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Bioinformatics Advance Access published online on May 7, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm239
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Bioinformatics Software for Biologists in the Genomics Era

Sudhir Kumar 1,* and Joel Dudley 2

1Center for Evolutionary Functional Genomics, The Biodesign Institute and School of Life Sciences, Arizona State University, Tempe, Arizona 85287-5301, USA. 2Stanford Medical Informatics, Stanford University, Stanford, CA 94305-5479, USA.

*To whom correspondence should be addressed. Dr. Sudhir Kumar, E-mail: s.kumar{at}asu.edu


  Abstract

Motivation: The genome sequencing revolution is approaching a landmark figure of 1,000 completely sequenced genomes. Coupled with fast-declining, per-base sequencing costs, this influx of DNA sequence data has encouraged laboratory scientists to engage large datasets in comparative sequence analyses for making evolutionary, functional and translational inferences. However, the majority of the scientists at the forefront of experimental research are not bioinformaticians, so a gap exists between the user-friendly software needed and the scripting/programming infrastructure often employed for the analysis of large numbers of genes, long genomic segments, and groups of sequences. We see an urgent need for the expansion of the fundamental paradigms under which biologist-friendly software tools are designed and developed to fulfill the needs of biologists to analyze large datasets by using sophisticated computational methods. We argue that the design principles need to be sensitive to the reality that comparatively small teams of biologists have historically developed some of the most popular biological software packages in molecular evolutionary analysis. Furthermore, biological intuitiveness and biological investigator empowerment need to take precedence over the current supposition that biologists should re-tool and become programmers when analyzing genome scale data sets.

Associate Editor: Dr. Jonathan Wren

Received on March 16, 2007; revised on April 26, 2007; accepted on April 26, 2007

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