Dataset Information

SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences.

ABSTRACT: Simple Sequence Repeats (SSRs) are used to address a variety of research questions in a variety of fields (e.g. population genetics, phylogenetics, forensics, etc.), due to their high mutability within and between species. Here, we present an innovative algorithm, SA-SSR, based on suffix and longest common prefix arrays for efficiently detecting SSRs in large sets of sequences. Existing SSR detection applications are hampered by one or more limitations (i.e. speed, accuracy, ease-of-use, etc.). Our algorithm addresses these challenges while being the most comprehensive and correct SSR detection software available. SA-SSR is 100% accurate and detected >1000 more SSRs than the second best algorithm, while offering greater control to the user than any existing software.SA-SSR is freely available at http://github.com/ridgelab/SA-SSR CONTACT: perry.ridge@byu.eduSupplementary data are available at Bioinformatics online.

SUBMITTER: Pickett BD

PROVIDER: S-EPMC5013907 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Publications

SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences.

Pickett B D BD Karlinsey S M SM Penrod C E CE Cormier M J MJ Ebbert M T W MT Shiozawa D K DK Whipple C J CJ Ridge P G PG

Bioinformatics (Oxford, England) 20160511 17

<h4>Unlabelled</h4>Simple Sequence Repeats (SSRs) are used to address a variety of research questions in a variety of fields (e.g. population genetics, phylogenetics, forensics, etc.), due to their high mutability within and between species. Here, we present an innovative algorithm, SA-SSR, based on suffix and longest common prefix arrays for efficiently detecting SSRs in large sets of sequences. Existing SSR detection applications are hampered by one or more limitations (i.e. speed, accuracy, e ...[more]

PMID: 27170037

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Project description:BACKGROUND:High levels of pairwise linkage disequilibrium (LD) in single nucleotide polymorphism (SNP) array or whole-genome sequence data may affect both performance and efficiency of genomic prediction models. Thus, this warrants pruning of genotyping data for high LD. We developed an algorithm, named SNPrune, which enables the rapid detection of any pair of SNPs in complete or high LD throughout the genome. METHODS:LD, measured as the squared correlation between phased alleles (r2), can only reach a value of 1 when both loci have the same count of the minor allele. Sorting loci based on the minor allele count, followed by comparison of their alleles, enables rapid detection of loci in complete LD. Detection of loci in high LD can be optimized by computing the range of the minor allele count at another locus for each possible value of the minor allele count that can yield LD values higher than a predefined threshold. This efficiently reduces the number of pairs of loci for which LD needs to be computed, instead of considering all pairwise combinations of loci. The implemented algorithm SNPrune considered bi-allelic loci either using phased alleles or allele counts as input. SNPrune was validated against PLINK on two datasets, using an r2 threshold of 0.99. The first dataset contained 52k SNP genotypes on 3534 pigs and the second dataset contained simulated whole-genome sequence data with 10.8 million SNPs and 2500 animals. RESULTS:SNPrune removed a similar number of SNPs as PLINK from the pig data but SNPrune was almost 12 times faster than PLINK. From the simulated sequence data with 10.8 million SNPs, SNPrune removed 6.4 and 1.4 million SNPs due to complete and high LD. Results were very similar regardless of whether phased alleles or allele counts were used. Using allele counts and multi-threading with 10 threads, SNPrune completed the analysis in 21 min. Using a sliding window of up to 500,000 SNPs, PLINK removed ~ 43,000 less SNPs (0.6%) in the sequence data and SNPrune was 24 to 170 times faster, using one or ten threads, respectively. CONCLUSIONS:The SNPrune algorithm developed here is able to remove SNPs in high LD throughout the genome very efficiently in large datasets.

Dataset Information

SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences.

Publications

SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences.

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