doi.org/10.1184/R1/6585107.v1. Accessed 1 Sep. 2024.

Caralli, Richard; Stevens, James; Willke, Bradford; & Wilson, William. The Critical Success Factor Method: Establishing a Foundation for Enterprise Security Management . CMU/SEI-2004-TR-010. Software Engineering Institute. 2004. https://doi.org/10.1184/R1/6585107.v1

• Home > B2B Blog > 6 Critical Success Factors of a Qualitative Research Project

6 Critical Success Factors of a Qualitative Research Project

Over the last couple of years qualitative research has made a comeback and piqued the interest of many of our clients in the b2b sector. In taking the lead for qual research here at B2B International HQ, I’ve compiled a list of the 6 critical factors for delivering a successful qualitative research project.

Using the Right Methodology

The crux of any research project is using the correct methodology but within qualitative research it can be even more important. In order to choose the right methodology you first need to understand your research objectives. Once you know what you want to find out, you need to ask yourself one question:

“Does this topic require group discussion or individual in-depth understanding?”

In answering this question you then have a basis for whether your methodology is based around focus groups or in-depth interviews (I’m including ethnography in this for simplicity!).

As an example, in-depth interviews (IDIs) will be more appropriate to gather an intricate understanding of a decision making unit whereas focus groups will be better for discussing a new creative concept.

Talking To the Right Person

With a much smaller sample size with a need to gather greater depth, it’s imperative that the respondent is knowledgeable about the subject and, in b2b markets, that they have significant influence within their company on the subject.

Compiling your list is going to play a huge part in this and in some instances this may need some troubleshooting; particularly if you aren’t 100% sure on who you need to be talking to.

Briefing Interviewers & Moderators

When briefing your interviewers and/or moderators (where necessary) it’s important to remember to get across the main research objective as THE most critical piece of information, rather than focusing on the small details.

A good interviewer or moderator will be able to bring out the key themes from knowing the high-level project objectives.

Note Taking or Transcripts

This one is an area of personal preference but whatever your methodology you will need at least one of them!

The important thing to note here is that when taking notes at a focus group or IDI you must write verbatim (as much as possible) without analysing what’s being said. It should be left for you or the person analysing the notes afterwards to make that judgement.

Reading Transcripts

Transcripts from IDIs should be read as single entities first to get a ‘feel’ for each interview i.e. things that aren’t actually written down. (This is also something you can ask the interviewer to do after each interview).

It is good practice not to leave all this to the end of fieldwork either! You will have a lot of data to read through so it should be done as you go along.

Qualitative Analysis

This type of data should be treated very differently to quantitative data, in most cases you aren’t looking for percentages but instead want themes.

Digging into your data, you should look for themes which can be linked to categories and eventually frameworks. There are also a number of different techniques we can utilise during analysis including Grounded Theory, Interpretive Phenomenological Analysis and Discourse Analysis. (Look out for my next blogs which will go into these techniques in detail!)

Learn More About Qualitative Research >

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Introduction, materials and methods, data availability, supplementary data, acknowledgements, identification of transcription factor co-binding patterns with non-negative matrix factorization.

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Ieva Rauluseviciute, Timothée Launay, Guido Barzaghi, Sarvesh Nikumbh, Boris Lenhard, Arnaud Regis Krebs, Jaime A Castro-Mondragon, Anthony Mathelier, Identification of transcription factor co-binding patterns with non-negative matrix factorization, Nucleic Acids Research , 2024;, gkae743, https://doi.org/10.1093/nar/gkae743

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Transcription factor (TF) binding to DNA is critical to transcription regulation. Although the binding properties of numerous individual TFs are well-documented, a more detailed comprehension of how TFs interact cooperatively with DNA is required. We present COBIND, a novel method based on non-negative matrix factorization (NMF) to identify TF co-binding patterns automatically. COBIND applies NMF to one-hot encoded regions flanking known TF binding sites (TFBSs) to pinpoint enriched DNA patterns at fixed distances. We applied COBIND to 5699 TFBS datasets from UniBind for 401 TFs in seven species. The method uncovered already established co-binding patterns and new co-binding configurations not yet reported in the literature and inferred through motif similarity and protein-protein interaction knowledge. Our extensive analyses across species revealed that 67% of the TFs shared a co-binding motif with other TFs from the same structural family. The co-binding patterns captured by COBIND are likely functionally relevant as they harbor higher evolutionarily conservation than isolated TFBSs. Open chromatin data from matching human cell lines further supported the co-binding predictions. Finally, we used single-molecule footprinting data from mouse embryonic stem cells to confirm that the COBIND-predicted co-binding events associated with some TFs likely occurred on the same DNA molecules.

The interactions between DNA and transcription factors (TFs) are crucial to transcription regulation as they intrinsically determine cell growth, development, and response to stimuli. TFs bind DNA at TF binding sites (TFBSs) in a sequence-specific manner through direct contact between DNA nucleotides and amino acids of the TFs’ DNA-binding domain (DBD) ( 1 ). Structural similarities between DBDs classify TFs into structural classes and families, and TFs from the same class or family usually recognize similar DNA patterns (or motifs).

The binding properties of individual TFs have been vastly studied ( 2–4 ), and several databases store DNA binding profiles for TFs across multiple taxonomic groups (e.g. JASPAR, CIS-BP and HOCOMOCO ( 5–7 )). While these databases primarily provide TF binding motifs for individual TFs, there is a need to increase our understanding of how TFs cooperatively bind DNA to regulate transcription ( 8 ). The cooperative binding of TFs generates many possible binding combinations, thus increasing the complexity of gene regulatory networks ( 8 , 9 ). Recent studies argue for a flexible grammar of motifs at cis- regulatory regions, where the spacing and orientation between TFBSs would not be a key determinant for transcription regulation ( 10–13 ). Nevertheless, some TFs physically cooperate, providing a strict motif syntax important for transcription regulation. For instance, exhaustively testing the combinatorics of liver-associated TFBSs with massively parallel reporter assays revealed that TFBS orientation and order are major drivers of gene regulatory activity ( 14 ). A well-characterized example of physical interaction between TFs is POU5F1 (OCT4) cooperation with either SOX2 or SOX17 in pluripotent cells. The spacing between the binding sites will determine POU5F1’s partner and the corresponding regulatory effect ( 15 , 16 ). Hence, the specific spacing and orientation between the canonical motifs recognized by two TFs can characterize their co-binding at given genomic regions ( 17 , 18 ). However, the cooperative binding of two TFs can also slightly modify their individual DNA sequence preference ( 19 ). Consequently, systematically identifying cooperative events genome-wide with strict binding grammar is still challenging.

The CAP-SELEX experimental technique captures co-binding events between predefined sets of TFs in vitro ( 19 ). However, whether the same co-binding properties will occur in vivo remains to be determined. Therefore, computational methods, such as TF-COMB, TACO, SpaMo and MCOT ( 20–23 ), leverage in vivo data, such as ChIP-seq ( 24 ), to predict co-binding events. These tools rely on identifying the co-occurrence of pre-defined genomic regions bound by the individual TFs or already known individual DNA binding motifs in predefined genomic regions. While these strategies reduce the search space, they restrict discoveries for the pairs of TFs where either bound regions or TF binding motifs exist for both TFs. When relying on already-known TF binding motifs, the quality of the available motif collections is a limiting factor, and this approach cannot discover new DNA sequence patterns. Another approach implemented in the RSAT dyad-analysis tool does not rely on pre-defined motifs and predicts spaced pairs of motifs de novo starting from spaced 3-mer patterns ( 25 , 26 ). Finally, deep learning approaches can infer regulatory patterns from experimental data, with the capacity to pinpoint TF co-binding events ( 12 , 27 ). Even though the underlying algorithms are advanced and powerful, their complexity and interpretability make it challenging to characterize specific co-binding events without extensive a posteriori analyses.

In this study, we aimed to discover TF co-binding patterns in the vicinity of high-quality TFBSs (referred as anchors). The discovery of fixed co-binding patterns can be considered a matrix decomposition (or factorization) problem where an input matrix encodes nucleotides surrounding TFBSs. The ultimate goal is to group nucleotide patterns from this matrix. The non-negative matrix factorization (NMF) technique decomposes a given non-negative matrix into two low-rank, non-negative matrices to reveal underlying patterns and structures within the data ( 28 , 29 ). This technique has been useful in computational biology to reveal molecular patterns from high-throughput data ( 28 , 30 ). seqArchR is the first novel application of NMF for the simultaneous identification of sequence features and corresponding clusters ( 31 ). The seqArchR tool identifies critical DNA elements in promoter regions by applying NMF to the corresponding one-hot encoded sequences ( 31 ). This approach inspired us to use NMF to address the discovery of TF co-binding patterns.

