Bioinformatics Provides Clues into Interspecies Genetic Similarities

The study’s findings, published May 17, 2012, in the open access journal PLoS Computational Biology, revealed how bioinformatics makes it possible to study the most basic tenets on which life science is constructed. Examining genes helps life science researchers better determine how the human body functions and how diseases progress. Scientists have long studied model species--mice, for example--to understand human biology better. This is at the basis of what is called the “ortholog conjecture,” the hypothesis that one can take what scientists learn from a few species and apply it to many.

In genetics, scientists address a questions such as, is it better to compare genes in mice and humans that directly descend from a common ancestor (these are called orthologs) --or to compare flawed copies of genes within a human being (the ‘paralogs’)? For the past 40 years, scientists have gone with the ortholog strategy, and this has worked very well. Studying genes in model species has provided invaluable insights in all areas of biology. However, until now, there has not been enough data to resolve this question with confidence. With developments in biotechnology generating vast quantities of data every day, there is finally enough to resolve the debate.

Using sophisticated computational techniques on data derived from tens of thousands of scientific articles, the researchers, from the Swiss Institute of Bioinformatics (SIB; Lausanne, Switzerland; www.isb-sib.ch) and the European Molecular Biology Laboratory (EMBL)-European Bioinformatics Institute (Hinxton, Cambridgeshire, UK), analyzed 400,000 pairs of genes (orthologs and paralogs) from 13 different species. The team compared the two approaches and chose the best strategy.

“We have the data to prove that the study of orthologs is indeed useful, but we are only at the beginning,” said Prof. Marc Robinson-Rechavi of SIB and the University of Lausanne (Switzerland). “This is at the heart of all of comparative genomics, in which we try to extrapolate knowledge from a handful of organisms and apply it to all of life.”

“We found that current experimental annotations do support the standard model,” explained Christophe Dessimoz of EMBL-EBI. “Our work corroborates the assumption that studying the genes of other species--whether mice, yeast, or even bacteria--can elucidate aspects of human biology.”

The same question has recently been addressed by Matthew Hahn and colleagues from the University of Indiana (IN, USA), whose different conclusion triggered some debate. The new research demonstrates that these controversial findings were due to overlooked biases in the collective knowledge of gene function. Controlling for these, the new study unequivocally supports the ortholog conjecture and the fact that studying species we are only distantly related to--even worms, flies, yeasts, or bacteria--is pertinent and useful.

All of the data used in the study was freely available, including the genome sequences and research knowledge described in thousands of publications. EMBL-European Bioinformatics Institute’s ELIXIR will build on this innovation and provide the next generation of infrastructure for biologic information in Europe and worldwide. The purpose of ELIXIR is to construct and operate a sustainable infrastructure for biologic data in Europe to support life science research and its translation to medicine and the environment, the bioindustries, and society.

Related Links:
SIB Swiss Institute of Bioinformatics
European Molecular Biology Laboratory-European Bioinformatics Institute
ELIXIR