Evidence map›Paper›PMID 24885064›Full record

ArticleBMC bioinformatics2014

Improvement of domain-level ortholog clustering by optimizing domain-specific sum-of-pairs score.

Hirokazu Chiba, Ikuo Uchiyama

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Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

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8citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Towards molecular evolutionary epigenomics with an expanded nucleotide code involving methylated bases.DNA research : an international journal for rapid publication of reports on genes and genomes · 2025
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5 · Who and what money

Authors and funding

2 authors.

Hirokazu Chiba
Ikuo UchiyamaNational Institute for Basic Biology, National Institutes of Natural Sciences, Nishigonaka 38, Myodaiji, Okazaki 444-8585, Japan. uchiyama@nibb.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentification of ortholog groups is a crucial step in comparative analysis of multiple genomes. Although several computational methods have been developed to create ortholog groups, most of those methods do not evaluate orthology at the sub-gene level. In our method for domain-level ortholog clustering, DomClust, proteins are split into domains on the basis of alignment boundaries identified by all-against-all pairwise comparison, but it often fails to determine appropriate boundaries.

resultsWe developed a method to improve domain-level ortholog classification using multiple alignment information. This method is based on a scoring scheme, the domain-specific sum-of-pairs (DSP) score, which evaluates ortholog clustering results at the domain level as the sum total of domain-level alignment scores. We developed a refinement pipeline to improve domain-level clustering, DomRefine, by optimizing the DSP score. We applied DomRefine to domain-level ortholog groups created by DomClust using a dataset obtained from the Microbial Genome Database for Comparative Analysis (MBGD), and evaluated the results using COG clusters and TIGRFAMs models as the reference data. Thus, we observed that the agreement between the resulting classification and the classifications in the reference databases is improved at almost every step in the refinement pipeline. Moreover, the refined classification showed better agreement than the classifications in the eggNOG databases when TIGRFAMs was used as the reference database.

conclusionsDomRefine is a useful tool for improving the quality of domain-level ortholog classification among microbial genomes. Combining with a rapid domain-level ortholog clustering method, such as DomClust, it can be used to create a high-quality ortholog database that can serve as a solid basis for various comparative genome analyses.

Indexed as

Protein Structure, TertiarySequence Analysis, ProteinCluster AnalysisGenomicsPhylogenySequence AlignmentSoftware

Identifiers

PMID24885064
PMCPMC4035852

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.