We present COBIND, a Snakemake-based ( 32 ) pipeline for the de novo discovery of TF co-binding patterns from input sets of TFBSs ( https://bitbucket.org/CBGR/cobind_tool ). We applied COBIND to 5699 sets of high-quality TFBSs from seven species ( Arabidopsis thaliana , Caenorhabditis elegans , Danio rerio , Drosophila melanogaster , Homo sapiens , Mus musculus and Rattus norvegicus ) for 401 unique TFs stored in the UniBind database ( 33 ). COBIND recovered established and unreported co-binding events between TFs. Among TFs from the same structural family, the majority shared co-binding patterns. In human and mouse genomes, genomic regions harboring the predicted co-binding events are evolutionarily more conserved than genomic regions without co-binding. Moreover, increased chromatin accessibility in matching human cell lines supported the predicted co-binding events from bulk experimental data. Finally, using single-molecule footprinting data from mouse embryonic stem cells, we confirmed that the anchor and co-binding patterns associated with some TFs are significantly enriched for TF co-occupancy at the single-molecule level. Overall, COBIND can de novo discover regions in the genomes that harbor patterns of co-binding events between TFs.

Predicting co-binding patterns with COBIND

COBIND takes a BED (or FASTA) formatted file providing the genomic coordinates of anchor regions (or DNA sequences centered at the anchor sites), such as TFBS locations, as input. The tool uses NMF to reveal recurring DNA motifs with space constraints in the regions surrounding the input anchors, which are not factorized. The underlying computational pipeline consists of the following steps (Figure 1 ):

Step 1: Extraction of flanking regions . COBIND extracts the DNA sequences surrounding the anchor TFBSs ( n bp upstream and downstream; n = 30 by default) using bedtools (v2.29.2) ( 34 ).

Step 2: One-hot encoding . The flanking sequences are one-hot encoded as vectors of 4 bits per DNA nucleotide (A: 1000, C: 0100, G: 0010, T: 0001; ambiguous nucleotides |$ \notin [ {A,\ C,\ G,\ T} ]$| are encoded as 1111 (Figure 1 Step 2)). COBIND constructs two matrices representing the upstream and downstream sequences by combining the vectors of one-hot encoded sequences. Each row corresponds to a sequence flanking an anchor TFBS from the input set.

Step 3: Non-negative matrix factorization . COBIND applies NMF to each matrix using the NMF function of scikit-learn (v0.23.2) ( 35 ). The NMF decomposes an input matrix into two matrices: one representing k components (or factors) of nucleotide patterns and one informing the presence of the identified patterns in each row of the input matrix ( 28 , 30 ). Importantly, COBIND applies NMF with multiple values of k to increase its capacity to capture co-binding patterns with different resolutions (see section ‘Parameter settings’ below) (Figure 1 Step 3). Next, COBIND assigns input sequences to each component following the methodology described by Kim and Park ( 36 ). Finally, COBIND builds motifs as positional frequency matrices (PFM) for each component by computing the occurrence frequencies of each nucleotide at each position (Additional file 1: Supplementary Figure S1 ).

Step 4: Motifs filtering . COBIND filters out PFMs with information content (IC) < 2 to focus on informative patterns. Next, it aims to identify motifs corresponding to positions with local enrichment of high IC to distinguish them from motifs with scattered high IC positions or equal distribution of IC along the flanking region (Additional file 1: Supplementary Figure S1 ). Specifically, COBIND computes the Gini coefficient g of each PFM to measure the inequality of IC values ( 37 ). We discard PFMs with a low Gini coefficient ( g < 0.5 by default).

Step 5: Motif trimming . COBIND extracts the positions corresponding to the local enrichment of high IC values, revealing co-binding motifs. Specifically, it first computes the smallest window of size n that contains at least half of the IC of the complete flank. Finally, COBIND doubles the size of the window (adding n /2 nucleotides up and downstream) to define the co-binding motif.

Step 6: Motif clustering . COBIND can identify similar motifs multiple times since (i) NMF runs with different values for k , (ii) similar motifs can have different spacings with the core motif, and (iii) multiple TFBS sets can be processed in parallel, resulting in the identification of similar motifs (Figure 1 Step 3). COBIND provides non-redundant motifs by clustering the identified motifs (Figure 1 Step 6). Specifically, we integrate the RSAT matrix-clustering tool ( 38 ) to cluster similar motifs (we provide the used parameters in Additional file 1: Supplementary Table S1 ). For each motif cluster, COBIND constructs an archetypal motif following the methodology from ( 39 ), which summarizes motifs by computing the means of the counts from the aligned matrices in each cluster. We discarded successive flanking positions with IC < 0.1 from the archetypal motif.

Similarly, we cluster and summarize the anchor TFBS sequences associated with each co-binding motif (Additional file 1: Supplementary Table S1 ).

Schematic workflow of COBIND. 1) COBIND takes one or multiple input file(s) that provide regions of interest, such as TFBSs. The tool extracts upstream and downstream DNA sequences (default: 30 nt each). 2) The sequences are one-hot encoded to construct encoded matrices of upstream and downstream sequences. 3) COBIND applies NMF on each matrix to extract DNA patterns. 4) The tool computes the Gini coefficient of each DNA pattern and discards the uninformative patterns. 5) COBIND trims the identified flanking patterns to reveal possible TF co-binding motifs. 6) RSAT matrix-clustering groups redundant motifs, and COBIND constructs archetypal motifs summarizing each cluster of similar motifs. 7) The predicted co-binding patterns are visualized through a heatmap representing the predicted spacing between the anchor (at the center) and the co-binding patterns with the corresponding orientation. The predicted co-binding patterns are provided on the left and/or right of the plots, depending on whether they were found upstream and/or downstream of the anchor sites. The position of the heatmap tiles provides, for each pattern, the number of nucleotides between the co-binding and anchor sites. The color of a heatmap tile represents the number of input datasets in which the co-binding pattern was predicted. The dot size and number within the heatmap tileblocks correspond to the proportion of input sequences in which a co-binding instance with a specific spacing was predicted.

Step 7: Summarize the discovered co-binding events . COBIND computes the relative spacing and orientation to the anchor motif from each co-binding archetypal motif discovered. Finally, it illustrates the corresponding co-binding events as heatmaps (Figure 1 Step 7). The spacing summary plot describes the relationship between the anchor (provided at the center of the plot) and the discovered co-binding patterns (upstream or downstream from the anchor). On the x-axis, one visualizes the number of nucleotides between the anchor and the discovered co-binding pattern motifs, illustrated as sequence logos. The color intensity in the heatmap illustrates the proportion of the input datasets with predicted co-binding motif instances. The inserted number indicates the proportion of unique sequences in the datasets with the corresponding co-binding pattern, orientation, and spacing, that are predicted to harbor the corresponding co-binding configurations.

Parameter settings

The hyperparameters of COBIND are the length of the flanking regions, the number of components for the different runs of the NMF, and the Gini coefficient threshold. Below, we describe the values used in this study for each parameter and why we selected these values.

Length of the flanking sequences . We considered 30 bp upstream and downstream of the input TFBSs to report co-binding events likely associated with physical interactions between the TFs. The longest TFBS motif used in UniBind is about 20 bp-long ( 33 ), and cooperative TFs bind TFBSs 9–10 bp away from each other on average ( 19 ). Furthermore, larger flanking regions resulted in noisier co-binding motifs and could miss co-binding motif configurations (Additional file 1: Supplementary Figure S2 ).

Number of components . COBIND runs NMF several times with different numbers of components ( k ). We used synthetic data to estimate a suitable range of values for k . Specifically, we implanted different proportions of instances of predefined motifs in random sequences. We considered sets of 800, 1000, 4000, 8000, 10 000, 40 000 and 80 000 synthetic sequences. For each set of synthetic sequences, we injected an instance of the considered motif in 0.5, 1, 3, 5 and 10% of the sequences. We ran COBIND with k ∈ [2, 17] for each synthetic dataset. For each k value, we evaluated specificity and sensitivity by computing the F1 score and Matthew's correlation coefficient (MCC) for each discovered motif. Moreover, we reported the proportion of correctly predicted motifs. We considered that COBIND predicted the correct motif if it was similar to the injected motif—as assessed by Tomtom ( 40 ) with a P -value <0.05 (Additional file 1: Supplementary Table S1 ). We used the results from the synthetic data to select k ∈ [3, 6] for further analyses (Additional file 1: Supplementary Figures S3 – S15 ).

