NGSCheckMate
NGSCheckMate verifies sample identity across next-generation sequencing datasets by comparing allele read fractions at known single-nucleotide polymorphisms to confirm matching datasets from the same subject.
Key Features:
- Versatility Across Data Types: Handles exome sequencing, whole-genome sequencing, RNA-seq, ChIP-seq, targeted sequencing, and single-cell whole-genome sequencing.
- Model-based Genotype Comparison: Compares allele read fractions at known SNPs using a model-based method for genotype similarity assessment.
- Depth-dependent Similarity Metrics: Accounts for the depth-dependent behavior of similarity metrics when comparing datasets.
- Minimal Sequencing Depth Requirement: Operates effectively at sequencing depths greater than 0.5X.
- Alignment-free Module: Provides an alignment-free analysis that can be run directly on FASTQ files for rapid checks.
- File Format Support: Accepts FASTQ, BAM, and VCF files for sample identity verification.
Scientific Applications:
- Quality Control in NGS studies: Serves as a QC step to verify that datasets are correctly paired to subjects across NGS experiments.
- Sample Identity Verification: Detects mismatched or swapped samples across multi-modal or multi-sample datasets.
- Large-scale Genomic Projects: Applies to cohort studies, clinical trials, and population genetics projects where multiple samples or data types are profiled per subject.
Methodology:
Compares allele read fractions at known SNPs using a model-based method that accounts for sequencing-depth-dependent behavior, and includes an alignment-free module that analyzes FASTQ files directly.
Topics
Details
- License:
- MIT
- Tool Type:
- command-line tool
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- R, Python
- Added:
- 7/16/2018
- Last Updated:
- 12/10/2018
Operations
Publications
Lee S, Lee S, Ouellette S, Park W, Lee EA, Park PJ. NGSCheckMate: software for validating sample identity in next-generation sequencing studies within and across data types. Nucleic Acids Research. 2017;45(11):e103-e103. doi:10.1093/nar/gkx193. PMID:28369524. PMCID:PMC5499645.