PypeAmplicon
PypeAmplicon processes amplicon sequencing data to isolate short genomic regions containing single nucleotide polymorphisms (SNPs) and generate individual genotypes for population genetic analyses.
Key Features:
- Reduced SNP Set Isolation: Provides a systematic approach for isolating short genomic regions containing single nucleotide polymorphisms (SNPs) for targeted amplicon sequencing, enabling focus on informative markers without whole-genome sequencing.
- Automated Data Processing: Automates the processing of raw sequence reads into individual genotypes for datasets generated by high-multiplexed PCR and next-generation sequencing.
- Empirical Recovery Rates (Belgica antarctica): Demonstrated recovery of 81% of designed amplicons and successful genotyping of 76% of targeted SNPs in the Antarctic midge Belgica antarctica.
- Discovery of Novel SNPs: Sequencing approximately 150 base pairs around targeted regions identified 80 additional novel SNPs.
- Comparative Analysis Capability: Enables comparison of amplicon data with low-coverage whole-genome resequencing (lcWGR) data to produce results comparable to lcWGR for population genetic inference.
Scientific Applications:
- Population genetics data development: Supports efficient development of population genetic datasets from targeted amplicon sequencing.
- Non-model organism studies: Applicable to marker development and genotyping in non-model organisms such as Belgica antarctica.
- Conservation genetics: Facilitates targeted marker generation and genotyping for conservation and management studies.
- Evolutionary biology and ecological genomics: Provides targeted SNP data for studies of evolutionary processes and ecological genomic patterns.
Methodology:
Integrates high-multiplexed PCR with next-generation sequencing to generate amplicon data and processes raw sequence reads through automated steps to extract individual genotypes, with support for comparison to low-coverage whole-genome resequencing (lcWGR) datasets.
Topics
Details
- License:
- GPL-3.0
- Programming Languages:
- Python
- Added:
- 11/14/2019
- Last Updated:
- 12/11/2020
Operations
Publications
Pavinato VAC, Wijeratne S, Spacht D, Denlinger DL, Meulia T, Michel AP. Leveraging targeted sequencing for non-model species: a step-by-step guide to obtain a reduced SNP set and a pipeline to automate data processing in the Antarctic Midge, <i>Belgica antarctica</i>. Unknown Journal. 2019. doi:10.1101/772384.