Gini coefficient threshold . To empirically determine the Gini coefficient threshold, we compared the distributions of Gini coefficients for the motifs predicted by COBIND on the TFBS datasets from UniBind (see below) with those of predictions from random sequences. For each UniBind set of TFBSs, we constructed a set of synthetic sequences by shuffling the DNA sequences flanking the TFBSs with the kmer shuffling module of the BiasAway tool with k = 1 ( 41 ). For each species considered, we selected the Gini coefficient threshold corresponding to a 1% false discovery rate (Additional file 1: Supplementary Figure S16 ). This strategy resulted in the following Gini coefficient thresholds: 0.50 for Arabidopsis thaliana , 0.46 for Caenorhabditis elegans , 0.48 for Danio rerio , 0.49 for Drosophila melanogaster , 0.50 for Homo sapiens , 0.54 for Mus musculus and 0.53 for Rattus norvegicus . Finally, we estimated the stability of the thresholds obtained with this strategy by performing the same analysis multiple times and subsampling the number of datasets from H. sapiens and M. musculus . The Gini coefficient thresholds were stable across the different runs (Additional file 1: Supplementary Figure S17 ).

Transcription factor binding site datasets

We used the robust collection of TFBS sets from the UniBind database (2021 version) ( https://unibind.uio.no/ ) derived from 7 species ( Arabidopsis thaliana , Caenorhabditis elegans , Danio rerio , Drosophila melanogaster , Homo sapiens , Mus musculus, Rattus norvegicus ) ( 33 ). UniBind TFBS datasets derive from pairs of ChIP-seq peak datasets and JASPAR TF binding profiles. When multiple TF binding profiles exist for a given TF, we selected, for each corresponding ChIP-seq dataset, the profile providing the best centrality P -value as assessed in UniBind. We did not consider JASPAR TF binding profiles associated with TF dimers. Datasets with <1000 TFBSs were filtered out (Additional file 1: Supplementary Table S2 ).

Benchmarking

Synthetic datasets.

We generated the synthetic datasets following the methodology mentioned above. We constructed synthetic datasets with 800, 1000, 4000, 8000, 10 000, 40 000 and 80 000 sequences with instances of a known motif inserted in 0.5, 1, 3, 5 and 10% of the sequences. We considered 13 motifs (3- to 19-bp long) from the JASPAR database ( 7 ), which were synthetically inserted into random sequences with varying spacing (6- to 22-bp) from the anchor sites. To run SpaMO, we inserted the CTCF (MA0139.1) motif at the center of the flanking sequences since SpaMO requires a motif to anchor its search for co-binding patterns.

Comparisons between COBIND and other tools

We used the synthetic data to compare COBIND with STREME ( 42 ), RSAT peak-motifs dyad-analysis ( 25 ), RSAT peak-motifs oligo-analysis ( 26 , 43 , 44 ), RSAT peak-motifs position-analysis ( 43 , 44 ) and SpaMo ( 21 ). We compared the motifs predicted by the different tools with the original inserted motifs using Tomtom ( 40 ) (Additional file 1: Supplementary Table S1 ). We considered a discovered motif as a match to the injected motif if Tomtom predicted them to be similar with a P -value <0.05. We provide the number of incorrectly predicted motifs. To run SpaMo, we used the JASPAR 2020 CORE collection of non-redundant profiles ( 45 ). We computed the F1 score and Matthew's correlation coefficient (MCC) to assess the capacity of the tools to retrieve the correct sequences where the motifs were injected. When the correct motif was predicted multiple times, we reported the one providing the highest F 1 score.

STREME and RSAT algorithms were run to discover a maximum of 18 motifs to match the number of possible motifs to be predicted by COBIND. All tools were run with default parameters otherwise. When estimating the running time, we used the parallelizable version of the NMF for COBIND.

Inference of the co-binding transcription factors from the discovered co-binding motifs

We assessed the similarities between a predicted co-binding motif and known motifs associated with 5272 TFs—collected from JASPAR 2022 non-redundant CORE, JASPAR 2022 non-redundant Unvalidated taxon-specific, and CIS-BP ( 5 )—using Tomtom (Additional file 1: Supplementary Table S1 ) ( 40 ). We predicted TF B to bind the predicted co-binding motif associated with anchor TFBSs of TF A if Tomtom reported a similarity P -value <0.05 with the known canonical motif bound by TF B (criteria I). To assess possible physical interactions between TF A and TF B , we retrieved protein-protein interaction (PPI) data from STRING v11.0 ( 46 ). We considered TF A and TF B to interact physically when their STRING PPI score was above or equal to 500 (criteria II). We reported the co-binding pair TF A -TF B to be already ‘known’ when they met criteria I and II. We reported TF A –TF B as a novel co-binding pair when they met criteria I but not II.

Shared co-binding motifs across transcription factor structural families

To assess the conservation of co-binding motifs across TFs, we ran COBIND (up to Step 5) on all UniBind datasets. We independently clustered all the predictions (COBIND Step 6) for each TF structural family; note that this step considered TFs from multiple species. TFs without known structural families were excluded from this analysis. When the co-binding motifs associated with two (or more) TFs from the same family were similar (i.e. belonged to the same cluster), we considered the TFs to share the same co-binding motif.

Evolutionary conservation

We downloaded the following conservation tracks in bigwig format from the UCSC genome browser: phastCons100way (human; hg38), phastCons60way (mouse; mm10), phastCons135way ( C. elegans ; WBcel235), and phastCons27way ( D. melanogaster ; dm6) ( 47 ). We obtained the 63 flowering plants PhastCons conservation track from PlantRegMap ( 48 ) for analyzing datasets from A. thaliana . We computed the conservation scores of a genomic region using the aggregate and extract functions of bwtool (v1.0) ( 49 ).

We compared the distributions of conservation scores between two regions with one-sided Wilcoxon signed-rank tests. Specifically, we compared the conservation values at the anchor (or co-binding) sites between the genomic regions predicted to harbor a co-binding pattern or not. These comparisons result in two P -values per co-binding pattern - one comparing the anchor sites and one comparing the co-binding sites. Histograms were used to summarize the P -values across datasets, considering the co-binding pattern with the lowest P -value per dataset. We compared the P -values to random expectation. Specifically, we randomly assigned, for each dataset, each genomic region to harbor or not a co-binding pattern (keeping the same number of regions in each category as the number predicted by COBIND). We performed five randomizations per dataset and reported the combined results.

DNase I hypersensitive footprinting analysis

We downloaded DNase I hypersensitivity (DHS) data generated by the ENCODE consortium for multiple human cell types in bigwig format at https://resources.altius.org/∼jvierstra/projects/footprinting.2020/ ( 50 ). We considered the 53 cell lines or tissues for which both DHS and COBIND predictions were available; we analyzed 66 DHS datasets associated with 70 co-binding configurations for 44 TF binding profiles as anchors. We followed the methodology described above to compare evolutionary conservation scores when comparing the DHS signal between two genomic regions (with or without a co-binding event predicted by COBIND).

Single-molecule footprinting analysis

We downloaded 12 double enzyme single-molecule footprinting (SMF) datasets produced from triple DNA methyltransferases knockout lines of mouse embryonic stem cells (mESCs) from ArrayExpress with accession numbers E-MTAB-9033 and E-MTAB-9123 ( 51 ). Whenever applicable, we performed the analyses of SMF data with the SingleMoleculeFootprinting R package following the instructions previously described ( 52 ). To compare the SMF data in the genomic regions predicted by COBIND to contain or not a co-binding event in mESC datasets, we modified the SortReadsByTFCluster_MultiSiteWrapper functions from the development version of the SingleMoleculeFootprinting package ( 53 ) ( https://bitbucket.org/CBGR/cobind_manuscript/src/master/bin/single_molecule/analyse_sm/sort_tf_reads_by_cobind_clusters.R ). Using the methylation status provided by the SMF data, we determined each genomic region to be either (i) free of nucleosomes (‘Accessible’), (ii) occupied by nucleosomes (‘Nucleosome’), (iii) bound only by the anchor (‘Anchor’), (iv) bound only by the co-binding TF (‘Co-binding’) or (v) bound by both the anchor and co-binding TF (‘Anchor + Co-binding’). We summed the number of molecules for each state in different samples and computed the proportions of molecules. We summarized the proportions of each state for all predictions and all anchors together. We compared the proportions of each state in genomic regions predicted to contain a co-binding event or not using a two-sided Wilcoxon signed-rank test. We compared the same type of regions in the ‘Anchor + Co-binding’ state for individual anchors using a one-sided Wilcoxon signed-rank test.

