ArticleNucleic acids research2011
Integration of sequence-similarity and functional association information can overcome intrinsic problems in orthology mapping across bacterial genomes.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- The Transcriptomic Response of Cells of the Thermophilic BacteriumInternational journal of molecular sciences · 2024Article
- RECTA: Regulon Identification Based on Comparative Genomics and Transcriptomics Analysis.Genes · 2018Article
- An integrative and applicable phylogenetic footprinting framework for cis-regulatory motifs identification in prokaryotic genomes.BMC genomics · 2016Article
- Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses.Scientific reports · 2016Article
- Revisiting operons: an analysis of the landscape of transcriptional units in E. coli.BMC bioinformatics · 2015Article
- DOOR 2.0: presenting operons and their functions through dynamic and integrated views.Nucleic acids research · 2014Article
- Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Existing methods for orthologous gene mapping suffer from two general problems: (i) they are computationally too slow and their results are difficult to interpret for automated large-scale applications when based on phylogenetic analyses; or (ii) they are too prone to making mistakes in dealing with complex situations involving horizontal gene transfers and gene fusion due to the lack of a sound basis when based on sequence similarity information. We present a novel algorithm, Global Optimization Strategy (GOST), for orthologous gene mapping through combining sequence similarity and contextual (working partners) information, using a combinatorial optimization framework. Genome-scale applications of GOST show substantial improvements over the predictions by three popular sequence similarity-based orthology mapping programs. Our analysis indicates that our algorithm overcomes the intrinsic issues faced by sequence similarity-based methods, when orthology mapping involves gene fusions and horizontal gene transfers. Our program runs as efficiently as the most efficient sequence similarity-based algorithm in the public domain. GOST is freely downloadable at http://csbl.bmb.uga.edu/~maqin/GOST.
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