Evidence map›Paper›PMID 21712981›Full record

ArticlePloS one2011

MultiMSOAR 2.0: an accurate tool to identify ortholog groups among multiple genomes.

Guanqun Shi, Meng-Chih Peng, Tao Jiang

Abstract read
In one paragraph

Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
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  4. Article
  5. Article
  6. Xenolog classification.Bioinformatics (Oxford, England) · 2017
    Article
  7. The gene family-free median of three.Algorithms for molecular biology : AMB · 2017
    Article
  8. Article
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  11. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Guanqun ShiDepartment of Computer Science, University of California Riverside, Riverside, California, United States of America. gshi@cs.ucr.edu
Meng-Chih Peng
Tao Jiang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The identification of orthologous genes shared by multiple genomes plays an important role in evolutionary studies and gene functional analyses. Based on a recently developed accurate tool, called MSOAR 2.0, for ortholog assignment between a pair of closely related genomes based on genome rearrangement, we present a new system MultiMSOAR 2.0, to identify ortholog groups among multiple genomes in this paper. In the system, we construct gene families for all the genomes using sequence similarity search and clustering, run MSOAR 2.0 for all pairs of genomes to obtain the pairwise orthology relationship, and partition each gene family into a set of disjoint sets of orthologous genes (called super ortholog groups or SOGs) such that each SOG contains at most one gene from each genome. For each such SOG, we label the leaves of the species tree using 1 or 0 to indicate if the SOG contains a gene from the corresponding species or not. The resulting tree is called a tree of ortholog groups (or TOGs). We then label the internal nodes of each TOG based on the parsimony principle and some biological constraints. Ortholog groups are finally identified from each fully labeled TOG. In comparison with a popular tool MultiParanoid on simulated data, MultiMSOAR 2.0 shows significantly higher prediction accuracy. It also outperforms MultiParanoid, the Roundup multi-ortholog repository and the Ensembl ortholog database in real data experiments using gene symbols as a validation tool. In addition to ortholog group identification, MultiMSOAR 2.0 also provides information about gene births, duplications and losses in evolution, which may be of independent biological interest. Our experiments on simulated data demonstrate that MultiMSOAR 2.0 is able to infer these evolutionary events much more accurately than a well-known software tool Notung. The software MultiMSOAR 2.0 is available to the public for free.

Indexed as

AlgorithmsEvolution, MolecularGenomeSoftwareAnimalsComputational BiologyDatabases, FactualGenomicsHumansPhylogenySequence AlignmentSequence Analysis, DNASequence Homology, Nucleic Acid

Identifiers

PMID21712981
PMCPMC3119667

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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.