Dataset Information

A fully automated pipeline for quantitative genotype calling from next generation sequencing data in autopolyploids.

ABSTRACT: BACKGROUND:Genotyping-by-sequencing (GBS) has been used broadly in genetic studies for several species, especially those with agricultural importance. However, its use is still limited in autopolyploid species because genotype calling software generally fails to properly distinguish heterozygous classes based on allele dosage. RESULTS:VCF2SM is a Python script that integrates sequencing depth information of polymorphisms in variant call format (VCF) files and SUPERMASSA software for quantitative genotype calling. VCFs can be obtained from any variant discovery software that outputs exact allele sequencing depth, such as a modified version of the TASSEL-GBS pipeline provided here. VCF2SM was successfully applied in analyzing GBS data from diverse panels (alfalfa and potato) and full-sib mapping populations (alfalfa and switchgrass) of polyploid species. CONCLUSIONS:We demonstrate that our approach can help plant geneticists working with autopolyploid species to advance their studies by distinguishing allele dosage from GBS data.

SUBMITTER: Pereira GS

PROVIDER: S-EPMC6211426 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A fully automated pipeline for quantitative genotype calling from next generation sequencing data in autopolyploids.

Pereira Guilherme S GS Garcia Antonio Augusto F AAF Margarido Gabriel R A GRA

BMC bioinformatics 20181101 1

<h4>Background</h4>Genotyping-by-sequencing (GBS) has been used broadly in genetic studies for several species, especially those with agricultural importance. However, its use is still limited in autopolyploid species because genotype calling software generally fails to properly distinguish heterozygous classes based on allele dosage.<h4>Results</h4>VCF2SM is a Python script that integrates sequencing depth information of polymorphisms in variant call format (VCF) files and SUPERMASSA software f ...[more]

PMID: 30382832

Similar Datasets

Project description:A fundamental challenge in analyzing next-generation sequencing (NGS) data is to determine an individual's genotype accurately, as the accuracy of the inferred genotype is essential to downstream analyses. Correctly estimating the base-calling error rate is critical to accurate genotype calls. Phred scores that accompany each call can be used to decide which calls are reliable. Some genotype callers, such as GATK and SAMtools, directly calculate the base-calling error rates from phred scores or recalibrated base quality scores. Others, such as SeqEM, estimate error rates from the read data without using any quality scores. It is also a common quality control procedure to filter out reads with low phred scores. However, choosing an appropriate phred score threshold is problematic as a too high threshold may lose data, while a too low threshold may introduce errors. We propose a new likelihood-based genotype-calling approach that exploits all reads and estimates the per-base error rates by incorporating phred scores through a logistic regression model. The approach, which we call PhredEM, uses the expectation-maximization (EM) algorithm to obtain consistent estimates of genotype frequencies and logistic regression parameters. It also includes a simple, computationally efficient screening algorithm to identify loci that are estimated to be monomorphic, so that only loci estimated to be nonmonomorphic require application of the EM algorithm. Like GATK, PhredEM can be used together with a linkage-disequilibrium-based method such as Beagle, which can further improve genotype calling as a refinement step. We evaluate the performance of PhredEM using both simulated data and real sequencing data from the UK10K project and the 1000 Genomes project. The results demonstrate that PhredEM performs better than either GATK or SeqEM, and that PhredEM is an improved, robust, and widely applicable genotype-calling approach for NGS studies. The relevant software is freely available.

Dataset Information

A fully automated pipeline for quantitative genotype calling from next generation sequencing data in autopolyploids.

Publications

A fully automated pipeline for quantitative genotype calling from next generation sequencing data in autopolyploids.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets