Orthograph: a versatile tool for mapping coding nucleotide sequences to clusters of orthologous genes

Abstract

BackgroundOrthology characterizes genes of different organisms that arose from a single ancestral gene via speciation, in contrast to paralogy, which is assigned to genes that arose via gene duplication. An accurate orthology assignment is a crucial step for comparative genomic studies. Orthologous genes in two organisms can be identified by applying a so-called reciprocal search strategy, given that complete information of the organisms’ gene repertoire is available. In many investigations, however, only a fraction of the gene content of the organisms under study is examined (e.g., RNA sequencing). Here, identification of orthologous nucleotide or amino acid sequences can be achieved using a graph-based approach that maps nucleotide sequences to genes of known orthology. Existing implementations of this approach, however, suffer from algorithmic issues that may cause problems in downstream analyses.ResultsWe present a new software pipeline, Orthograph, that addresses and solves the above problems and implements useful features for a wide range of comparative genomic and transcriptomic analyses. Orthograph applies a best reciprocal hit search strategy using profile hidden Markov models and maps nucleotide sequences to the globally best matching cluster of orthologous genes, thus enabling researchers to conveniently and reliably delineate orthologs and paralogs from transcriptomic and genomic sequence data. We demonstrate the performance of our approach on de novo-sequenced and assembled transcript libraries of 24 species of apoid wasps (Hymenoptera: Aculeata) as well as on published genomic datasets.ConclusionWith Orthograph, we implemented a best reciprocal hit approach to reference-based orthology prediction for coding nucleotide sequences such as RNAseq data. Orthograph is flexible, easy to use, open source and freely available at https://mptrsen.github.io/Orthograph. Additionally, we release 24 de novo-sequenced and assembled transcript libraries of apoid wasp species.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1529-8) contains supplementary material, which is available to authorized users.

Phylogenomic analysis resolves the relationships among net‐winged beetles (Coleoptera: Lycidae) and reveals the parallel evolution of morphological traits

“…Settings were as follows: ‐long option, the conserved genes in the Endopterygota set ( n = 2442), and ‐sp tribolium2012 as the closest relative. The predicted species‐specific gene models were then used for ab initio gene predictions in augustus , and predicted protein coding sequences were used in Orthograph v. 0.6.1 (Petersen et al , ). Outgroup data were assembled as described in previous studies (Kusy et al , ,b).…”

Anchored hybrid enrichment provides new insights into the phylogeny and evolution of longhorned beetles (Cerambycidae)

“…Each of the 522 genes was then assessed for orthology in Orthograph using a reciprocal blast search, and this was done for each taxon-based result from the Orthograph pipeline. Results were stored in both AA and nucleotide (NT) format for each taxon-based result (following Petersen et al, 2017). The resulting fasta formatted NT files for each species were screened for vector contamination using UniVec (Cochrane & Galperin, 2010).…”

“…As a result, we employed a custom bioinformatics pipeline to process these files. First, headers of all files were modified using orthograph2hamstrad.pl (Perl script provided from the Orthograph package; Petersen et al, 2017). Second, reference genes (OGSs) are removed for each file, such that each of them contained only one target sequence for each gene and a clear taxon name (or taxon code) that includes an OrthoDB7 ID.…”

“…Second, reference genes (OGSs) are removed for each file, such that each of them contained only one target sequence for each gene and a clear taxon name (or taxon code) that includes an OrthoDB7 ID. Next, fasta files for each taxon were combined using Perl script summarize_orthograph_results.pl (from Orthograph package; Petersen et al, 2017). Genes were then aligned in mafft v7 (Katoh & Standley, 2013) with L-ins-i and default options.…”

“…Orthologous genes were identified using Orthograph (Petersen et al, 2017), a protein-based orthology search pipeline. It removes possible paralogous genes using Hidden Markov Model (HMM)-based orthology searches of protein-translated sequences.…”