ArticleF1000Research2012
A domain sequence approach to pangenomics: applications to Escherichia coli.
Article in F1000Research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 17 citations in OpenAlex.
- Pan-Genome of the GenusFrontiers in microbiology · 2021Article
- Hierarchical sets: analyzing pangenome structure through scalable set visualizations.Bioinformatics (Oxford, England) · 2017Article
- Comparison of 432 Pseudomonas strains through integration of genomic, functional, metabolic and expression data.Scientific reports · 2016Article
- Protein domain architectures provide a fast, efficient and scalable alternative to sequence-based methods for comparative functional genomics.F1000Research · 2016Article
- micropan: an R-package for microbial pan-genomics.BMC bioinformatics · 2015Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of microbial pangenomes relies on the computation of gene families, i.e. the clustering of coding sequences into groups of essentially similar genes. There is no standard approach to obtain such gene families. Ideally, the gene family computations should be robust against errors in the annotation of genes in various genomes. In an attempt to achieve this robustness, we propose to cluster sequences by their domain sequence, i.e. the ordered sequence of domains in their protein sequence. In a study of 347 genomes from Escherichia coli we find on average around 4500 proteins having hits in Pfam-A in every genome, clustering into around 2500 distinct domain sequence families in each genome. Across all genomes we find a total of 5724 such families. A binomial mixture model approach indicates this is around 95% of all domain sequences we would expect to see in E. coli in the future. A Heaps law analysis indicates the population of domain sequences is larger, but this analysis is also very sensitive to smaller changes in the computation procedure. The resolution between strains is good despite the coarse grouping obtained by domain sequence families. Clustering sequences by their ordered domain content give us domain sequence families, who are robust to errors in the gene prediction step. The computational load of the procedure scales linearly with the number of genomes, which is needed for the future explosion in the number of re-sequenced strains. The use of domain sequence families for a functional classification of strains clearly has some potential to be explored.
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Registered trials
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