Comparative genomics approach to detecting split-coding regions in a low-coverage genome: lessons from the chimaera Callorhinchus milii (Holocephali, Chondrichthyes)

Abstract

Recent development of deep sequencing technologies has facilitated de novo genome sequencing projects, now conducted even by individual laboratories. However, this will yield more and more genome sequences that are not well assembled, and will hinder thorough annotation when no closely related reference genome is available. One of the challenging issues is the identification of protein-coding sequences split into multiple unassembled genomic segments, which can confound orthology assignment and various laboratory experiments requiring the identification of individual genes. In this study, using the genome of a cartilaginous fish, Callorhinchus milii, as test case, we performed gene prediction using a model specifically trained for this genome. We implemented an algorithm, designated ESPRIT, to identify possible linkages between multiple protein-coding portions derived from a single genomic locus split into multiple unassembled genomic segments. We developed a validation framework based on an artificially fragmented human genome, improvements between early and recent mouse genome assemblies, comparison with experimentally validated sequences from GenBank, and phylogenetic analyses. Our strategy provided insights into practical solutions for efficient annotation of only partially sequenced (low-coverage) genomes. To our knowledge, our study is the first formulation of a method to link unassembled genomic segments based on proteomes of relatively distantly related species as references.

Inferring Hierarchical Orthologous Groups from Orthologous Gene Pairs

“…We reanalysed the three gene families from a recent manually curated study by Boeckmann et al [9]. As input for our algorithm, we used the pairwise orthologs from the OMA May 2010 release as orthology graph and the NCBI taxonomy [26] as the species tree.…”

“…To construct the orthology graph, we used pairwise orthologs inferred by the OMA algorithm [7], which has been shown to be competitive in benchmarking studies [8], [9], [27], [28]. In brief, the OMA algorithm first computes all-against-all sequence alignments using full dynamic programming.…”

“…Following Boeckmann et al [9], we measured the performance of a method in terms of the precision and recall of pairwise orthology or paralogy. Precision and recall are defined as and , where is the number of true positive reported relations, the number of spuriously reported relations and the number of missing predictions.…”

“…x being orthologous to y and y being orthologous to z does not imply x being orthologous to z), the generalisation of these concepts to sets of genes is not straightforward. As a consequence, several definitions of orthologous groups have been proposed, with considerable differences in terms of evolutionary relations implied [9].…”

“…This definition has several interesting implications: (i) defining groups in terms of specific taxonomic ranges enables users to fine-tune their analyses to different contexts of investigation–for instance, studying “ubiquitous” genes among all species, but studying lactation genes in terms of the last mammalian common ancestor only; (ii) hierarchical groups have a straightforward interpretation in terms of gene trees: they are clades on these trees; (iii) collectively, hierarchical groups defined with respect to every ancestral species capture all orthologous and paralogous relations [9]. Hierarchical groups are at the heart of the orthology databases EggNOG [13] and OrthoDB [14].…”