AdvaitaBio, a leading provider of bioinformatics to core facilities, researchers and pharmaceutical companies, recently conducted a global poll of bioinformatics service providers in order to determine what factors improve the overall quality, productivity and customer satisfaction.

Bioinformatics core facilities and service providers participate in and support research in several ways, including experimental design, analysis, and meta-analysis of experimental results. Cores and service providers create, select, maintain, and train on software to support this work and their customers’ research. This survey delves into how they choose and value software solutions.

Quality is Critical

The first survey question asked was what criteria the core facility used to select bioinformatics software for their use. Not surprisingly, quality of results was—by far— the most important factor.

How do they determine quality in software? Software products range from homegrown, to open source, to out-of-the-box commercial solutions. Quality signals in bioinformatics software come from the literature, word of mouth (recommendations from colleagues, reviews online), fitness of the tool to the purpose/research, and quality of support and underlying data. Independent studies evaluate various algorithms and determine which provides the most accurate, unbiased results given well-characterized input data. Some algorithms are shared across tools, while others are found in only one available package. Interestingly, popularity is not necessarily correlated with accuracy. This was recently illustrated in a benchmarking effort published in Genome Biology. The work presented in this paper was done with “gold standard” datasets in which the true cause of the phenotype is known. Surprisingly, the most popular method, enrichment analysis, had the lowest accuracy.

What Interferes with Efficiency?

The next survey question focused on what interferes with the core facility working efficiently. As the chart below shows, the top four answers were:

  • re-doing projects to incorporate small adjustments,
  • time spent searching for small details in projects completed long ago,
  • time wasted deciphering unclear answers from software packages, and
  • re-learning knowledge that was the domain of a recently- departed team member.

These are the issues that core facilities wrestle with on a regular basis and that hold up analysis for customers and potentially cause discontent with those customers.

Rework issues was the top issue for core facilities. Often, work is bid to do analysis but profitability gets chewed up in the constant questions, follow-up requests, and re-work required by investigators.

How to Improve Productivity

Finally, the study asked what factors would improve their productivity of the core facility. The chart below shows the results of that survey. Do these results match the experience at your core facility?

What this shows is that as highly-skilled scientists providing a variety of services for a large number of customers, core facilities must have very specific needs that should be addressed by their bioinformatics software.

Core Facilities should be choosing software that:

  1. Is High Quality. Delivers the best results in independent studies. Quality is not a function of cost of software. It is best determined by evaluating the software versus known results to see if simulations match these known results.
  2. Empowers scientist to do their own exploration. This requires a superior user interface, easy to understand user interface, cloud technology to make it accessible from anywhere and no restrictions on sub-licensing. In addition, the software must provide an interesting story to help the scientist better understand what is happening.
  3. Allows easy sharing of results or output. Again, with no restrictions on licensing.
  4. Provides superior audit trails to eliminate issues around not remembering how analysis may have been done months ago or due to loss of institutional memory due to attrition.
  5. Avoids having to do the same work over and over again for minor adjustments. Help the scientist be more engaged in the process after the heavy lifting is done by the core facility.
  6. Has bioinformatics capabilities that help the scientist tell a story as opposed to simply giving them data is an essential part of improved quality and productivity. It is both the quality of the results and how those results are presented as well as the ability to engage that scientist in self-exploration that will improve overall customer satisfaction, reduce backlogs and improve outcomes.

Does your software have these characteristics? Our customers tell us that they have gained control of the above issues and dramatically improved their analytical capabilities, empowered scientists to be more self-sufficient with re-work and saved money in the process.  Click here to read their case studies.  We can do the same for you.

Contact us if you care about these issues.

Categories

Analyze Now

  1. Register to explore demo data
  2. Subscribe to analyze your ‘omics data
  3. Review and interact with pathways impacted in your experiment
  4. Share your results with collaborators for interpretation and analysis iteration
  5. Create publication-ready figures simply and easily

What You Can Expect

  • Better Insights
  • Higher Quality
  • Superior Convenience
  • Unmatched Usability
  • Unparalleled Reproducibility
Register to Explore Advaita’s Platform with Demo Data

Get Started!

Get in touch with Advaita to learn how our software will improve quality and efficiency for your Core Facility, Enterprise Bioinformatics team, or Research Lab.

=

About the Author: Sorin Draghici

Sorin Draghici
Dr. Draghici is a Professor in the Department of Computer Science, and the head of the Intelligent Systems and Bioinformatics Laboratory at Wayne State University. He also holds a joint appointment in the Department Obstetrics and Gynecology and is an Associate Dean in Wayne State University's College of Engineering. Dr. Draghici is a senior member of IEEE, and an editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Protocols in Bioinformatics, Discoveries Journals, Journal of Biomedicine and Biotechnology, and International Journal of Functional Informatics and Personalized Medicine. His publications include two books (”Data Analysis Tools for DNA Microarrays and Statistics” and ”Data Analysis for Microarrays using R”), 8 book chapters, and over 150 peer-reviewed journal and conference publications which gathered over 12,000 citations to date.