ArticleMicrobial genomics2015
K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets.
Article in Microbial genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Genomics Analysis to Identify Multiple Genetic Determinants That Drive the Global Transmission of the Pandemic ST95 Lineage of Extraintestinal PathogenicPathogens (Basel, Switzerland) · 2022Article
- Contrasting selective patterns across the segmented genome of bluetongue virus in a global reassortment hotspot.Virus evolution · 2019Article
- Kpax3: Bayesian bi-clustering of large sequence datasets.Bioinformatics (Oxford, England) · 2018Article
- Acquisition and dissemination of cephalosporin-resistant E. coli in migratory birds sampled at an Alaska landfill as inferred through genomic analysis.Scientific reports · 2018Article
- Convergent Amino Acid Signatures in Polyphyletic Campylobacter jejuni Subpopulations Suggest Human Niche Tropism.Genome biology and evolution · 2018Article
- Deciphering the unexplored Leptospira diversity from soils uncovers genomic evolution to virulence.Microbial genomics · 2018Article
- Phylogeographic separation and formation of sexually discrete lineages in a global population ofMicrobial genomics · 2017Article
- Combined Analysis of Variation in Core, Accessory and Regulatory Genome Regions Provides a Super-Resolution View into the Evolution of Bacterial Populations.PLoS genetics · 2016Article
- K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets.Microbial genomics · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution.
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Registered trials
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.