Evidence map›Paper›PMID 40674579›Full record

ArticleBioinformatics (Oxford, England)2025

AffMB: affinity maturation analysis with SHM-guided B-cell lineage trees.

Jiaqi Luo, Yiping Zou, Shuai Cheng Li

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Jiaqi LuoDepartment of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China.ORCID 0000-0001-5453-6431
Yiping ZouDepartment of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China.ORCID 0000-0001-7522-6088
Shuai Cheng LiDepartment of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China.ORCID 0000-0001-6246-6349

Funding

Shenzhen Science and Technology Program JCYJ20200109143216036
6 · The paper itself

Abstract

motivationB-cell lineage trees describe the evolutionary process of immunoglobulin genes during affinity maturation. Existing methods for building B-cell lineage trees generally do not guarantee the parent-to-child inheritance and accumulation of advantageous mutations under successive rounds of somatic hypermutation (SHM) and selection, and are often incompatible with repertoire input.

resultsTo address previous limitations, we developed AffMB (Affinity Maturation of B-cell receptor), a comprehensive toolkit for tracking affinity maturation through the generation and visualization of SHM-ordered, inheritance-based B-cell lineage trees from single-cell or bulk B-cell receptor sequencing data. The SHM-ordered inheritance tree algorithm outperformed state-of-the-art benchmarks in simulations. When applied to single-cell data from BNT162b2 vaccination (n = 42), AffMB demonstrated the ability to infer immunization responses and showed the feasibility of identifying potential high-affinity antibody sequences. AVAILABILITY AND IMPLEMENTATION: AffMB is an open-source Python package that supports contig FASTA or AIRR rearrangement TSV inputs. The source code for AffMB is freely available at https://github.com/deepomicslab/AffMB.

Indexed as

Antibody AffinityB-LymphocytesCell LineageReceptors, Antigen, B-CellSoftwareSomatic Hypermutation, ImmunoglobulinAlgorithmsHumansReceptors, Antigen, B-Cell

Identifiers

PMID40674579
PMCPMC12282942

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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.