Evidence map›Paper›PMID 20147303›Full record

ArticleBioinformatics (Oxford, England)2010

SoDA2: a Hidden Markov Model approach for identification of immunoglobulin rearrangements.

Supriya Munshaw, Thomas B Kepler

Open access · hybridAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
4.2field-weighted citation impact, top 6% of its field
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

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

46 citing papers in PubMed, 75 citations in OpenAlex.

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  8. Deep generative selection models of T and B cell receptor repertoires with soNNia.Proceedings of the National Academy of Sciences of the United States of America · 2021
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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

2 authors at 1 institution in 1 country.

Supriya MunshawCenter for Computational Immunology, Computational Biology and Bioinformatics Program, Duke University, Durham, NC 27705, USA.
Thomas B Kepler
Duke University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationThe inference of pre-mutation immunoglobulin (Ig) rearrangements is essential in the study of the antibody repertoires produced in response to infection, in B-cell neoplasms and in autoimmune disease. Often, there are several rearrangements that are nearly equivalent as candidates for a given Ig gene, but have different consequences in an analysis. Our aim in this article is to develop a probabilistic model of the rearrangement process and a Bayesian method for estimating posterior probabilities for the comparison of multiple plausible rearrangements.

resultsWe have developed SoDA2, which is based on a Hidden Markov Model and used to compute the posterior probabilities of candidate rearrangements and to find those with the highest values among them. We validated the software on a set of simulated data, a set of clonally related sequences, and a group of randomly selected Ig heavy chains from Genbank. In most tests, SoDA2 performed better than other available software for the task. Furthermore, the output format has been redesigned, in part, to facilitate comparison of multiple solutions. AVAILABILITY: SoDA2 is available online at https://hippocrates.duhs.duke.edu/soda. Simulated sequences are available upon request.

Indexed as

Gene Rearrangement, B-LymphocyteGenes, ImmunoglobulinMarkov ChainsSoftwareAmino Acid SequenceBase SequenceB-LymphocytesImmunoglobulinsMolecular Sequence DataSequence AlignmentImmunoglobulins

Identifiers

PMID20147303
PMCPMC2844993
OpenAlexW2114937727

What OpenQuestion holds

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LicenceCC BY-NC
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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.