Dataset Information

Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes.

ABSTRACT: Bacterial genomes vary extensively in terms of both gene content and gene sequence. This plasticity hampers the use of traditional SNP-based methods for identifying all genetic associations with phenotypic variation. Here we introduce a computationally scalable and widely applicable statistical method (SEER) for the identification of sequence elements that are significantly enriched in a phenotype of interest. SEER is applicable to tens of thousands of genomes by counting variable-length k-mers using a distributed string-mining algorithm. Robust options are provided for association analysis that also correct for the clonal population structure of bacteria. Using large collections of genomes of the major human pathogens Streptococcus pneumoniae and Streptococcus pyogenes, SEER identifies relevant previously characterized resistance determinants for several antibiotics and discovers potential novel factors related to the invasiveness of S. pyogenes. We thus demonstrate that our method can answer important biologically and medically relevant questions.

SUBMITTER: Lees JA

PROVIDER: S-EPMC5028413 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes.

Lees John A JA Vehkala Minna M Välimäki Niko N Harris Simon R SR Chewapreecha Claire C Croucher Nicholas J NJ Marttinen Pekka P Davies Mark R MR Steer Andrew C AC Tong Steven Y C SY Honkela Antti A Parkhill Julian J Bentley Stephen D SD Corander Jukka J

Nature communications 20160916

Bacterial genomes vary extensively in terms of both gene content and gene sequence. This plasticity hampers the use of traditional SNP-based methods for identifying all genetic associations with phenotypic variation. Here we introduce a computationally scalable and widely applicable statistical method (SEER) for the identification of sequence elements that are significantly enriched in a phenotype of interest. SEER is applicable to tens of thousands of genomes by counting variable-length k-mers ...[more]

PMID: 27633831

Similar Datasets

Project description:MotivationBrain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful interpretation and modest dimensionality compared with voxelwise approaches. Typical ROI-level measures used in these studies are summary statistics from voxelwise measures in the region, without making full use of individual voxel signals.ResultsIn this article, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxelwise enrichment analysis, which embraces the collective effect of weak voxel-level signals and integrates brain anatomical annotation information. Our proposed method achieves three goals at the same time: (i) increase the statistical power by substantially reducing the burden of multiple comparison correction; (ii) employ brain annotation information to enable biologically meaningful interpretation and (iii) make full use of fine-grained voxelwise signals. We demonstrate our method on an imaging genetic analysis using data from the Alzheimer's Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxelwise FDG-positron emission tomography measures between 116 ROIs and 565 373 single-nucleotide polymorphisms. Compared with traditional ROI-wise and voxelwise approaches, our method identified 2946 novel imaging genetic associations in addition to 33 ones overlapping with the two benchmark methods. In particular, two newly reported variants were further supported by transcriptome evidences from region-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain.Availability and implementationThe R code and sample data are freely available at https://github.com/lshen/RIGEA.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes.

Publications

Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets