SNPConvert:家畜SNP阵列标准化与整合。

Ezequiel Luis Nicolazzi, Gabriele Marras, Alessandra Stella
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引用次数: 7

摘要

单核苷酸多态性(SNP)阵列技术的主要优点之一是以相对较低的成本为特定数量的SNP标记提供基因型调用。自首次应用于动物遗传学以来,每个物种的可用SNP阵列数量一直在不断增加。然而,与在全基因组序列数据分析中观察到的相反,SNP阵列数据没有一套通用的文件格式或用于等位基因调用的编码约定。因此,多源SNP阵列数据的标准化和集成已经成为一个障碍,特别是对于具有基本或没有编程技能的用户。在这里,我们描述了与处理SNP阵列数据相关的困难,重点是文件格式,SNP等位基因编码和映射。我们还提供了SNPConvert套件,这是一个多平台、开源和用户友好的工具集,可以克服这些问题。该工具可以与现有的开源和开放访问工具集成,是迈向标准化和集成任何类型的原始SNP阵列数据的集成系统的第一步。该工具可从https://github获得。com/nicolazzie/SNPConvert.git。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SNPConvert: SNP Array Standardization and Integration in Livestock Species.

SNPConvert: SNP Array Standardization and Integration in Livestock Species.

One of the main advantages of single nucleotide polymorphism (SNP) array technology is providing genotype calls for a specific number of SNP markers at a relatively low cost. Since its first application in animal genetics, the number of available SNP arrays for each species has been constantly increasing. However, conversely to that observed in whole genome sequence data analysis, SNP array data does not have a common set of file formats or coding conventions for allele calling. Therefore, the standardization and integration of SNP array data from multiple sources have become an obstacle, especially for users with basic or no programming skills. Here, we describe the difficulties related to handling SNP array data, focusing on file formats, SNP allele coding, and mapping. We also present SNPConvert suite, a multi-platform, open-source, and user-friendly set of tools to overcome these issues. This tool, which can be integrated with open-source and open-access tools already available, is a first step towards an integrated system to standardize and integrate any type of raw SNP array data. The tool is available at: https://github. com/nicolazzie/SNPConvert.git.

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来源期刊
自引率
0.00%
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0
审稿时长
11 weeks
期刊介绍: High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: Microarrays, DNA Sequencing, RNA Sequencing, Protein Identification and Quantification, Cell-based Approaches, Omics Technologies, Imaging, Bioinformatics, Computational Biology/Chemistry, Statistics, Integrative Omics, Drug Discovery and Development, Microfluidics, Lab-on-a-chip, Data Mining, Databases, Multiplex Assays.
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