Evidence map›Paper›PMID 21665923›Full record

ArticleNucleic acids research2011

Detecting selection in immunoglobulin sequences.

Mohamed Uduman, Gur Yaari, Uri Hershberg, Jacob A Stern, Mark J Shlomchik, Steven H Kleinstein

Abstract read
In one paragraph

Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.

0numbers the graph read from it
0cells of the map it votes in
52citing 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

52 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

6 authors.

Mohamed UdumanInterdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Gur Yaari
Uri Hershberg
Jacob A Stern
Mark J Shlomchik
Steven H Kleinstein

Funding

Computational tools for analysis of B cell somatic hypermutationR03AI092379 · NIAID · YALE UNIVERSITY · PI KLEINSTEIN, STEVEN H. · 2011 to 2012
$166k
NIAID NIH HHS R03 AI092379NIAID NIH HHS R03AI092379-01
6 · The paper itself

Abstract

The ability to detect selection by analyzing mutation patterns in experimentally derived immunoglobulin (Ig) sequences is a critical part of many studies. Such techniques are useful not only for understanding the response to pathogens, but also to determine the role of antigen-driven selection in autoimmunity, B cell cancers and the diversification of pre-immune repertoires in certain species. Despite its importance, quantifying selection in experimentally derived sequences is fraught with difficulties. The necessary parameters for statistical tests (such as the expected frequency of replacement mutations in the absence of selection) are non-trivial to calculate, and results are not easily interpretable when analyzing more than a handful of sequences. We have developed a web server that implements our previously proposed Focused binomial test for detecting selection. Several features are integrated into the web site in order to facilitate analysis, including V(D)J germline segment identification with IMGT alignment, batch submission of sequences and integration of additional test statistics proposed by other groups. We also implement a Z-score-based statistic that increases the power of detecting selection while maintaining specificity, and further allows for the combined analysis of sequences from different germlines. The tool is freely available at http://clip.med.yale.edu/selection.

Indexed as

DNA Mutational AnalysisSoftwareSomatic Hypermutation, ImmunoglobulinAntibody AffinityGenes, ImmunoglobulinInternet

Identifiers

PMID21665923
PMCPMC3125793

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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.