VarCon:一个检索SNV邻近核苷酸的R包。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2020-11-24 eCollection Date: 2020-01-01 DOI:10.1177/1176935120976399
Johannes Ptok, Stephan Theiss, Heiner Schaal
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引用次数: 0

摘要

单核苷酸变异(SNV)的报告遵循序列变异命名法(http://varnomen.hgvs.org/),使用特定于编码和非编码DNA的明确编号方案。然而,一个给定SNV的相应序列邻域,这是评估其对剪接调节的影响所必需的,不容易从这个命名法中获得。新工具VarCon结合了Ensembl人类参考基因组的信息和相应的转录表,可以快速方便地从给定的SNV参考中获取该邻域,从而进行准确的检索。VarCon还显示剪接位点分数(HBond和MaxEnt分数)和SNV邻域的HEXplorer概况,反映位置依赖的剪接增强和沉默特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

VarCon: An R Package for Retrieving Neighboring Nucleotides of an SNV.

VarCon: An R Package for Retrieving Neighboring Nucleotides of an SNV.

VarCon: An R Package for Retrieving Neighboring Nucleotides of an SNV.

Reporting of a single nucleotide variant (SNV) follows the Sequence Variant Nomenclature (http://varnomen.hgvs.org/), using an unambiguous numbering scheme specific for coding and noncoding DNA. However, the corresponding sequence neighborhood of a given SNV, which is required to assess its impact on splicing regulation, is not easily accessible from this nomenclature. Providing fast and easy access to this neighborhood just from a given SNV reference, the novel tool VarCon combines information of the Ensembl human reference genome and the corresponding transcript table for accurate retrieval. VarCon also displays splice site scores (HBond and MaxEnt scores) and HEXplorer profiles of an SNV neighborhood, reflecting position-dependent splice enhancing and silencing properties.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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