COBIND discovers co-binding patterns de novo

Comparisons with other tools on simulated data.

We report a new computational framework, COBIND, to predict space-fixed co-binding patterns in the vicinity of TFBSs provided as input. COBIND relies on applying NMF to the one-hot encoded regions flanking the user-provided TFBSs to predict co-occurring DNA motifs with fixed spacing (Materials and methods). We compared COBIND to other tools discovering motifs de novo (STREME, RSAT dyad-analysis , RSAT oligo-analysis  and RSAT position-analysis ). Additionally, we compared COBIND to SpaMO, which predicts spatially co-occurring instances of known motifs (Additional file 1: Supplementary Table S3 ). For comparisons, we generated synthetic data by injecting instances of known motifs (13 distinct motifs were used) at different frequencies in random sets of 800 to 80 000 DNA sequences (Materials and methods).

COBIND predicted the correct motif inserted when considering a minimum of 800 sequences for 10 of the 13 used inserted motifs. For example, COBIND accurately predicted the injected ATF4 motif in datasets of varying sizes without incorrect motif predictions (Figure 2 ). When considering the shorter TAL1 motif (3-bp known to be co-bound by the dimer GATA:TAL1), COBIND provides the most accurate predictions in multiple datasets when other tools obtain lower F 1 scores and MCC values and predict incorrect motifs (Additional file 1: Supplementary Figure S18 - S29 ). Overall, COBIND predicted no false positive motif across most configurations (see the number of incorrect motifs discovered in Additional file 1: Supplementary Figure S18 - S29 ). In most cases, COBIND discovered the injected motifs in the correct sequences, as illustrated by high F 1 scores and MCC values. Comparably, STREME discovered only correct motifs but failed to predict any motifs in some configurations (especially when the number of sequences with the injected motif was low). When considering many sequences (datasets of 40 000 and 80 000 sequences), COBIND and STREME recovered the correct motifs. SpaMo discovered the correct inserted motif in most cases, but it came at the cost of predicting many false positives. One should note that SpaMo requires a reference set of known motifs to predict co-occurrences, while COBIND predicts co-binding motifs de novo .

Comparisons between COBIND and other tools on simulated data. We ran COBIND, STREME, RSAT-dyads, RSAT-oligos, RSAT-positions and SPAMO (subfacets) on simulated data containing different numbers of sequences (from 800 to 80 000, see facets) with inserted JASPAR MA0833.2 TF binding motif instances for ATF4 (see Methods for details). We considered (i) F 1 scores and Matthew's correlation coefficient (MCC) (in case multiple correct motifs are found, a motif with the highest F 1 score is visualized). (ii) the number of incorrect motifs discovered in each experiment. Good performance of a tool would be indicated by a discovered motif with a high F 1 score and high MCC and no incorrectly discovered motifs.

COBIND exhibited a similar run time to the other tools on datasets with up to 10 000 sequences. Nevertheless, it was slower than the other tools, except STREME, with larger datasets (>40 000 sequences) due to the motif clustering step applied to the results of the NMF with 3–6 components. Notwithstanding, the COBIND workflow (from motif discovery with NMF to co-binding summary) takes ∼500 s on 40 000 sequences as it allows for parallelization across CPU cores (Additional file 1: Supplementary Figure S30 ).

Proof-of-concept: POU5F1 co-binding with SOX2 or SOX17

As a proof-of-concept, we applied COBIND to UniBind TFBS datasets for the SOX2 and SOX17 TFs. Previous studies established that both TFs partner with POU5F1 in pluripotent cells, where POU5F1 co-binds with SOX2 at regulatory elements to promote pluripotency. At the same time, it co-binds with SOX17 to control endoderm differentiation at other regulatory elements ( 11 , 16 ). While POU5F1 and SOX2 cooperate through binding at instances of their respective canonical motifs, POU5F1 can associate with SOX17 to bind a compressed motif (Figure 3A ). COBIND successfully discovered the two co-binding patterns - canonical and compressed - recognized by POU5F1 when applied to the SOX2 and SOX17 TFBS datasets from H. sapiens and M. musculus (Figure 3B ). COBIND retrieved the correct pattern in 11 human SOX2 datasets (58%), with ∼7% of the sequences predicted to harbor the pattern, and in 20 mouse datasets (27%), with ∼6% of the sequences harboring the pattern (Additional file 1: Supplementary Figure S31A, B ). In agreement with previous studies, the datasets where COBIND predicted the pattern derived from human and mouse ESC and mouse embryonic fibroblast ( 16 , 18 , 54 ). When considering the SOX17 datasets, COBIND successfully discovered the canonical co-binding pattern and the compressed motif. It predicted the compressed co-binding pattern in two datasets (50%; derived from mouse ESC), with 5% of the sequences harboring the co-binding motif (Additional file 1: Supplementary Figure S31C ).

Application of COBIND to SOX2 and SOX17 TFBS datasets. COBIND predicts POU5F1 to co-bind with SOX2 (human and mouse) and SOX17 (mouse), upholding the previously known mechanism of POU5F1 switching partner TFs and binding motifs with different syntax. ( A ) POU5F1 partners with SOX2 to bind its canonical motif. In contrast, POU5F1 associates with SOX17 to bind a compressed motif. ( B ) The co-binding patterns discovered in SOX2 and SOX17 datasets correspond to the expected canonical and compressed motifs. One co-binding pattern was discovered downstream of the anchor with either zero or one nucleotide between them. ( C ) The co-binding motif found in the SOX2 datasets was associated with POU5F1 using motif similarity and PPI data (significant motif similarity P -value < 0.05 and PPI combined score > 500). ( D ) Visual representation of the similarity between the discovered co-binding motif and motifs recognized by POU5F1 in JASPAR and CIS-BP.

In this proof-of-concept example, we knew a priori that POU5F1 was the binding partner of SOX2 and SOX17. Nevertheless, COBIND discovered de novo the co-binding motif. In other settings, one would not know the TF binding to the co-binding motif discovered. To address this challenge, we combined motif similarity to already known motifs from JASPAR and CIS-BP with protein-protein interaction (PPI) data from the STRING database to infer the TFs potentially binding the motifs revealed by COBIND (Materials and methods). This strategy confirmed that the framework inferred POU5F1 as the binding partner of SOX2 and SOX17 (Figure 3C , D ; Additional file 1: Supplementary Figure S31 ).

COBIND discovers known and novel co-binding patterns in a large-scale analysis across several species

We ran COBIND on 5699 UniBind TFBS datasets associated with 401 unique TFs from seven species (Materials and methods; Additional file 1: Supplementary Table S2 ). Beyond predicting co-binding patterns with COBIND, we inferred the TFs likely binding to the discovered patterns following the strategy described above (also see Materials and Methods). Altogether, COBIND revealed 591 co-binding patterns for 224 unique TFs (22 in A. thaliana datasets, 7 in C. elegans , 3 in D. rerio , 22 in D. melanogaster , 309 in H. sapiens , 217 in M. musculus and 11 in R. norvegicus ). For 78% of the reported co-binding motifs (462 out of 591), we found motif similarity and PPI data to support the inferred pair of co-binding TFs (Figure 4 ). Specifically, the data supported all co-binding patterns for D. rerio and C. elegans , more than half of the patterns for R. norvegicus , H. sapiens and M. musculus , 18.2% for A. thaliana and 31.8% for D. melanogaster . We provide all predictions for the community to explore through a dedicated website at https://cbgr.bitbucket.io/COBIND_results_page/ .

Overview of COBIND predicted co-binding patterns. The bar plot provides the number of co-binding patterns (y-axis) discovered by COBIND with (dark pink) or without (light pink) support from motif similarity and PPI data. The bars contain the corresponding numbers and percentages. Each bar summarizes the results for a species (x-axis).

Next, we evaluated the frequency at which a TF A is predicted as a partner of TF B and, conversely, TF B as a partner of TF A . We analyzed co-binding patterns predicted in a specific cell type for each TF A , provided data was available for a predicted co-binding TF B in the same cell type (noting that multiple TFs can recognize the same co-binding pattern). When examining datasets linked to TF B , COBIND successfully predicted back TF A in 77% of the co-binding patterns from human datasets and 84% from mouse datasets.

