Homology-independent metrics for comparative genomics

Publication date: Available online 4 May 2015 Source:Computational and Structural Biotechnology Journal Author(s): Tarcisio José Domingos Coutinho , Glória Regina Franco , Francisco Pereira Lobo A mainstream procedure to analyze the wealth of genomic data available nowadays is the detection of homologous regions shared across genomes, followed by the extraction of biological information from the patterns of conservation and variation observed in such regions. Although of pivotal importance, comparative genomics procedures that rely on homology inference are obviously not applicable if no homologous regions are detectable. This fact excludes a considerable portion of “genomic dark matter” with no significant similarity - and, consequently, no inferred homology to any other known sequence - from several downstream comparative genomics methods. In this review we compile several sequence metrics that do not rely on homology inference and can be used to compare nucleotide sequences and extract biologically meaningful information from them. These metrics comprises several compositional parameters calculated from sequence data alone, such as GC content, dinucleotide odds ratio, and several codon bias metrics. They also share other interesting properties, such as pervasiveness (patterns persist on smaller scales) and phylogenetic signal. We also cite examples where these homology-independent metrics have been successfully applied to support several bioinformatics challen...
Source: Computational and Structural Biotechnology Journal - Category: Biotechnology Source Type: research