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  1. BMC Genomics
  2. Volume 21
  3. Issue 9
  4. GLaMST: grow lineages along minimum spanning tree for b cell receptor sequencing data
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Volume 26
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Issue 13
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Issue 9
Analysis of two mechanisms of telomere maintenance based on the theory of g-Networks and stochastic automata networks
Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data
ComHapDet: a spatial community detection algorithm for haplotype assembly
GLaMST: grow lineages along minimum spanning tree for b cell receptor sequencing data
Selected Research Articles from the 2019 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC)
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GLaMST: Grow Lineages along Minimum Spanning Tree for B Cell Receptor Sequencing Data

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GLaMST: grow lineages along minimum spanning tree for b cell receptor sequencing data

Content Provider Springer Nature : BioMed Central
Author Yang, Xingyu Tipton, Christopher M. Woodruff, Matthew C. Zhou, Enlu Lee, F. Eun-Hyung Sanz, InĂ£ki Qiu, Peng
Abstract Background B cell affinity maturation enables B cells to generate high-affinity antibodies. This process involves somatic hypermutation of B cell immunoglobulin receptor (BCR) genes and selection by their ability to bind antigens. Lineage trees are used to describe this microevolution of B cell immunoglobulin genes. In a lineage tree, each node is one BCR sequence that mutated from the germinal center and each directed edge represents a single base mutation, insertion or deletion. In BCR sequencing data, the observed data only contains a subset of BCR sequences in this microevolution process. Therefore, reconstructing the lineage tree from experimental data requires algorithms to build the tree based on partially observed tree nodes. Results We developed a new algorithm named Grow Lineages along Minimum Spanning Tree (GLaMST), which efficiently reconstruct the lineage tree given observed BCR sequences that correspond to a subset of the tree nodes. Through comparison using simulated and real data, GLaMST outperforms existing algorithms in simulations with high rates of mutation, insertion and deletion, and generates lineage trees with smaller size and closer to ground truth according to tree features that highly correlated with selection pressure. Conclusions GLaMST outperforms state-of-art in reconstruction of the BCR lineage tree in both efficiency and accuracy. Integrating it into existing BCR sequencing analysis frameworks can significant improve lineage tree reconstruction aspect of the analysis.
Related Links https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-020-06936-w.pdf
Ending Page 11
Page Count 11
Starting Page 1
File Format HTM / HTML
ISSN 14712164
DOI 10.1186/s12864-020-06936-w
Journal BMC Genomics
Issue Number 9
Volume Number 21
Language English
Publisher BioMed Central
Publisher Date 2020-09-09
Access Restriction Open
Subject Keyword Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics B cell receptor gene Lineage tree
Content Type Text
Resource Type Article
Subject Biotechnology Genetics
Journal Impact Factor 3.5/2023
5-Year Journal Impact Factor 4.1/2023
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