title: Homology-independent metrics for comparative genomics.
abstract: 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 genomic 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 genomic 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 comprise 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 challenges, such as taxonomic classification of biological sequences without homology inference. They where also used to detect higher-order patterns of interactions in biological systems, ranging from detecting coevolutionary trends between the genomes of viruses and their hosts to characterization of gene pools of entire microbial communities. We argue that, if correctly understood and applied, homology-independent metrics can add important layers of biological information in comparative genomic studies without prior homology inference.
authors.affiliation: Departamento de Bioquímica e Imunologia, Programa de pós-graduação em Bioinformática, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Antonio Carlos Avenue, 6627, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil.
authors.forename: Tarcisio José Domingos
authors.initials: TJ
authors.lastname: Coutinho
authors.affiliation: Departamento de Bioquímica e Imunologia, Programa de pós-graduação em Bioinformática, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Antonio Carlos Avenue, 6627, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil.
authors.forename: Glória Regina
authors.initials: GR
authors.lastname: Franco
authors.affiliation: Laboratório Multiusuário de Bioinformática, Embrapa Informática Agropecuária, André Tosello Avenue, 209, Barão Geraldo, Campinas, São Paulo CEP 13083-886, Brazil.
authors.forename: Francisco Pereira
authors.initials: FP
authors.lastname: Lobo
keywords: Comparative genomics; DOR, dinucleotide odds ratio; GC3S, frequency of G + C at the third position of synonymous codons; Genomic signatures; HD, homology-dependent; HI, homology-independent; Homology-independent metrics; NC, effective number of codons; NC′, effective number of codons considering GC3S; ORF, open reading frame; RSCU, relative synonymous codon usage