Dataset Information

A pipeline for high throughput detection and mapping of SNPs from EST databases.

ABSTRACT: Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic variation that can be used as molecular markers. The SNPs that are hidden in sequence databases can be unlocked using bioinformatic tools. For efficient application of these SNPs, the sequence set should be error-free as much as possible, targeting single loci and suitable for the SNP scoring platform of choice. We have developed a pipeline to effectively mine SNPs from public EST databases with or without quality information using QualitySNP software, select reliable SNP and prepare the loci for analysis on the Illumina GoldenGate genotyping platform. The applicability of the pipeline was demonstrated using publicly available potato EST data, genotyping individuals from two diploid mapping populations and subsequently mapping the SNP markers (putative genes) in both populations. Over 7000 reliable SNPs were identified that met the criteria for genotyping on the GoldenGate platform. Of the 384 SNPs on the SNP array approximately 12% dropped out. For the two potato mapping populations 165 and 185 SNPs segregating SNP loci could be mapped on the respective genetic maps, illustrating the effectiveness of our pipeline for SNP selection and validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11032-009-9377-5) contains supplementary material, which is available to authorized users.

SUBMITTER: Anithakumari AM

PROVIDER: S-EPMC2869401 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A pipeline for high throughput detection and mapping of SNPs from EST databases.

Anithakumari A M AM Tang Jifeng J van Eck Herman J HJ Visser Richard G F RG Leunissen Jack A M JA Vosman Ben B van der Linden C Gerard CG

Molecular breeding : new strategies in plant improvement 20100120 1

Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic variation that can be used as molecular markers. The SNPs that are hidden in sequence databases can be unlocked using bioinformatic tools. For efficient application of these SNPs, the sequence set should be error-free as much as possible, targeting single loci and suitable for the SNP scoring platform of choice. We have developed a pipeline to effectively mine SNPs from public EST databases with or without quality ...[more]

PMID: 20502512

Similar Datasets

Project description:BackgroundApproximately half of the mitochondrial genome inherent within 546 individual Atlantic salmon (Salmo salar) derived from across the species' North Atlantic range, was selectively amplified with a novel combination of standard PCR and pyro-sequencing in a single run using 454 Titanium FLX technology (Roche, 454 Life Sciences). A unique combination of barcoded primers and a partitioned sequencing plate was employed to designate each sequence read to its original sample. The sequence reads were aligned according to the S. salar mitochondrial reference sequence (NC_001960.1), with the objective of identifying single nucleotide polymorphisms (SNPs). They were validated if they met with the following three stringent criteria: (i) sequence reads were produced from both DNA strands; (ii) SNPs were confirmed in a minimum of 90% of replicate sequence reads; and (iii) SNPs occurred in more than one individual.ResultsPyrosequencing generated a total of 179,826,884 bp of data, and 10,765 of the total 10,920 S. salar sequences (98.6%) were assigned back to their original samples. The approach taken resulted in a total of 216 SNPs and 2 indels, which were validated and mapped onto the S. salar mitochondrial genome, including 107 SNPs and one indel not previously reported. An average of 27.3 sequence reads with a standard deviation of 11.7 supported each SNP per individual.ConclusionThe study generated a mitochondrial SNP panel from a large sample group across a broad geographical area, reducing the potential for ascertainment bias, which has hampered previous studies. The SNPs identified here validate those identified in previous studies, and also contribute additional potentially informative loci for the future study of phylogeography and evolution in the Atlantic salmon. The overall success experienced with this novel application of HT sequencing of targeted regions suggests that the same approach could be successfully applied for SNP mining in other species.

Project description:BackgroundSingle nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable.ResultsWe developed SNP-PHAGE (SNP discovery Pipeline with additional features for identification of common haplotypes within a sequence tagged site (Haplotype Analysis) and GenBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at http://bfgl.anri.barc.usda.gov/ML/snp-phage/.ConclusionSNP-PHAGE provides a bioinformatics solution for high throughput SNP discovery, identification of common haplotypes within an amplicon, and GenBank (dbSNP) submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers.

Dataset Information

A pipeline for high throughput detection and mapping of SNPs from EST databases.

Publications

A pipeline for high throughput detection and mapping of SNPs from EST databases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets