Skip to main content
Premium Trial:

Request an Annual Quote

Cynthia Gibas, Assistant Professor, Department of Biology, Virginia Tech


PhD in biophysics and computational biology from the University of Illinois.
Joined the biology department at Virginia Tech as an assistant professor in August 2000. Involved in planning for the Virginia Bioinformatics Institute, which formally opened last summer.
Co-author of Developing Bioinformatics Computer Skills, recently published by O’Reilly & Associates.

QWhere will bioinformatics be in two years? Five years?

AStill here and still growing as a field — and probably for many years after that.

QWhat are the biggest challenges the field of bioinformatics faces?

AI think usability of results is a big issue. Great search tools and big databases are only useful if users are getting results back in a form that really helps answer their questions. As the databases get bigger, and as there are more types of information being collected, there''s going to be an increasing need for methods that integrate multiple searches and analyses, create compact, effective visualizations, and help users interpret results.

QWhat do you see as the most important task for bioinformatics to address beyond genome sequencing?

AGenome sequencing is only the very beginning. We don''t even know where all the genes are, let alone what they do and when they do it. When you get down to it, the task in front of us as biologists is the same as it''s always been — to integrate a massive amount of complex information from different experimental sources into models of functioning biological systems.

Bioinformatics will help this happen faster, but it''s not going to be as a result of one specific project or task. Information technology is eventually going to work its way into every corner of biological sciences, but it will happen piece by piece. There are questions to be answered at every level, from protein structure/function to cellular physiology and beyond. Probably the biggest hurdle to overcome will be developing public databases and data standards for information that''s more complicated and open to interpretation than just sequence or structure.

QWhat hardware do you use?

AStandard commercial PCs running Linux. I''ve got a small Beowulf cluster based on AMD Athlon processors, a Dell server, and a few Dell Pentium III boxes.

QHow would you compare the quality of publicly available and commercially available bioinformatics products?

ACommercial software products tend to have nice graphical interfaces and to be easier for novices to work with. In my research and in the classes I teach, though, I primarily work with publicly available software. That''s the only way to get your hands on the latest methods, because it takes a while for even the best commercial vendors to implement new ideas in their software packages. It''s also the only way to be certain that methods are actually implemented correctly; commercial software is usually set up as a "black box," so it''s hard to get at the details of what it''s doing.

QWhat non-existing technology do you most wish you had?

AI’m interested in automating multi-step analysis processes — figuring out what software users do to answer a particular type of question and then integrating the steps of the process.

In graduate school, I did a lot of what you’d probably call protein structure annotation — hunting down information about individual amino acids, functional and structural effects of mutations, etc., in the literature — combined with structural analysis, to develop hypotheses about protein function. I’d like to come up with ways of collecting and presenting that information so that interpretation of the information would be less labor-intensive for the user.

QWhat made you decide to become a bioinformaticist?

AI started out in biophysics, measuring rates of charge transfer in photosynthetic reaction centers. I got into computer modeling with a project that was supposed to help us select targets for site-directed mutagenesis. Then, I found that the modeling project was much more interesting to me than the actual laboratory work. From there, it was only a few short steps to developing bioinformatics software.

Filed under

The Scan

Fertility Fraud Found

Consumer genetic testing has uncovered cases of fertility fraud that are leading to lawsuits, according to USA Today.

Ties Between Vigorous Exercise, ALS in Genetically At-Risk People

Regular strenuous exercise could contribute to motor neuron disease development among those already at genetic risk, Sky News reports.

Test Warning

The Guardian writes that the US regulators have warned against using a rapid COVID-19 test that is a key part of mass testing in the UK.

Science Papers Examine Feedback Mechanism Affecting Xist, Continuous Health Monitoring for Precision Medicine

In Science this week: analysis of cis confinement of the X-inactive specific transcript, and more.