Comparative genomics - A perspective (2024)

Genome correspondence

Genome correspondence [1], the method of determining the correct correspondence of chromosomal segments andfunctional elements across the species compared is the first step in comparative genomics. This involves determining orthologous (genes diverged after a speciation event)segments of DNA that descend from the same region in the common ancestor of the species compared, and paralogous (genes diverged after a duplication event) regions that aroseby duplication events prior to the divergence of the species compared. The mapping of regions across two genomes can be one-to-one in absence of duplication events; one-tomanyif a region has undergone duplication or loss in one of the species, or many-to-many if duplication/loss has occurred in both lineages. Fitch et al., [2] developed amethod called BBH (Best Bidirectional Hits), which identifies gene pairs that are best matches of each other as orthologous. Tatusov et al., [3] further enhanced thismethod, which matches groups of genes to groups of genes.

Understanding the ancestry of the functional elements compared is central to our understanding and applications of genome comparison. Most comparative methods havefocused on one-to-one orthologous regions, but it is equally important to recognize which segments have undergone duplication events, and which segments were lost since thedivergence of the species. Comparing segments that arose before the divergence of the species may result in the wrong interpretations of sequence conservation anddivergence. Further, in the presence of gene duplication, some of the evolutionary constraints that a region is under are relieved, and uniform models of evolution no longercapture the underlying selection for these sites. Thus, our methods for determining gene correspondence should account for duplication and loss events, and ensure that the segments we compare are orthologous.

Gene identification

Once genome correspondence is established, comparative genomics can aid gene identification. Comparative genomics can recognize real genes based on their patternsof nucleotide conservation across evolutionary time. With the availability of genome-wide alignments across the genomes compared, the different ways by which sequenceschange in known genes and in intergenic regions can be analyzed. The alignments of known genes will reveal the conservation of the reading frame of protein translation.

The genome of a species encodes genes and other functional elements, interspersed with non-functional nucleotides in a single uninterrupted string of DNA.Recognizing protein-coding genes typically relies on finding stretches of nucleotides free of stop codons (called Open Reading Frames, or ORFs) that are too long to havelikely occurred by chance. Since stop codons occur at a frequency of roughly 1 in 20 in random sequence, ORFs of at least 60 amino acids will occur frequently by chance (5%under a simple Poisson model), and even ORFs of 150 amino acids will appear by chance in a large genome (0.05%). This poses a huge challenge for higher eukaryotesin which genes are typically broken into many, small exons (on average 125 nucleotides long for internal exons) in mammals. The basic problem is distinguishing real genes –those ORFs encoding a translated protein product – from spurious ORFs – the remaining ORFs whose presence is simply due to chance. In mammalian genomes, estimates ofhypothetical genes have ranged from 28,000 to more than 120,000 genes. The internal coding exons were easily identified using Comparative analysis of human genomewith mouse genome. [4]

Regulatory motif discovery

Regulatory motifs are short DNA sequences about 6 to 15bp long that are used to control the expression of genes, dictating the conditions under which a gene will be turnedon or off. Each motif is typically recognized by a specific DNA-binding protein called a transcription factor (TF). A transcription factor binds precise sites in the promoterregion of target genes in a sequence-specific way, but this contact can tolerate some degree of sequence variation. Thus, different binding sites may contain slight variationsof the same underlying motif, and the definition of a regulatory motif should capture these variations while remaining as specific as possible. Comparative genomicsprovides a powerful way to distinguish regulatory motifs from non-functional patterns based on their conservation. One such example is the identification of TF DNA-bindingmotif [5] using comparative genomics and denovo motif. The regulatory motifs of the Human Promoters wereidentified by comparison with other mammals. [6] Yet another important finding is the gene and regulatoryelement by comparison of yeast species. [7]

Other applications

Comparative genomics has wide applications in the field of molecular medicine and molecular evolution. The most significant application of comparative genomics inmolecular medicine is the identification of drug targets of many infectious diseases. For example, comparative analyses of fungal genomes have led to the identification ofmany putative targets for novel antifungal. [8] This discovery can aid in target based drug design to cure fungaldiseases in human. Comparative analysis of genomes of individuals with genetic disease against healthy individuals may reveal clues of eliminating that disease.

Comparative genomics helps in selecting model organisms. A model system [9] is a simple, idealized system that canbe accessible and easily manipulated. For example, a comparison of the fruit fly genome with the human genome discovered that about 60 percent of genes are conservedbetween fly and human. Researchers have found that two-thirds of human genes known to be involved in cancer have counterparts in the fruit fly. Even more surprisingly, whenscientists inserted a human gene associated with early-onset Parkinson's disease into fruit flies, they displayed symptoms similar to those seen in humans with thedisorder, raising the possibility that the tiny insects could serve as a new model for testing therapies aimed at Parkinson's. Thus, comparative genomics may providegene functional annotation. Gene finding is an important application of comparative genomics. Comparative genomics identify Synteny (genes present in the same orderin the genomes) and hence reveal gene clusters.

Comparative genomics also helps in the clustering of regulatory sites [10], which can help in the recognition ofunknown regulatory regions in other genomes. The metabolic pathway regulation can also be recognized by means of comparative genomics of a species. Dmitry andcolleagues [11] have identified the regulons of methionine metabolism in gram-positive bacteria using comparativegenomics analysis. Similarly Kai Tan [12] and colleagues have identified regulatory networks of H. influenzae bycomparing its genome with that of E. coli. The adaptive properties of organisms [13] like evolution of sex, genesilencing can also be correlated to genome sequence by comparative genomics.

Citation:Sivashankari & Shanmughavel, Bioinformation 1(9): 376-378 (2007)

3. Fitch WM, et al. Philos Trans R Soc Lond B Biol Sci. 1995;349:93. [PubMed] [Google Scholar]

10. Nimwegen EV, et al. PNAS. 2002;99:7323. [Google Scholar]