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      MISA-web: a web server for microsatellite prediction

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          Abstract

          Motivation

          Microsatellites are a widely-used marker system in plant genetics and forensics. The development of reliable microsatellite markers from resequencing data is challenging.

          Results

          We extended MISA, a computational tool assisting the development of microsatellite markers, and reimplemented it as a web-based application. We improved compound microsatellite detection and added the possibility to display and export MISA results in GFF3 format for downstream analysis.

          Availability and Implementation

          MISA-web can be accessed under http://misaweb.ipk-gatersleben.de/. The website provides tutorials, usage note as well as download links to the source code.

          Contact

          scholz@ 123456ipk-gatersleben.de

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          Most cited references13

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          Tandem repeats finder: a program to analyze DNA sequences.

          G. Benson (1999)
          A tandem repeat in DNA is two or more contiguous, approximate copies of a pattern of nucleotides. Tandem repeats have been shown to cause human disease, may play a variety of regulatory and evolutionary roles and are important laboratory and analytic tools. Extensive knowledge about pattern size, copy number, mutational history, etc. for tandem repeats has been limited by the inability to easily detect them in genomic sequence data. In this paper, we present a new algorithm for finding tandem repeats which works without the need to specify either the pattern or pattern size. We model tandem repeats by percent identity and frequency of indels between adjacent pattern copies and use statistically based recognition criteria. We demonstrate the algorithm's speed and its ability to detect tandem repeats that have undergone extensive mutational change by analyzing four sequences: the human frataxin gene, the human beta T cellreceptor locus sequence and two yeast chromosomes. These sequences range in size from 3 kb up to 700 kb. A World Wide Web server interface atc3.biomath.mssm.edu/trf.html has been established for automated use of the program.
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            Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L.).

            T Thiel, W Michalek, R. K. Varshney … (2003)
            A software tool was developed for the identification of simple sequence repeats (SSRs) in a barley ( Hordeum vulgare L.) EST (expressed sequence tag) database comprising 24,595 sequences. In total, 1,856 SSR-containing sequences were identified. Trimeric SSR repeat motifs appeared to be the most abundant type. A subset of 311 primer pairs flanking SSR loci have been used for screening polymorphisms among six barley cultivars, being parents of three mapping populations. As a result, 76 EST-derived SSR-markers were integrated into a barley genetic consensus map. A correlation between polymorphism and the number of repeats was observed for SSRs built of dimeric up to tetrameric units. 3'-ESTs yielded a higher portion of polymorphic SSRs (64%) than 5'-ESTs did. The estimated PIC (polymorphic information content) value was 0.45 +/- 0.03. Approximately 80% of the SSR-markers amplified DNA fragments in Hordeum bulbosum, followed by rye, wheat (both about 60%) and rice (40%). A subset of 38 EST-derived SSR-markers comprising 114 alleles were used to investigate genetic diversity among 54 barley cultivars. In accordance with a previous, RFLP-based, study, spring and winter cultivars, as well as two- and six-rowed barleys, formed separate clades upon PCoA analysis. The results show that: (1) with the software tool developed, EST databases can be efficiently exploited for the development of cDNA-SSRs, (2) EST-derived SSRs are significantly less polymorphic than those derived from genomic regions, (3) a considerable portion of the developed SSRs can be transferred to related species, and (4) compared to RFLP-markers, cDNA-SSRs yield similar patterns of genetic diversity.
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              SciRoKo: a new tool for whole genome microsatellite search and investigation.

              Tamas Lelley, Robert Kofler, Christian Schlötterer (2007)
              SciRoKo is a user-friendly software tool for the identification of microsatellites in genomic sequences. The combination of an extremely fast search algorithm with a built-in summary statistic tool makes SciRoKo an excellent tool for full genome analysis. Compared to other already existing tools, SciRoKo also allows the analysis of compound microsatellites. free for use: www.kofler.or.at/Bioinformatics. Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 August 2017
                Publication date (Electronic): 07 April 2017
                Publication date PMC-release: 07 April 2017
                Volume: 33
                Issue: 16
                Pages: 2583-2585
                Affiliations
                [1 ]Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, 06466 Seeland, Germany
                [2 ]KWS Saat SE, Grimsehlstr. 31, 37555 Einbeck, Germany
                [3 ]German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany
                Author notes
                [* ]To whom correspondence should be addressed.

                Associate Editor: Alfonso Valencia

                Article
                Publisher ID: btx198
                DOI: 10.1093/bioinformatics/btx198
                PMC ID: 5870701
                PubMed ID: 28398459
                SO-VID: 3e3e5e3e-05f1-467a-bdeb-87ecc1084261
                Copyright © © The Author 2017. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Date received : 02 November 2016
                Date revision received : 07 March 2017
                Date accepted : 06 April 2017
                Page count
                Pages: 3
                Categories
                Subject: Applications Notes
                Subject: Sequence Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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