OMA standalone: orthology inference among public and custom genomes and transcriptomes.
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ABSTRACT:
SUBMITTER: Altenhoff AM
PROVIDER: S-EPMC6633268 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Orthology inference is central to phylogenomic analyses. Phylogenomic data sets commonly include transcriptomes and low-coverage genomes that are incomplete and contain errors and isoforms. These properties can severely violate the underlying assumptions of orthology inference with existing heuristics. We present a procedure that uses phylogenies for both homology and orthology assignment. The procedure first uses similarity scores to infer putative homologs that are then aligned, constructed into phylogenies, and pruned of spurious branches caused by deep paralogs, misassembly, frameshifts, or recombination. These final homologs are then used to identify orthologs. We explore four alternative tree-based orthology inference approaches, of which two are new. These accommodate gene and genome duplications as well as gene tree discordance. We demonstrate these methods in three published data sets including the grape family, Hymenoptera, and millipedes with divergence times ranging from approximately 100 to over 400 Ma. The procedure significantly increased the completeness and accuracy of the inferred homologs and orthologs. We also found that data sets that are more recently diverged and/or include more high-coverage genomes had more complete sets of orthologs. To explicitly evaluate sources of conflicting phylogenetic signals, we applied serial jackknife analyses of gene regions keeping each locus intact. The methods described here can scale to over 100 taxa. They have been implemented in python with independent scripts for each step, making it easy to modify or incorporate them into existing pipelines. All scripts are available from https://bitbucket.org/yangya/phylogenomic_dataset_construction.
| S-EPMC4209138 | biostudies-literature
Project description:BackgroundOrthology inference is normally based on full-length protein sequences. However, most proteins contain independently folding and recurring regions, domains. The domain architecture of a protein is vital for its function, and recombination events mean individual domains can have different evolutionary histories. It has previously been shown that orthologous proteins may differ in domain architecture, creating challenges for orthology inference methods operating on full-length sequences. We have developed Domainoid, a new tool aiming to overcome these challenges faced by full-length orthology methods by inferring orthology on the domain level. It employs the InParanoid algorithm on single domains separately, to infer groups of orthologous domains.ResultsThis domain-oriented approach allows detection of discordant domain orthologs, cases where different domains on the same protein have different evolutionary histories. In addition to domain level analysis, protein level orthology based on the fraction of domains that are orthologous can be inferred. Domainoid orthology assignments were compared to those yielded by the conventional full-length approach InParanoid, and were validated in a standard benchmark.ConclusionsOur results show that domain-based orthology inference can reveal many orthologous relationships that are not found by full-length sequence approaches.Availabilityhttps://bitbucket.org/sonnhammergroup/domainoid/.
| S-EPMC6816169 | biostudies-literature
Project description:Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder's high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder's comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.
| S-EPMC6857279 | biostudies-literature
Project description:MotivationOrthology inference constitutes a common base of many genome-based studies, as a pre-requisite for annotating new genomes, finding target genes for biotechnological applications and revealing the evolutionary history of life. Although its importance keeps rising with the ever-growing number of sequenced genomes, existing tools are computationally demanding and difficult to employ.ResultsHere, we present SonicParanoid, which is faster than, but comparably accurate to, the well-established tools with a balanced precision-recall trade-off. Furthermore, SonicParanoid substantially relieves the difficulties of orthology inference for those who need to construct and maintain their own genomic datasets.Availability and implementationSonicParanoid is available with a GNU GPLv3 license on the Python Package Index and BitBucket. Documentation is available at http://iwasakilab.bs.s.u-tokyo.ac.jp/sonicparanoid.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6298048 | biostudies-literature
Project description:The Orthologous Matrix (OMA) project is a method and associated database inferring evolutionary relationships amongst currently 1706 complete proteomes (i.e. the protein sequence associated for every protein-coding gene in all genomes). In this update article, we present six major new developments in OMA: (i) a new web interface; (ii) Gene Ontology function predictions as part of the OMA pipeline; (iii) better support for plant genomes and in particular homeologs in the wheat genome; (iv) a new synteny viewer providing the genomic context of orthologs; (v) statically computed hierarchical orthologous groups subsets downloadable in OrthoXML format; and (vi) possibility to export parts of the all-against-all computations and to combine them with custom data for 'client-side' orthology prediction. OMA can be accessed through the OMA Browser and various programmatic interfaces at http://omabrowser.org.
