ArticleNucleic acids research2012
Efficient large-scale protein sequence comparison and gene matching to identify orthologs and co-orthologs.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 62 citations in OpenAlex.
- A framework for privacy-preserving similarity search of massive multi-party genomic data.Frontiers in plant science · 2025Article
- Graph Theory-Based Sequence Descriptors as Remote Homology Predictors.Biomolecules · 2019Article
- Alternative characterizations of Fitch's xenology relation.Journal of mathematical biology · 2019Article
- RAFTSBMC bioinformatics · 2019Article
- Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers.BMC bioinformatics · 2018Article
- OrthoGNC: A Software for Accurate Identification of Orthologs Based on Gene Neighborhood Conservation.Genomics, proteomics & bioinformatics · 2017Article
- A null model for microbial diversification.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- PATtyFams: Protein Families for the Microbial Genomes in the PATRIC Database.Frontiers in microbiology · 2016Article
- Genome-Wide Identification of Calcium Dependent Protein Kinase Gene Family in Plant Lineage Shows Presence of Novel D-x-D and D-E-L Motifs in EF-Hand Domain.Frontiers in plant science · 2015Article
- Article
- A geometric interpretation for local alignment-free sequence comparison.Journal of computational biology : a journal of computational molecular cell biology · 2013Article
- Functional and evolutionary implications of gene orthology.Nature reviews. Genetics · 2013Review
- PhyloTreePruner: A Phylogenetic Tree-Based Approach for Selection of Orthologous Sequences for Phylogenomics.Evolutionary bioinformatics online · 2013Article
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Authors and funding
5 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Broadly, computational approaches for ortholog assignment is a three steps process: (i) identify all putative homologs between the genomes, (ii) identify gene anchors and (iii) link anchors to identify best gene matches given their order and context. In this article, we engineer two methods to improve two important aspects of this pipeline [specifically steps (ii) and (iii)]. First, computing sequence similarity data [step (i)] is a computationally intensive task for large sequence sets, creating a bottleneck in the ortholog assignment pipeline. We have designed a fast and highly scalable sort-join method (afree) based on k-mer counts to rapidly compare all pairs of sequences in a large protein sequence set to identify putative homologs. Second, availability of complex genomes containing large gene families with prevalence of complex evolutionary events, such as duplications, has made the task of assigning orthologs and co-orthologs difficult. Here, we have developed an iterative graph matching strategy where at each iteration the best gene assignments are identified resulting in a set of orthologs and co-orthologs. We find that the afree algorithm is faster than existing methods and maintains high accuracy in identifying similar genes. The iterative graph matching strategy also showed high accuracy in identifying complex gene relationships. Standalone afree available from http://vbc.med.monash.edu.au/∼kmahmood/afree. EGM2, complete ortholog assignment pipeline (including afree and the iterative graph matching method) available from http://vbc.med.monash.edu.au/∼kmahmood/EGM2.
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