Dataset Information

TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data.

ABSTRACT:

Unlabelled

TREAT (Targeted RE-sequencing Annotation Tool) is a tool for facile navigation and mining of the variants from both targeted resequencing and whole exome sequencing. It provides a rich integration of publicly available as well as in-house developed annotations and visualizations for variants, variant-hosting genes and host-gene pathways.

Availability and implementation

TREAT is freely available to non-commercial users as either a stand-alone annotation and visualization tool, or as a comprehensive workflow integrating sequencing alignment and variant calling. The executables, instructions and the Amazon Cloud Images of TREAT can be downloaded at the website: http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm.

SUBMITTER: Asmann YW

PROVIDER: S-EPMC3259432 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Publications

TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data.

Asmann Yan W YW Middha Sumit S Hossain Asif A Baheti Saurabh S Li Ying Y Chai High-Seng HS Sun Zhifu Z Duffy Patrick H PH Hadad Ahmed A AA Nair Asha A Liu Xiaoyu X Zhang Yuji Y Klee Eric W EW Kalari Krishna R KR Kocher Jean-Pierre A JP

Bioinformatics (Oxford, England) 20111115 2

<h4>Unlabelled</h4>TREAT (Targeted RE-sequencing Annotation Tool) is a tool for facile navigation and mining of the variants from both targeted resequencing and whole exome sequencing. It provides a rich integration of publicly available as well as in-house developed annotations and visualizations for variants, variant-hosting genes and host-gene pathways.<h4>Availability and implementation</h4>TREAT is freely available to non-commercial users as either a stand-alone annotation and visualization ...[more]

PMID: 22088845

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Project description:The targeted capture and sequencing of genomic regions has rapidly demonstrated its utility in genetic studies. Inherent in this technology is considerable heterogeneity of target coverage and this is expected to systematically impact our sensitivity to detect genuine polymorphisms. To fully interpret the polymorphisms identified in a genetic study it is often essential to both detect polymorphisms and to understand where and with what probability real polymorphisms may have been missed.Using down-sampling of 30 deeply sequenced exomes and a set of gold-standard single nucleotide variant (SNV) genotype calls for each sample, we developed an empirical model relating the read depth at a polymorphic site to the probability of calling the correct genotype at that site. We find that measured sensitivity in SNV detection is substantially worse than that predicted from the naive expectation of sampling from a binomial. This calibrated model allows us to produce single nucleotide resolution SNV sensitivity estimates which can be merged to give summary sensitivity measures for any arbitrary partition of the target sequences (nucleotide, exon, gene, pathway, exome). These metrics are directly comparable between platforms and can be combined between samples to give "power estimates" for an entire study. We estimate a local read depth of 13X is required to detect the alleles and genotype of a heterozygous SNV 95% of the time, but only 3X for a homozygous SNV. At a mean on-target read depth of 20X, commonly used for rare disease exome sequencing studies, we predict 5-15% of heterozygous and 1-4% of homozygous SNVs in the targeted regions will be missed.Non-reference alleles in the heterozygote state have a high chance of being missed when commonly applied read coverage thresholds are used despite the widely held assumption that there is good polymorphism detection at these coverage levels. Such alleles are likely to be of functional importance in population based studies of rare diseases, somatic mutations in cancer and explaining the "missing heritability" of quantitative traits.

Dataset Information

TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data.

Unlabelled

Availability and implementation

Publications

TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data.

Similar Datasets

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