Evidence map›Paper›PMID 39489605›Full record

ArticleBriefings in bioinformatics2024

An unbiased comparison of immunoglobulin sequence aligners.

Thomas Konstantinovsky, Ayelet Peres, Pazit Polak, Gur Yaari

Abstract readComparative Study
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

4 authors.

Thomas KonstantinovskyFaculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel.
Ayelet PeresFaculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel.
Pazit PolakFaculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel.
Gur YaariFaculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel.

Funding

i-AKC: Integrated AIRR Knowledge CommonsU24AI177622 · NIAID · UT SOUTHWESTERN MEDICAL CENTER · PI LINDSAY G. COWELL · 2023 to 2026
$4.0M
ISF 2940/21Ministry of Innovation, Science & Tecnology 1001576181/0004941NIAID NIH HHS U24 AI177622
6 · The paper itself

Abstract

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is critical for our understanding of the adaptive immune system's dynamics in health and disease. Reliable analysis of AIRR-seq data depends on accurate rearranged immunoglobulin (Ig) sequence alignment. Various Ig sequence aligners exist, but there is no unified benchmarking standard representing the complexities of AIRR-seq data, obscuring objective comparisons of aligners across tasks. Here, we introduce GenAIRR, a modular simulation framework for generating Ig sequences alongside their ground truths. GenAIRR realistically simulates the intricacies of V(D)J recombination, somatic hypermutation, and an array of sequence corruptions. We comprehensively assessed prominent Ig sequence aligners across various metrics, unveiling unique performance characteristics for each aligner. The GenAIRR-produced datasets, combined with the proposed rigorous evaluation criteria, establish a solid basis for unbiased benchmarking of immunogenetics computational tools. It sets up the ground for further improving the crucial task of Ig sequence alignment, ultimately enhancing our understanding of adaptive immunity.

Indexed as

ImmunoglobulinsSequence AlignmentAdaptive ImmunityAlgorithmsComputational BiologyHumansSoftwareV(D)J RecombinationImmunoglobulinsAIRR-seqbenchmarkingimmunoglobulinsequence alignmentsomatic hypermutationV(D)J recombination

Identifiers

PMID39489605
PMCPMC11531861

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.