Evidence map›Paper›PMID 21092314›Full record

ArticleBMC bioinformatics2010

iGTP: a software package for large-scale gene tree parsimony analysis.

Ruchi Chaudhary, Mukul S Bansal, André Wehe, David Fernández-Baca, Oliver Eulenstein

Abstract read
In one paragraph

Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Review
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  3. Article
  4. Phylogenetic reconciliation.PLoS computational biology · 2022
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  15. Multilocus inference of species trees and DNA barcoding.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2016
    Review
  16. Article
  17. Article
  18. Review
  19. Article
  20. Large-scale multiple sequence alignment and tree estimation using SATé.Methods in molecular biology (Clifton, N.J.) · 2014
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ruchi ChaudharyDepartment of Computer Science, Iowa State University, Ames, IA 50011, USA.
Mukul S Bansal
André Wehe
David Fernández-Baca
Oliver Eulenstein

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe ever-increasing wealth of genomic sequence information provides an unprecedented opportunity for large-scale phylogenetic analysis. However, species phylogeny inference is obfuscated by incongruence among gene trees due to evolutionary events such as gene duplication and loss, incomplete lineage sorting (deep coalescence), and horizontal gene transfer. Gene tree parsimony (GTP) addresses this issue by seeking a species tree that requires the minimum number of evolutionary events to reconcile a given set of incongruent gene trees. Despite its promise, the use of gene tree parsimony has been limited by the fact that existing software is either not fast enough to tackle large data sets or is restricted in the range of evolutionary events it can handle.

resultsWe introduce iGTP, a platform-independent software program that implements state-of-the-art algorithms that greatly speed up species tree inference under the duplication, duplication-loss, and deep coalescence reconciliation costs. iGTP significantly extends and improves the functionality and performance of existing gene tree parsimony software and offers advanced features such as building effective initial trees using stepwise leaf addition and the ability to have unrooted gene trees in the input. Moreover, iGTP provides a user-friendly graphical interface with integrated tree visualization software to facilitate analysis of the results.

conclusionsiGTP enables, for the first time, gene tree parsimony analyses of thousands of genes from hundreds of taxa using the duplication, duplication-loss, and deep coalescence reconciliation costs, all from within a convenient graphical user interface.

Indexed as

PhylogenySoftwareAlgorithmsDatabases, GeneticEvolution, MolecularGene DuplicationGenomeGenomics

Identifiers

PMID21092314
PMCPMC3002902

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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.