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PolyMorphPredict: A Universal Web-Tool for Rapid Polymorphic Microsatellite Marker Discovery From Whole Genome and Transcriptome Data

机译:PolyMorphPredict:一种从整个基因组和转录组数据中快速发现多态微卫星标记的通用网络工具

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摘要

Microsatellites are ubiquitously distributed, polymorphic repeat sequence valuable for association, selection, population structure and identification. They can be mined by genomic library, probe hybridization and sequencing of selected clones. Such approach has many limitations like biased hybridization and selection of larger repeats. In silico mining of polymorphic markers using data of various genotypes can be rapid and economical. Available tools lack in some or other aspects like: targeted user defined primer generation, polymorphism discovery using multiple sequence, size and number limits of input sequence, no option for primer generation and e-PCR evaluation, transferability, lack of complete automation and user-friendliness. They also lack the provision to evaluate published primers in e-PCR mode to generate additional allelic data using re-sequenced data of various genotypes for judicious utilization of previously generated data. We developed the tool (PolyMorphPredict) using Perl, R, Java and launched at Apache which is available at . It mines microsatellite loci and computes primers from genome/transcriptome data of any species. It can perform e-PCR using published primers for polymorphism discovery and across species transferability of microsatellite loci. Present tool has been evaluated using five species of different genome size having 21 genotypes. Though server is equipped with genomic data of three species for test run with gel simulation, but can be used for any species. Further, polymorphism predictability has been validated using in silico and in vitro PCR of four rice genotypes. This tool can accelerate the in silico microsatellite polymorphism discovery in re-sequencing projects of any species of plant and animal for their diversity estimation along with variety/breed identification, population structure, MAS, QTL and gene discovery, traceability, parentage testing, fungal diagnostics and genome finishing.
机译:微卫星无处不在,多态性重复序列对于关联,选择,种群结构和鉴定非常有价值。它们可以通过基因组文库,探针杂交和选定克隆的测序进行挖掘。这种方法具有许多局限性,例如偏向杂交和选择较大的重复序列。在计算机上使用各种基因型数据进行多态性标记的挖掘可能是快速而经济的。可用的工具在某些或其他方面缺乏,例如:有针对性的用户定义引物生成,使用多个序列的多态性发现,输入序列的大小和数量限制,无法进行引物生成和e-PCR评估的选项,可移植性,缺乏完全的自动化和用户-友善。他们还缺乏用e-PCR模式评估已发表的引物,使用各种基因型的重测序数据来生成其他等位基因数据以明智地利用先前生成的数据的规定。我们使用Perl,R,Java开发了工具(PolyMorphPredict),并在Apache上启动了该工具,该工具可从下载。它可以挖掘微卫星基因座,并根据任何物种的基因组/转录组数据计算引物。它可以使用已发表的引物进行多态性发现和微卫星基因座跨物种转移性的e-PCR。已经使用具有21个基因型的不同基因组大小的五个物种对本工具进行了评估。尽管服务器配备了三种物种的基因组数据以进行凝胶模拟测试,但可以用于任何物种。此外,多态性可预测性已使用四种水稻基因型的计算机模拟和体外PCR验证。此工具可以加快对任何动植物物种进行重测序项目中的硅计算机微卫星多态性的发现,以评估其多样性,品种/品种,种群结构,MAS,QTL和基因发现,可追溯性,亲子关系测试,真菌诊断和基因组整理。

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