Furthermore, the analysis of motif similarity between COBIND’s predictions revealed that 67% (143 out of 214) of the TFs shared a co-binding motif with another member of the same TF structural family (Materials and Methods), which confirmed the conservation of co-binding patterns within families of TFs across species. It is noteworthy that anchor regions bound by TFs from the same structural family tend to overlap (Mann–Whitney U test P -value < 0.05) with those bound by different TF families, even though they typically show low Jaccard index values (Additional file 1: Supplementary Figure S32 ). This observation was consistent across different species, covering all regions with and without predicted co-binding patterns (Additional file 1: Supplementary Figure S32 ). However, no statistically significant differences were noted (Mann–Whitney U test P -value > 0.05) when comparing regions with predicted co-binding patterns to those without, irrespective of whether the same or different TF family members bind them.

As a case example, COBIND predicted two co-binding patterns around TEAD1 TFBSs. Specifically, COBIND revealed the same motif with different spacings from the provided anchor TFBSs (2nt upstream or 3nt downstream of TEAD1 TFBSs; Additional file 1: Supplementary Figure S33A ). We found that the two co-binding patterns occurred in ∼6% (3-bp downstream pattern) and ∼7% (2-bp upstream pattern) of the input sequences from mouse nerve tumor cells and in ∼3% (2-bp upstream and 3-bp downstream patterns each) of the sequences from human pancreas and astrocytoma cells. Our approach inferred that TEAD2 and TEAD4 cooperate with TEAD1 (Additional file 1: Supplementary Figure S33B, C ). Previous studies reported that TEAD TFs bind either an isolated M-CAT element or direct DNA repeats with spacing ranging from 0 to 6 bp ( 55–57 ). These studies further support the co-binding patterns predicted by COBIND. The predictions around TEAD1 TFBSs provide an example of COBIND’s capacity to predict co-binding events supported by motif similarity and PPI data.

To exemplify predictions of new co-binding TFs, COBIND revealed a co-binding motif located 4 bp upstream of HY5 anchor TFBSs in A. thaliana (Additional file 1: Supplementary Figure S34A ). The analyses of TFBS datasets associated with the TFs ABF1, ABF3, ABF4, GBF2 and GBF3 displayed the same co-binding pattern. We found that the canonical motifs recognized by human NF-Y TFs (NF-YC and NF-YA) were similar to the co-binding motif discovered by COBIND (Additional file 1: Supplementary Figure S34B, C ). However, no PPI data currently support the physical interactions between the anchor TFs and NF-Y orthologs in plants. Nevertheless, the bZIP67 TF, which belongs to the same basic leucine zipper (bZIP) family as HY5, ABF1, ABF3, ABF4, GBF2 and GBF3, is known to form a transcriptional complex together with NF-YC2 and bind ER stress response elements (ERSEs) to regulate omega-3 fatty acid content in A. thaliana ( 58 ). In agreement with this observation, we noted that the anchor motif combined with the co-binding motif discovered by COBIND forms the motif associated with ERSEs ( 59 ). Furthermore, HY5 competes with bZIP28, another member of the bZIP family, to bind ERSEs on promoters of unfolded protein response genes, and bZIP28 interacts with NF-Y protein complexes ( 59 , 60 ). Consistent with this knowledge, we found that most of the genomic regions predicted by COBIND to harbor the co-binding events in the HY5 dataset were in promoter regions (Additional file 1: Supplementary Figure S34D ). Altogether, these multiple lines of evidence strongly support the co-binding patterns predicted by COBIND between bZIP and NF-Y TFs in A. thaliana .

COBIND discovers the extended motif bound by CTCF

We investigated another example where COBIND predicted a co-binding pattern unsupported by motif similarity and PPI data. Specifically, COBIND identified a co-binding motif in the proximity of CTCF TFBSs for several datasets (Figure 5A ). We observed two spacing configurations between the anchor CTCF TFBSs and the predicted co-binding events. Across the datasets, COBIND predicted from 2.6 to 5% of the sequences to harbor the co-binding patterns. All datasets from human and zebrafish and 25% of the mouse datasets (95 out of 382), which cover a large spectrum of cell types (Additional file 1: Supplementary Table S4 ), contained the identified co-binding patterns (Additional file 1: Supplementary Figure S35 ). Previous studies have reported the motif predicted by COBIND as an extension of the canonical CTCF motif ( 61 , 62 ). These studies revealed that the eighth zinc finger of CTCF acts as a linker instead of a clamp to allow zinc fingers 9–11 to bind the extended motif (Figure 5B ), affecting the binding efficiency, residence time, and binding off-rate of CTCF ( 61 , 62 ). Consequently, COBIND did not predict co-binding between distinct TFs, but CTCF seldomly bound an extended motif through different combinations of zinc finger contacts with the DNA.

CTCF binds a conserved extended motif. ( A ) COBIND predicted two binding configurations through an extended motif for CTCF in human and mouse datasets. The extended binding pattern was discovered upstream of the anchor with either thirteen or fourteen nucleotides between them. ( B ) The two binding configurations derive from contacts between the zinc fingers (ZF) 9–11 and the DNA at the upstream motif. ( C ) Comparison between vertebrate evolutionary conservation of H. sapiens genomic regions harbouring the CTCF canonical motif only (left) and regions with the extended motif revealed higher conservation of the extended motif. The purple lines provide the mean conservation score across the regions considered. The grey lines provide the mean conservation score across the same number of random genomic regions in the human genome. ** indicates a Wilcoxon test P -value < 0.001 at the anchor (red) and motif sites (green).

Since evolutionary conservation is a hallmark of functional importance ( 47 ), we assessed the functional relevance of the genomic regions harboring the discovered binding patterns by examining their evolutionary conservation across vertebrates (Materials and Methods). We observed that the anchor and extended motif sites (Figure 5C , right, red and green segments, respectively) were more conserved in the regions predicted to harbor the extended motif than in the other regions (Wilcoxon test P -value < 0.001 for both segments; Figure 5C , right versus left). The increased evolutionary conservation was consistent across all spacing configurations observed in human and mouse. These results exemplify how COBIND can predict relevant DNA patterns that do not correspond to the co-binding of two proteins but to binding variants for the same TF, a particular case of the zinc finger families.

Genomic regions harboring a co-binding pattern are evolutionarily more conserved than regions without co-binding

We assessed the functional relevance of all the co-binding patterns discovered by COBIND across species by analyzing their evolutionary conservation (Materials and methods). Specifically, we compared the evolutionary conservation of the anchor and co-binding positions (Figure 5C , red and green, respectively) in genomic regions harboring a co-binding pattern, named co-bound regions for simplicity, to those without a predicted co-binding pattern. We first describe the analyses of the case studies presented above as examples. We found that the co-bound regions associated with SOX2::POU5F1 and TEAD1::TEAD in the human and mouse genomes exhibited a significantly increased evolutionary conservation compared to those without predicted co-binding (Wilcoxon test P -value < 0.001; Figure 6A ; Additional file 1: Supplementary Figure S36A ). Notably, we observed increased conservation at the co-binding and the anchor motifs. When considering the co-binding pattern associated with HY5 in A. thaliana , we did not observe a significant difference in conservation between the regions with and without the co-binding pattern. Nevertheless, both the anchor and the co-binding motifs exhibited peaks of evolutionary conservation, confirming the likely functional relevance of the predicted co-binding pattern across the flowering plant kingdom (Additional file 1: Supplementary Figure S37A ).

Analysis of the evolutionary conservation of co-binding patterns discovered by COBIND in the human genome. ( A ) Comparison between vertebrate evolutionary conservation of genomic regions where COBIND predicted a co-binding pattern (right) or not (left). The purple lines provide the mean conservation score across the regions considered. The grey lines provide the mean conservation score across the same number of random genomic regions in the human genome. ** indicates a Wilcoxon test P -value < 0.001. The anchor SOX2 anchor motif is underlined in red, and the co-binding motif is underlined in green. ( B ) We compared the evolutionary conservation scores at the anchors' motif locations in genomic regions harboring a co-binding pattern or not. The left panel represents the histogram of the proportion of datasets with the anchor sites harboring higher evolutionary conservation in co-bound regions than in other regions. The right panel represents the corresponding histogram when comparing genomic locations of the co-binding motif. Dark pink represents observed values, and light pink the expected values computed from random assignment of genomic regions predicted as co-bound or not.

