Evidence map›Paper›PMID 23793106›Full record

ArticleImmunogenetics2013

An automated algorithm for extracting functional immunologic V-genes from genomes in jawed vertebrates.

David Olivieri, Jose Faro, Bernardo von Haeften, Christian Sánchez-Espinel, Francisco Gambón-Deza

Abstract read
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In one paragraph

Article in Immunogenetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

15 citing papers in PubMed.

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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.

David OlivieriSchool of Computer Engineering, University of Vigo, 32004 Ourense, Spain. dnolivieri@gmail.com
Jose Faro
Bernardo von Haeften
Christian Sánchez-Espinel
Francisco Gambón-Deza

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Variable (V) domains of immunoglobulins (Ig) and T cell receptors (TCR) are generated from genomic V gene segments (V-genes). At present, such V-genes have been annotated only within the genome of a few species. We have developed a bioinformatics tool that accelerates the task of identifying functional V-genes from genome datasets. Automated recognition is accomplished by recognizing key V-gene signatures, such as recombination signal sequences, size of the exon region, and position of amino acid motifs within the translated exon. This algorithm also classifies extracted V-genes into either TCR or Ig loci. We describe the implementation of the algorithm and validate its accuracy by comparing V-genes identified from the human and mouse genomes with known V-gene annotations documented and available in public repositories. The advantages and utility of the algorithm are illustrated by using it to identify functional V-genes in the rat genome, where V-gene annotation is still incomplete. This allowed us to perform a comparative human-rodent phylogenetic analysis based on V-genes that supports the hypothesis that distinct evolutionary pressures shape the TCRs and Igs V-gene repertoires. Our program, together with a user graphical interface, is available as open-source software, downloadable at http://code.google.com/p/vgenextract/.

Indexed as

AlgorithmsAmino Acid SequenceAnimalsComputational BiologyGenes, ImmunoglobulinGenetic VariationGenomeHumansImmunoglobulin Variable RegionMicePhylogenyRatsReceptors, Antigen, T-CellSoftwareImmunoglobulin Variable RegionReceptors, Antigen, T-Cell

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Registered trials

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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.