Dataset Information

Disk covering methods improve phylogenomic analyses.

ABSTRACT:

Motivation

With the rapid growth rate of newly sequenced genomes, species tree inference from multiple genes has become a basic bioinformatics task in comparative and evolutionary biology. However, accurate species tree estimation is difficult in the presence of gene tree discordance, which is often due to incomplete lineage sorting (ILS), modelled by the multi-species coalescent. Several highly accurate coalescent-based species tree estimation methods have been developed over the last decade, including MP-EST. However, the running time for MP-EST increases rapidly as the number of species grows.

Results

We present divide-and-conquer techniques that improve the scalability of MP-EST so that it can run efficiently on large datasets. Surprisingly, this technique also improves the accuracy of species trees estimated by MP-EST, as our study shows on a collection of simulated and biological datasets.

SUBMITTER: Bayzid MS

PROVIDER: S-EPMC4239662 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Disk covering methods improve phylogenomic analyses.

Bayzid Md Shamsuzzoha MS Hunt Tyler T Warnow Tandy T

BMC genomics 20141017

<h4>Motivation</h4>With the rapid growth rate of newly sequenced genomes, species tree inference from multiple genes has become a basic bioinformatics task in comparative and evolutionary biology. However, accurate species tree estimation is difficult in the presence of gene tree discordance, which is often due to incomplete lineage sorting (ILS), modelled by the multi-species coalescent. Several highly accurate coalescent-based species tree estimation methods have been developed over the last d ...[more]

PMID: 25572610

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Project description:Members of the Bifidobacteriaceae family represent both dominant microbial groups that colonize the gut of various animals, especially during the suckling stage of their life, while they also occur as pathogenic bacteria of the urogenital tract. The pan-genome of the genus Bifidobacterium has been explored in detail in recent years, though genomics of the Bifidobacteriaceae family has not yet received much attention. Here, a comparative genomic analyses of 67 Bifidobacteriaceae (sub) species including all currently recognized genera of this family, i.e., Aeriscardovia, Alloscardovia, Bifidobacterium, Bombiscardovia, Gardnerella, Neoscardovia, Parascardovia, Pseudoscardovia and Scardovia, was performed. Furthermore, in order to include a representative of each of the 67 (currently recognized) (sub) species belonging to the Bifidobacteriaceae family, we sequenced the genomes of an additional 11 species from this family, accomplishing the most extensive comparative genomic analysis performed within this family so far.Phylogenomics-based analyses revealed the deduced evolutionary pathway followed by each member of the Bifidobacteriaceae family, highlighting Aeriscardovia aeriphila LMG 21773 as the deepest branch in the evolutionary tree of this family. Furthermore, functional analyses based on genome content unveil connections between a given member of the family, its carbohydrate utilization abilities and its corresponding host. In this context, bifidobacterial (sub) species isolated from humans and monkeys possess the highest relative number of acquired glycosyl hydrolase-encoding genes, probably in order to enhance their metabolic ability to utilize different carbon sources consumed by the host.Within the Bifidobacteriaceae family, genomics of the genus Bifidobacterium has been extensively investigated. In contrast, very little is known about the genomics of members of the other eight genera of this family. In this study, we decoded the genome sequences of each member of the Bifidobacteriaceae family. Thanks to subsequent comparative genomic and phylogenetic analyses, the deduced pan-genome of this family, as well as the predicted evolutionary development of each taxon belonging to this family was assessed.

Dataset Information

Disk covering methods improve phylogenomic analyses.

Motivation

Results

Publications

Disk covering methods improve phylogenomic analyses.

Similar Datasets

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