Across all datasets and species, we systematically compared the evolutionary conservation at locations of the anchor and predicted co-binding sites discovered by COBIND in co-bound regions versus regions without co-binding sites. Altogether, we observed across species that 23% (for A. thaliana ), 55% (for D. melanogaster ), 60% (for C. elegans ), 82% (for H. Sapiens ), and 83% (for M. musculus ) of the datasets with predicted co-binding patterns exhibited a co-binding motif with significantly more conservation in co-bound regions than in the other regions (Figure 6B ; Additional file 1: Supplementary Figure S36B - S38 ). Furthermore, the associated anchor sites were more conserved in the co-bound regions for 31% ( A. thaliana ), 55% (for D. melanogaster ), 60% (for C. elegans ), 64% (for M. musculus ), and 67% ( H. sapiens ) of the datasets (Figure 6B ; Additional file 1: Supplementary Figure S36B , Supplementary Figure S37B , Supplementary Figure S38 ). Finally, we observed for 64% of the human datasets that the locations of both the anchor and a co-binding motif were more conserved in the co-bound regions than in regions without co-binding sites (61% for M. musculus , 60% for C. elegans , 45% for D. melanogaster , and 23% for A. thaliana ). We compared with random expectation to further support the higher conservation of the anchor and co-binding sites in co-bound regions than similar locations in regions without a predicted co-binding pattern. Specifically, we randomly assigned, for each dataset, each genomic region to harbor or not a co-binding pattern (keeping the same number of regions in each category as the number predicted by COBIND). Figure 6B and Additional file 1: Supplementary Figures S36B and S37B confirm that more datasets exhibit increased conservation in co-bound regions than expected by chance. The consistent increased evolutionary conservation of the co-binding patterns supports the functional importance of COBIND’s predictions across species. Furthermore, our results suggest that the underlying genomic regions harboring a fixed binding motif syntax are evolutionarily important.

DNase I hypersensitive footprints support the predicted co-binding patterns

We further assessed the co-binding patterns discovered by COBIND by analyzing orthogonal experimental data probing chromatin openness. The DNase-seq assay captures open chromatin regions by revealing DNase I hypersensitive sites (DHS) ( 63 ). Importantly, TFs interacting with the DNA in open chromatin regions protect their TFBSs from the DNase cleavage, which leaves a footprint on the corresponding TFBSs ( 50 , 64 ). We retrieved 66 DHS footprint datasets from 53 human cell types ( 50 ) that matched some of the UniBind TFBS datasets used in this study. This data allowed us to investigate DHS footprints at the discovered co-binding motifs for 70 co-binding patterns associated with 44 TF binding profiles as anchors. For each co-binding pattern, we compared the depth of the DHS footprint at the locations of the co-binding motifs between the co-bound regions and the other regions (Materials and methods).

As we ran COBIND on regions surrounding TFBSs predicted as high-quality direct TF–DNA interactions with ChIP-seq and computational evidence from UniBind, we expected DHS footprints at the anchor TFBSs. Indeed, we observed footprints of TF-DNA interactions; for instance, the DHS footprints observed for the CTCF datasets (Figure 7A ). Notably, the DHS footprints at the CTCF anchor locations were significantly deeper in regions predicted with the extended motif than in the other regions (Figure 7A , red segments). Furthermore, the analyses exhibited deep DHS footprints at the location of the co-binding motifs (Figure 7A , green segment on the right compared to the equivalent locations on the left panel). Overall, we found significantly deeper DHS footprints at the locations of a co-binding motif in the predicted co-bound regions than in the other regions for 85% of the co-binding pattern - DHS dataset pairs. In comparison, 26% was expected by chance when randomly assigned the genomic regions as co-bound or not (Figure 7B , right panel). The anchor TFBSs at co-bound regions exhibited deeper DHS footprints than the TFBSs in other regions for 78% of the pairs, while 21% were expected by chance (Figure 7B , left panel). When considering deeper DHS footprints at both the anchor and a co-binding pattern sites, it was observed for 73% of the pairs, while it was never observed with the random assignments (Figure 7B ). As CTCF datasets represented 43% of the total datasets analyzed here, we performed the same analysis, excluding the CTCF datasets. We found that 53% (25% expected by chance) of the co-binding pattern-DHS dataset pairs showed significant DHS footprints at the anchor TFBSs, and 68% (23% expected by chance) exhibited significantly deeper DHS footprints at the co-binding motif locations in co-bound regions than in the other regions. Altogether, 45% of the pairs exhibited deeper DHS footprints at both the co-bound and anchor TFBS locations. Overall, the DHS footprint analyses supported the co-occupancy of the anchor and the co-binding motifs at the co-bound regions.

DHS footprinting analyses at anchor and co-binding locations. ( A ) The plots represent the average DHS score in regions surrounding CTCF TFBSs in neuronal stem cells (purple lines) or at random regions (grey lines). The left plot provides DHS scores at genomic regions where COBIND did not predict the CTCF extended motif; the right plot provides the DHS scores at genomic regions harbouring the CTCF extended motif. ** indicates a Wilcoxon test P -value < 0.001. The anchor CTCF motif is underlined in red and the upstream portion of the extended motif is in green. ( B ) We compared DHS scores at the anchor motif locations in genomic regions harbouring a co-binding pattern or not. The left panel represents the histogram of the datasets where the DHS footprint was deeper in genomic regions harbouring a co-binding pattern. The right panel represents the corresponding histogram when comparing genomic locations of the co-binding motif locations. Dark pink represents observed values, and light pink the expectation computed from randomly assigning regions as containing or not a co-binding pattern.

Single-molecule footprints support co-occupancy at single-molecule resolution for some TFs

The DHS footprinting analysis described above relied on bulk DNase-seq data. Consequently, the results provided average estimations of the co-occupancy of TFs across cells. We aimed to investigate the co-occupancy of TFs at the co-binding patterns predicted by COBIND at single-molecule resolution. To this end, we considered single-molecule footprinting (SMF) data, which probed the co-occupancy of TFs and nucleosomes at single-molecule resolution for accessible genomic regions in mouse embryonic stem cells (mESC) ( 51 ). The SMF data overlapped 1694 regions with predicted co-binding patterns (483 453 reads) and 54 727 regions (14 457 475 reads) not predicted to contain any co-binding pattern for 17 TFs in mESC (Materials and Methods).

For each genomic region predicted by COBIND to contain a co-binding pattern or not, we determined the fractions of molecules in each of the following five states: (i) accessible, (ii) occupied by nucleosomes, (iii) only occupied at the anchor motif, (iv) only occupied at the co-binding motif or (v) co-occupied at both the anchor and the co-binding motifs. This is done by evaluating the binary methylation status of each molecule for each cytosine in and around the anchor and co-binding pattern sites (Figure 8A ). Figure 8B and  C illustrates the SMF data analysis at two genomic regions predicted to harbour co-binding events for SOX2 and POU5F1 (Figure 8B ) and the extended CTCF motif (Figure 8C ). We observed footprints of co-occupancy at both the anchor motif and the co-binding motif locations. Specifically, the analysis of 273 molecules across five replicates revealed co-occupancy for 27% of the molecules when considering the SOX2-associated region (Figure 8B ). Five hundred ninety-two molecules across six replicates revealed co-occupancy of the extended CTCF motif for 59% of the molecules (Figure 8C ). In contrast, the example regions where COBIND did not predict a co-binding pattern for SOX2 or CTCF, we only observed occupancy for the SOX2 anchor (17% of 89 molecules) or the canonical CTCF sites (46% of 297 molecules) while co-binding sites were unoccupied (Additional file 1: Supplementary Figure S39A-B ). When considering all the regions with SMF data, we found a larger fraction of co-occupied anchor and co-binding motif locations on the same molecule in COBIND-predicted co-bound regions than in other regions ( P -value < 0.001; Additional file 1: Supplementary Figure S39C ). Significant differences existed in all state abundances, except for the specific occupancy of the co-binding sites. This agrees with these sites not being occupied when the anchor sites are not either. However, not all anchor TF binding profiles satisfied this observation (Figure 8D ). Moreover, we found that genomic regions predicted by COBIND to harbour a co-binding pattern were more occupied (for ‘Anchor + Co-binding’ occupancy state independently) than the other genomic regions for 13 out of 17 anchor TF; statistical significance was observed for 8 TF ( P -value < 0.05; Figure 8D ). Altogether, the results confirmed that SMF data supported the COBIND co-occupancy predictions for some TFs, with the co-bound regions more accessible or co-occupied at single-molecule resolution.

