Sniper
Sniper identifies single nucleotide polymorphisms (SNPs) from next-generation sequencing data by explicitly modeling multi-mapping reads with a multi-locus Bayesian probabilistic approach to improve SNP discovery in repetitive and paralogous regions of eukaryotic genomes.
Key Features:
- Multi-locus Bayesian model: Implements a multi-locus Bayesian probabilistic model to represent allele configurations across multiple genomic loci.
- Multi-mapping read handling: Explicitly incorporates and analyzes reads that map to multiple genomic locations rather than discarding them.
- Computational efficiency: Uses a computationally efficient algorithm specifically tailored to handle sequence reads mapping to multiple loci.
- Error modeling: Accounts for sequencing errors when estimating variant probabilities.
- Template bias correction: Models template bias to reduce false-positive variant calls arising from amplification or library biases.
- Multi-locus SNP combination modeling: Represents and evaluates combinations of SNPs across loci to improve accuracy in repetitive regions.
- Sensitivity and specificity considerations: Designed to maintain high sensitivity and specificity across a range of conditions encountered in complex genomes.
Scientific Applications:
- Population genomics: Detection and characterization of genetic variation within and between populations using NGS data.
- Disease-association studies: Identification of SNPs for downstream association analyses linking variants to phenotypes or diseases.
- Evolutionary biology: Analysis of genomic diversity and evolutionary patterns in organisms with repetitive or paralogous genomes.
- SNP discovery in complex genomes: Improved SNP identification in eukaryotic genomes containing interspersed repetitive elements and paralogous genes.
Methodology:
Applies a multi-locus Bayesian probabilistic model with a computationally efficient algorithm to explicitly incorporate and analyze multi-mapping reads while accounting for sequencing errors, template bias, and multi-locus SNP combinations.
Topics
Details
- Tool Type:
- command-line tool
- Operating Systems:
- Linux, Mac
- Programming Languages:
- Python, C
- Added:
- 1/13/2017
- Last Updated:
- 11/25/2024
Operations
Publications
Simola DF, Kim J. Sniper: improved SNP discovery by multiply mapping deep sequenced reads. Genome Biology. 2011;12(6). doi:10.1186/gb-2011-12-6-r55. PMID:21689413. PMCID:PMC3218843.