Dataset Information

SSRgenotyper: A simple sequence repeat genotyping application for whole-genome resequencing and reduced representational sequencing projects.

ABSTRACT:

Premise

Many programs can identify simple sequence repeat (SSR) motifs in genomic data. SSRgenotyper extends SSR identification to en masse genotyping from resequencing data for diversity panels and linkage mapping populations.

Methods and results

SSRgenotyper will find and genotype SSRs from SAM files and an SSR reference FASTA. Several outputs are possible, including a simple table with the SSR marker name, position, and SSR alleles, defined by the repeat number of the repeat motif. Specific output files include a GENEPOP-formatted file for downstream genetic diversity analyses and a traditional A, H, B mapping file output that is phased to the parents of the population for biparental linkage map construction. Linkage maps produced using SSRgenotyper genotypes were highly collinear with physical maps and correctly inferred known phylogenies.

Conclusions

SSRgenotyper provides an easy-to-use, accurate, and scalable SSR genotyping platform for whole-genome resequencing data. SSRgenotyper is freely available at https://github.com/dlewis27/SSRgenotyper.

SUBMITTER: Lewis DH

PROVIDER: S-EPMC7742204 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

SSRgenotyper: A simple sequence repeat genotyping application for whole-genome resequencing and reduced representational sequencing projects.

Lewis Daniel H DH Jarvis David E DE Maughan Peter J PJ

Applications in plant sciences 20201203 12

<h4>Premise</h4>Many programs can identify simple sequence repeat (SSR) motifs in genomic data. SSRgenotyper extends SSR identification to en masse genotyping from resequencing data for diversity panels and linkage mapping populations.<h4>Methods and results</h4>SSRgenotyper will find and genotype SSRs from SAM files and an SSR reference FASTA. Several outputs are possible, including a simple table with the SSR marker name, position, and SSR alleles, defined by the repeat number of the repeat mo ...[more]

PMID: 33344093

Similar Datasets

Project description:BackgroundMicrosatellites, or simple sequence repeats (SSRs), are tandemly repeated DNA sequences, including tandem copies of specific sequences no longer than six bases, that are distributed in the genome. SSR has been used as a molecular marker because it is easy to detect and is used in a range of applications, including genetic diversity, genome mapping, and marker assisted selection. It is also very mutable because of slipping in the DNA polymerase during DNA replication. This unique mutation increases the insertion/deletion (INDELs) mutation frequency to a high ratio - more than other types of molecular markers such as single nucleotide polymorphism (SNPs).SNPs are more frequent than INDELs. Therefore, all designed algorithms for sequence alignment fit the vast majority of the genomic sequence without considering microsatellite regions, as unique sequences that require special consideration. The old algorithm is limited in its application because there are many overlaps between different repeat units which result in false evolutionary relationships.FindingsTo overcome the limitation of the aligning algorithm when dealing with SSR loci, a new algorithm was developed using PERL script with a Tk graphical interface. This program is based on aligning sequences after determining the repeated units first, and the last SSR nucleotides positions. This results in a shifting process according to the inserted repeated unit type.When studying the phylogenic relations before and after applying the new algorithm, many differences in the trees were obtained by increasing the SSR length and complexity. However, less distance between different linage had been observed after applying the new algorithm.ConclusionsThe new algorithm produces better estimates for aligning SSR loci because it reflects more reliable evolutionary relations between different linages. It reduces overlapping during SSR alignment, which results in a more realistic phylogenic relationship.

Project description:Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96-98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions (among the 301 used in the GBS analysis), 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at ?20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses.

Dataset Information

SSRgenotyper: A simple sequence repeat genotyping application for whole-genome resequencing and reduced representational sequencing projects.

Premise

Methods and results

Conclusions

Publications

SSRgenotyper: A simple sequence repeat genotyping application for whole-genome resequencing and reduced representational sequencing projects.

Similar Datasets

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