Single-molecule footprinting analysis. ( A ) For each region, single molecules are grouped and assigned to one of the five states (‘Co-binding + Anchor’, ‘Co-binding’, ‘Anchor’, ‘Accessible’, and ‘Nucleosome’) based on the binary cytosine methylation status in and around anchor and co-binding patterns (for two regions of interest, four bins are analysed). (B, C) Single-locus examples of predicted co-bound regions where the anchor TFBSs associate with SOX2 ( B ) and CTCF ( C ). Line plots in the top panels represent the average methylation levels. We provide logos and locations of the anchor motifs (red) and the predicted co-binding motifs (green). Stacks of sorted single molecules are visualized in the lower panels (light grey indicates methylated Cs, accessible positions; black - unmethylated Cs, protected). The y-axis corresponds to the total number of molecules. Coloured bars and percentages show the proportion of molecules in each state (colours corresponding to A). ( D ) We provide boxplots and dotplots showing the distribution of ‘Anchor + Co-binding’ state frequencies of molecules that overlap the regions in two groups: one with predicted co-binding (shown in dark pink) and one with only an anchor TFBS (shown in light pink). The regions with co-binding events show a significantly higher frequency of molecules as co-occupied (‘Anchor + Co-binding’). The differences between groups persist for molecules in other states. ** represents a Wilcoxon test P -value < 0.05.

We introduced COBIND, a computational framework for discovering de novo space-fixed DNA motif patterns in the vicinity of predetermined genomic regions. We applied COBIND to sets of TFBSs from UniBind to identify locally enriched DNA motifs that define co-binding patterns of cooperative TFs. The method uncovered both established and new co-binding patterns, with most TFs sharing a co-binding motif with other TFs from the same family. Additionally, we inferred, when possible, the TFs most likely co-binding the discovered patterns. We make the collection of co-binding patterns revealed by COBIND available to the scientific community for each of the 214 TFs and 58 TF families across seven species. The co-binding events captured by COBIND are likely functionally relevant since they exhibit higher evolutionary conservation than isolated TFBSs. Furthermore, chromatin and single-molecule footprinting data support the occurrence of the identified co-binding events on the same DNA molecules.

We used a combination of motif similarity and prior knowledge of PPIs to identify TFs that may cooperatively bind to the patterns discovered by COBIND. Identifying well-known TF co-binding events, such as POU5F1 with SOX2 or SOX17, essential pluripotency regulators ( 15 , 16 , 65 ), confirmed the approach's efficacy. Importantly, we used a multi-species collection of DNA binding motifs to predict new co-binding configurations for pairs of TFs currently unsupported by PPIs. We discovered that NF-Y proteins are potential partners for HY5, ABF1, 3–4 and GBF2-3 TFs. Previous studies identified NF-Y proteins as co-binders for bZIP67 and bZIP28 proteins belonging to the same TF family ( 58–60 ). More specifically, other studies also pointed to physical interactions of either HY5 or ABF1, 3–4 with NF-YC9 proteins ( 66 , 67 ). We identified the NF-Y-bound co-binding motifs using motifs associated with human TFs because DNA binding profiles for NF-Y are limited in A. thaliana . As HY5 competes with bZIP28, we acknowledge that the NF-Y binding pattern observed close to HY5 TFBSs might be occupied only when bZIP28 is binding. Nevertheless, using multi-species libraries generated potential co-binding predictions that will require further validations to decipher the binding cooperation of TFs at the corresponding genomic regions.

We observed that the COBIND predictions associated with CTCF anchor TFBSs exhibited a binding pattern that did not result from the co-binding of two TFs. Previous studies have discussed this binding pattern and have suggested that CTCF co-binds with ZBTB3 in mouse liver and various human cell lines, including embryonic stem cells and K562 ( 68 , 69 ). Based on motif similarity, we inferred that ZBTB3 could also bind the additional motif identified by COBIND. Instead, the co-binding pattern identified by COBIND corresponds to an extended binding motif for CTCF, involving zinc fingers 9–11 ( 61 ). This orthogonal evidence allowed the two variants of extended CTCF motifs to complement the JASPAR database (MA1930.1 and MA1929.1) ( 7 , 70 ). Furthermore, this binding pattern is critical for optimal CTCF protein residence time on DNA ( 61 ). Importantly, we found that the genomic regions harbouring the extended motif were conserved in both mouse and human genomes, supporting the functional relevance of this binding pattern. Studies frequently reported that zinc-finger proteins bind DNA through a subset of their zinc fingers, but it remains unclear whether the other fingers have additional binding preferences ( 71 ). Thus, binding to extended motifs with additional zinc fingers may be a common characteristic of zinc finger proteins that de novo motif prediction tools like COBIND could capture.

The conservation of TFBSs is an essential indicator of their functional relevance in gene regulation ( 47 ). Furthermore, the combinatorial binding of TFs is important for evolutionary stability, and increased co-binding of TFs associates with a higher probability of a regulatory region being conserved ( 72 ). This study analysed the conservation of co-binding patterns across different species. The regions predicted to harbor TFBSs with fixed spacing were more evolutionarily conserved than those with a single TFBS. The increase in conservation at regions where multiple TFs are likely co-binding compared to single events confirms other reports showing that (i) TFBSs of cooperative TFs are more evolutionarily conserved in mammalian embryonic stem cells ( 73 ) and drosophila ( 74 ) and (ii) that TF cooperativity can increase speed of evolution ( 75 ). Interestingly, not only were the predicted co-bound motif locations conserved, but the anchor motif locations also showed an increase in conservation, which agrees with the previous reports. We found most co-binding patterns with significant conservation from the human and mouse datasets, which may reflect the more significant number of datasets analysed for these species. Overall, the increased conservation of both the anchor and co-binding sites was found in more datasets than expected by chance. However, this was not observed for A. thaliana ; we hypothesize that this is due to the limited number of conservation tracks, which was restricted to flowering plants ( 48 ).

Complementing the conservation analysis, similar results were observed in the DHS footprinting data. Predicted co-binding patterns, along with their anchors, exhibited deeper footprints compared to single TFBSs. A lower signal may indicate a higher false positive rate for single binding events. However, it can also reflect single binding events occurring in a smaller subset of cells while cooperative binding would be shared across cells. Future experiments probing TF binding at single-cell resolution would allow for assessing this hypothesis.

COBIND is a tool for the de novo discovery of co-occurring DNA patterns. Our synthetic data assessment found that STREME performed well in retrieving the injected motifs de novo . However, it underperformed with small numbers of sequences. Moreover, STREME, unlike COBIND, is not intended to capture specific cooperative binding events; therefore, capturing the specific spacings and orientations relative to an anchor motif would require specific post-processing steps. Importantly, these tools do not require a priori knowledge of DNA motifs, such as a known DNA motif binding library, to perform the discovery. The requirement of known motif collections as an input is a standard limitation for other tools dealing with TF binding analysis, such as SpaMo or TACO ( 20 , 21 ). This reliance on existing collections of DNA binding motifs can be problematic as many TF binding motifs still need to be discovered or included. The only input required for COBIND is a set of genomic reference regions to ‘anchor’ the analysis and generate regions where it will search for patterns. The de novo discovery can reveal potential new or unannotated DNA pattern motifs, thereby circumventing the constraints of other tools. As such, COBIND offers a powerful alternative to other motif discovery tools that rely on existing collections of DNA binding motifs. Nonetheless, a limitation is that additional analysis will be required to interpret newly discovered motifs.

In this study, we applied COBIND to genomic regions near TFBSs. However, one can use COBIND in different biological contexts. As a test case, we generated anchor regions for analysis by taking two nucleotides at the donor and acceptor sites of intron-exon boundaries in the human genome. Analysis with COBIND recovered known donor and acceptor motifs (Additional file 2: Supplementary Figure S40 ), demonstrating the versatility of the approach for DNA pattern discovery in other types of biological problems.

We utilized the available SMF data to assess the co-occupancy of the anchor and co-binding motif instances on the same DNA molecules ( 52 ). A limitation of the assay is that one can only apply it to organisms and cell lines that can survive without endogenous methylation. We restricted our analyses to mouse ESCs, for which SMF was already available. A recent study assessed chromatin openness using methylation in the GpC context, allowing applications to different cell types with larger genomic coverage ( 76 ). The SMF data we used was produced with a targeted sequencing approach. Consequently, only a selected set of genomic regions was analyzed, and the overlap with regions containing COBIND-predicted co-binding patterns was incomplete, limiting the full understanding of possible co-occupancy on single molecules.

We recognize that COBIND and this study have several limitations. A fundamental limitation of COBIND is the restricted prediction of co-binding motifs with fixed spacing with the anchor motif. Because of the strict spacing requirements and the limitations of the NMF, COBIND restricts the search space to the close proximity of TFBSs. Previous studies have hypothesized the existence of two distinct sets of enhancers with distinct information-processing mechanisms ( 10 ). One proposed mechanism distinguishes between enhancers where TF binding is highly cooperative and coordinated with fixed spacing, referred to as the ‘enhanceosome’ model, and enhancers where TF cooperation is flexible, known as the ‘billboard’ model ( 77 ). Regardless, these mechanisms are compatible with the presence of both fixed and flexible cooperation of TFs at regulatory elements, depending on the type of TF cooperation. Recent studies support both mechanisms, and even though some suggest that TFBS spacing and orientation are not key determinants of transcription regulation, other studies contradict this statement ( 10–14 ). The evolutionary conservation of the co-binding patterns revealed by COBIND further argues for the functional relevance of a strict grammar for a set of TFs and binding regions.

COBIND relies on the NMF algorithm to discover the co-binding patterns, so it requires enough sequences harbouring the fixed pattern. Our simulated data showed that we obtained reliable results when at least 10% of at least 800 sequences (also 5% of 1000 sequences) contained the pattern to be predicted by COBIND. Since we considered the anchor TFBS and the flanks independently in the COBIND processing, discovering overlapping motifs represents a challenge. COBIND could detect such overlaps by revealing a small co-binding motif, but interpreting such results becomes increasingly difficult as the partial motif becomes small. Furthermore, we used anchor TFBSs predicted from TF binding profiles corresponding to the canonical motifs recognized by the corresponding TFs. This approach prohibits the identification of co-binding patterns where the anchor TFs would recognize altered motifs when cooperating with other proteins. Furthermore, repetitive and low-complexity regions are established issues for de novo motif discovery tools ( 78 , 79 ). For instance, A/T-rich patterns with low complexity, such as A/T stretches, were discovered in 0.5% of random genomics regions matching the %GC composition of the real datasets used for the DHS analysis (Additional file 2: Supplementary Figure S41 - Supplementary Figure S42 ). Therefore, we recommend that users interpret such patterns cautiously. Finally, another limitation lies in the computational time necessary for motif clustering when the NMF identifies many possible motifs. Nevertheless, our comparison to other tools revealed that COBIND’s computational time was not prohibitive.

COBIND is implemented in Python, R, and C++. The COBIND source code and documentation are available at https://bitbucket.org/CBGR/cobind_tool/src/main/ . The source code and data to reproduce the results outlined in this report are available at https://bitbucket.org/CBGR/cobind_manuscript/src/master/ and pre-processed data deposited at https://doi.org/10.5281/zenodo.7681482 . All results presented here are available to the community through a webpage at https://cbgr.bitbucket.io/COBIND_results_page/ .

Supplementary Data are available at NAR Online.

As ‘research parasites’ ( 80 ), we thank all the researchers who made their data available. We thank Marcel Schulz for his suggestion on protein-protein interaction data analysis, François Parcy for fruitful discussion on the plant co-binding motifs and his help finding the plant evolutionary conservation data, Judith Zaugg for providing feedback on the project, Roza Berhanu Lemma, Vipin Kumar, Ladislav Hovan and Rafael Riudavets Puig for reading the manuscript and providing valuable feedback, Harold Gutch, Torfinn Nome, and the NCMM IT team for their IT support, Ingrid Kjelsvik for administrative support, and the members of the Kuijjer and Mathelier groups for insightful discussions.

We follow here the Contributor Roles Taxonomy (CRediT) ( 81 ). Ieva Rauluseviciute: methodology, software, validation, investigation, formal analysis, writing - original draft, visualization; Timothée Launay: methodology, investigation, software, writing - review & editing; Guido Barzaghi: resources, writing - review & editing; Sarvesh Nikumbh: writing - review & editing; Boris Lenhard: writing - review & editing, funding acquisition; Arnaud Regis Krebs: writing - review & editing, funding acquisition; Jaime A. Castro-Mondragon: conceptualization, methodology, writing - review & editing; Anthony Mathelier: conceptualization, methodology, writing - review & editing, supervision, project administration, funding acquisition.

Research Council of Norway [187615]; Helse Sør-Øst; University of Oslo through the Centre for Molecular Medicine Norway (NCMM) (to Mathelier group); Norwegian Cancer Society [197884 to Mathelier group]; Research Council of Norway [288404 to Mathelier group]; Nordic EMBL Partnership Hub for Molecular Medicine, NordForsk Grant [96782 to I.R.]; Deutsche Forschungsgemeinschaft [KR 5247/1-1, salary of G.B.]; core funding from the EMBL and the Deutsche Forschungsgemeinschaft [KR 5247/1-2] (to support research in the laboratory of A.R.K.); Wellcome Trust Joint-Investigator award [106955/Z/15/Z to B.L.]; core funding from the MRC LMS (salary for S.N.). Funding for open access charge: Research Council of Norway.

Conflict of interest statement . None declared.

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Preoperative cone beam computed topography assessment of maxillary sinus variations in dental implant patients.

1. Introduction

2. materials and methods, 2.1. study design, 2.2. image analysis, 2.3. statistical analysis, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Misăiloaie, A.; Tărăboanță, I.; Budacu, C.C.; Sava, A. Preoperative Cone Beam Computed Topography Assessment of Maxillary Sinus Variations in Dental Implant Patients. Diagnostics 2024 , 14 , 1929. https://doi.org/10.3390/diagnostics14171929

Misăiloaie A, Tărăboanță I, Budacu CC, Sava A. Preoperative Cone Beam Computed Topography Assessment of Maxillary Sinus Variations in Dental Implant Patients. Diagnostics . 2024; 14(17):1929. https://doi.org/10.3390/diagnostics14171929

Misăiloaie, Alexandru, Ionuț Tărăboanță, Cristian Constantin Budacu, and Anca Sava. 2024. "Preoperative Cone Beam Computed Topography Assessment of Maxillary Sinus Variations in Dental Implant Patients" Diagnostics 14, no. 17: 1929. https://doi.org/10.3390/diagnostics14171929

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Recent Advances in Diagnosing and Treating Post-Prostatectomy Urinary Incontinence

• Urologic Oncology
• Published: 31 August 2024

Cite this article

• Yunlong Li MM 1   na1 ,
• YingMing Xiao MM 1   na1 ,
• Zhengang Shen MM 1 ,
• ShengKe Yang MD 1 ,
• Zeng Li MM 1 ,
• Hong Liao MM 1 &
• Shukui Zhou PhD, MD 1  

Radical prostatectomy and radiotherapy are common first-line treatments for clinically localized prostate cancer. Despite advances in surgical technology and multidisciplinary management, post-prostatectomy urinary incontinence (PPI) remains a common clinical complication. The incidence and duration of PPI are highly heterogeneous, varying considerably between individuals. Post-prostatectomy urinary incontinence may result from a combination of factors, including patient characteristics, lower urinary tract function, and surgical procedures. Physicians often rely on detailed medical history, physical examinations, voiding diaries, pad tests, and questionnaires-based symptoms to identify critical factors and select appropriate treatment options. Post-prostatectomy urinary incontinence treatment can be divided into conservative treatment and surgical interventions, depending on the severity and type of incontinence. Pelvic floor muscle training and lifestyle interventions are commonly conservative strategies. When conservative treatment fails, surgery is frequently recommended, and the artificial urethral sphincter remains the “gold standard” surgical intervention for PPI. This review focuses on the diagnosis and treatment of PPI, based on the most recent clinical research and recommendations of guidelines, including epidemiology and risk factors, diagnostic methods, and treatment strategies, aimed at presenting a comprehensive overview of the latest advances in this field and assisting doctors in providing personalized treatment options for patients with PPI.

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Acknowledgments

This research was supported by the Natural Science Foundation of Sichuan Science and Technology Agency (No. 2024NSFSC0699).

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Yunlong Li and YingMing Xiao have contributed equally to this work.

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Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China

Yunlong Li MM, YingMing Xiao MM, Zhengang Shen MM, ShengKe Yang MD, Zeng Li MM, Hong Liao MM & Shukui Zhou PhD, MD

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Yunlong Li, Shukui Zhou, and Hong Liao conceptualized the article. Yunlong Li and YingMing Xiao were involved in writing the article. Yunlong Li prepared the figures. Shukui Zhou and Hong Liao critically revised the manuscript and figures. All authors read and approved the final manuscript.

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Li, Y., Xiao, Y., Shen, Z. et al. Recent Advances in Diagnosing and Treating Post-Prostatectomy Urinary Incontinence. Ann Surg Oncol (2024). https://doi.org/10.1245/s10434-024-16110-1

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Received : 15 July 2024

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DOI : https://doi.org/10.1245/s10434-024-16110-1

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