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.

Documentation