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Inferring hierarchical orthologous groups from orthologous gene pairs.

ABSTRACT: Hierarchical orthologous groups are defined as sets of genes that have descended from a single common ancestor within a taxonomic range of interest. Identifying such groups is useful in a wide range of contexts, including inference of gene function, study of gene evolution dynamics and comparative genomics. Hierarchical orthologous groups can be derived from reconciled gene/species trees but, this being a computationally costly procedure, many phylogenomic databases work on the basis of pairwise gene comparisons instead ("graph-based" approach). To our knowledge, there is only one published algorithm for graph-based hierarchical group inference, but both its theoretical justification and performance in practice are as of yet largely uncharacterised. We establish a formal correspondence between the orthology graph and hierarchical orthologous groups. Based on that, we devise GETHOGs ("Graph-based Efficient Technique for Hierarchical Orthologous Groups"), a novel algorithm to infer hierarchical groups directly from the orthology graph, thus without needing gene tree inference nor gene/species tree reconciliation. GETHOGs is shown to correctly reconstruct hierarchical orthologous groups when applied to perfect input, and several extensions with stringency parameters are provided to deal with imperfect input data. We demonstrate its competitiveness using both simulated and empirical data. GETHOGs is implemented as a part of the freely-available OMA standalone package (http://omabrowser.org/standalone). Furthermore, hierarchical groups inferred by GETHOGs ("OMA HOGs") on >1,000 genomes can be interactively queried via the OMA browser (http://omabrowser.org).

SUBMITTER: Altenhoff AM

PROVIDER: S-EPMC3544860 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Inferring hierarchical orthologous groups from orthologous gene pairs.

Altenhoff Adrian M AM Gil Manuel M Gonnet Gaston H GH Dessimoz Christophe C

PloS one 20130114 1

Hierarchical orthologous groups are defined as sets of genes that have descended from a single common ancestor within a taxonomic range of interest. Identifying such groups is useful in a wide range of contexts, including inference of gene function, study of gene evolution dynamics and comparative genomics. Hierarchical orthologous groups can be derived from reconciled gene/species trees but, this being a computationally costly procedure, many phylogenomic databases work on the basis of pairwise ...[more]

PMID: 23342000

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Project description:With the inclusion of new members, understanding about evolutionary mechanisms and processes by which members of the proposed order, Megavirales, have evolved has become a key area of interest. The central role of gene acquisition has been shown in previous studies. However, the major drawback in gene acquisition studies is the focus on few MV families or putative families with large variation in their genetic structure. Thus, here we have tried to develop a methodology by which we can detect horizontal gene transfers (HGTs), taking into consideration orthologous groups of distantly related Megavirale families. Here, we report an automated workflow MimiLook, prepared as a Perl command line program, that deduces orthologous groups (OGs) from ORFomes of Megavirales and constructs phylogenetic trees by performing alignment generation, alignment editing and protein-protein BLAST (BLASTP) searching across the National Center for Biotechnology Information (NCBI) non-redundant (nr) protein sequence database. Finally, this tool detects statistically validated events of gene acquisitions with the help of the T-REX algorithm by comparing individual gene tree with NCBI species tree. In between the steps, the workflow decides about handling paralogs, filtering outputs, identifying Megavirale specific OGs, detection of HGTs, along with retrieval of information about those OGs that are monophyletic with organisms from cellular domains of life. By implementing MimiLook, we noticed that nine percent of Megavirale gene families (i.e., OGs) have been acquired by HGT, 80% OGs were Megaviralespecific and eight percent were found to be sharing common ancestry with members of cellular domains (Eukaryote, Bacteria, Archaea, Phages or other viruses) and three percent were ambivalent. The results are briefly discussed to emphasize methodology. Also, MimiLook is relevant for detecting evolutionary scenarios in other targeted phyla with user defined modifications. It can be accessed at following link 10.6084/m9.figshare.4653622.

Dataset Information

Inferring hierarchical orthologous groups from orthologous gene pairs.

Publications

Inferring hierarchical orthologous groups from orthologous gene pairs.

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