Dataset Information

VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules.

ABSTRACT: We developed VariantBam, a C?++?read filtering and profiling tool for use with BAM, CRAM and SAM sequencing files. VariantBam provides a flexible framework for extracting sequencing reads or read-pairs that satisfy combinations of rules, defined by any number of genomic intervals or variant sites. We have implemented filters based on alignment data, sequence motifs, regional coverage and base quality. For example, VariantBam achieved a median size reduction ratio of 3.1:1 when applied to 10 lung cancer whole genome BAMs by removing large tags and selecting for only high-quality variant-supporting reads and reads matching a large dictionary of sequence motifs. Thus VariantBam enables efficient storage of sequencing data while preserving the most relevant information for downstream analysis.VariantBam and full documentation are available at github.com/jwalabroad/VariantBamrameen@broadinstitute.orgSupplementary data are available at Bioinformatics online.

SUBMITTER: Wala J

PROVIDER: S-EPMC4920121 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Publications

VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules.

Wala Jeremiah J Zhang Cheng-Zhong CZ Meyerson Matthew M Beroukhim Rameen R

Bioinformatics (Oxford, England) 20160226 13

<h4>Unlabelled</h4>We developed VariantBam, a C ++ read filtering and profiling tool for use with BAM, CRAM and SAM sequencing files. VariantBam provides a flexible framework for extracting sequencing reads or read-pairs that satisfy combinations of rules, defined by any number of genomic intervals or variant sites. We have implemented filters based on alignment data, sequence motifs, regional coverage and base quality. For example, VariantBam achieved a median size reduction ratio of 3.1:1 when ...[more]

PMID: 27153727

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Project description:BACKGROUND:DNA methylation (DNAm) age has been widely accepted as an epigenetic biomarker for biological aging. Emerging evidence suggests that DNAm age can be tissue-specific and female breast tissue ages faster than other parts of the body. The Horvath clock, which estimates DNAm age across multiple tissues, has been shown to be poorly calibrated in breast issue. We aim to develop a model to estimate breast tissue-specific DNAm age. METHODS:Genome-wide DNA methylation sequencing data were generated for 459 normal, 107 tumor, and 45 paired adjacent-normal breast tissue samples. We determined a novel set of 286 breast tissue-specific clock CpGs using penalized linear regression and developed a model to estimate breast tissue-specific DNAm age. The model was applied to estimate breast tissue-specific DNAm age in different breast tissue types and in tumors with distinct clinical characteristics to investigate cancer-related aging effects. RESULTS:Our estimated breast tissue-specific DNAm age was highly correlated with chronological age (r = 0.88; p = 2.9 × 10-31) in normal breast tissue. Breast tumor tissue samples exhibited a positive epigenetic age acceleration, where DNAm age was on average 7?years older than respective chronological age (p = 1.8 × 10-8). In age-matched analyses, tumor breast tissue appeared 12 and 13?years older in DNAm age than adjacent-normal and normal breast tissue (p = 4.0 × 10-6 and 1.0 × 10-6, respectively). Both HER2+ and hormone-receptor positive subtypes demonstrated significant acceleration in DNAm ages (p = 0.04 and 3.8 × 10-6, respectively), while no apparent DNAm age acceleration was observed for triple-negative breast tumors. We observed a non-linear pattern of epigenetic age acceleration with breast tumor grade. In addition, early-staged tumors showed a positive epigenetic age acceleration (p = 0.003) while late-staged tumors exhibited a non-significant negative epigenetic age acceleration (p = 0.10). CONCLUSIONS:The intended applications for this model are wide-spread and have been shown to provide biologically meaningful results for cancer-related aging effects in breast tumor tissue. Future studies are warranted to explore whether breast tissue-specific epigenetic age acceleration is predictive of breast cancer development, treatment response, and survival as well as the clinical utility of whether this model can be extended to blood samples.

Dataset Information

VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules.

Publications

VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules.

Similar Datasets

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