| S-EPMC4383958 | biostudies-literature
Project description:BackgroundRecently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space.ResultsIn this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into [Formula: see text] subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to [Formula: see text] families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with [Formula: see text], when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the [Formula: see text] algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with [Formula: see text]. Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc .
| S-EPMC10540461 | biostudies-literature
Project description:Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, and handles even highly fragmented assemblies. TOGA scales to hundreds of genomes, which we demonstrate by applying it to 488 placental mammal and 501 bird assemblies, creating the largest comparative gene resources so far. Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality. TOGA is a powerful and scalable method to annotate and compare genes in the genomic era.
| S-EPMC10193443 | biostudies-literature
Project description:BackgroundIn the past, rice genome served as a good model for studies involving comparative genomics of grass species. More recently, however, Brachypodium distachyon genome has emerged as a better model system for genomes of temperate cereals including wheat. During the present study, Brachypodium EST contigs were utilized to resolve orthologous relationships among the genomes of Brachypodium, wheat and rice.FindingsComparative sequence analysis of 3,818 Brachypodium EST (bEST) contigs and 3,792 physically mapped wheat EST (wEST) contigs revealed that as many as 449 bEST contigs were orthologous to 1,154 wEST loci that were bin-mapped on all the 21 wheat chromosomes. Similarly 743 bEST contigs were orthologous to specific rice genome sequences distributed on all the 12 rice chromosomes. As many as 183 bEST contigs were orthologous to both wheat and rice genome sequences, which harbored as many as 17 SSRs conserved across the three species. Primers developed for 12 of these 17 conserved SSRs were used for a wet-lab experiment, which resolved relatively high level of conservation among the genomes of Brachypodium, wheat and rice.ConclusionThe present study confirmed that Brachypodium is a better model than rice for analysis of the genomes of temperate cereals like wheat and barley. The whole genome sequence of Brachypodium, which should become available in the near future, will further facilitate greatly the studies involving comparative genomics of cereals.
| S-EPMC2695472 | biostudies-literature
Project description:The Orthologous Matrix (OMA) is a leading resource to relate genes across many species from all of life. In this update paper, we review the recent algorithmic improvements in the OMA pipeline, describe increases in species coverage (particularly in plants and early-branching eukaryotes) and introduce several new features in the OMA web browser. Notable improvements include: (i) a scalable, interactive viewer for hierarchical orthologous groups; (ii) protein domain annotations and domain-based links between orthologous groups; (iii) functionality to retrieve phylogenetic marker genes for a subset of species of interest; (iv) a new synteny dot plot viewer; and (v) an overhaul of the programmatic access (REST API and semantic web), which will facilitate incorporation of OMA analyses in computational pipelines and integration with other bioinformatic resources. OMA can be freely accessed at https://omabrowser.org.
| S-EPMC5753216 | biostudies-literature
Project description:BackgroundBacterial genomes are being deposited into online databases at an increasing rate. Genome annotation represents one of the first efforts to understand organisms and their diseases. Some evolutionary relationships capable of being annotated only from genomes are conserved gene neighbourhoods (CNs), phylogenetic profiles (PPs), and gene fusions. At present, there is no standalone software that enables networks of interactions among proteins to be created using these three evolutionary characteristics with efficient and effective results.ResultsWe developed GENPPI software for the ab initio prediction of interaction networks using predicted proteins from a genome. In our case study, we employed 50 genomes of the genus Corynebacterium. Based on the PP relationship, GENPPI differentiated genomes between the ovis and equi biovars of the species Corynebacterium pseudotuberculosis and created groups among the other species analysed. If we inspected only the CN relationship, we could not entirely separate biovars, only species. Our software GENPPI was determined to be efficient because, for example, it creates interaction networks from the central genomes of 50 species/lineages with an average size of 2200 genes in less than 40 min on a conventional computer. Moreover, the interaction networks that our software creates reflect correct evolutionary relationships between species, which we confirmed with average nucleotide identity analyses. Additionally, this software enables the user to define how he or she intends to explore the PP and CN characteristics through various parameters, enabling the creation of customized interaction networks. For instance, users can set parameters regarding the genus, metagenome, or pangenome. In addition to the parameterization of GENPPI, it is also the user's choice regarding which set of genomes they are going to study.ConclusionsGENPPI can help fill the gap concerning the considerable number of novel genomes assembled monthly and our ability to process interaction networks considering the noncore genes for all completed genome versions. With GENPPI, a user dictates how many and how evolutionarily correlated the genomes answer a scientific query.
| S-EPMC8680239 | biostudies-literature