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Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data

机译:Control-FREEC:使用下一代测序数据评估拷贝数和等位基因含量的工具

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摘要

Summary: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks.
机译:简介:越来越多的癌症研究使用下一代测序(NGS)数据来检测各种类型的基因组变异。然而,即使当研究人员掌握了此类数据时,单核苷酸多态性阵列也被认为是评估拷贝数变化尤其是杂合性丧失(LOH)所必需的。在这里,我们介绍了Control-FREEC工具,该工具可从NGS数据自动计算拷贝数和等位基因含量概况,并因此预测基因组改变的区域,例如增益,损失和LOH。以对齐读取作为输入,Control-FREEC构建拷贝数和B等位基因频率概况。然后对谱图进行归一化,分段和分析,以便为每个基因组区域分配基因型状态(拷贝数和等位基因含量)。提供匹配的正常样品后,Control-FREEC会将体细胞与种系事件区分开。 Control-FREEC能够分析超二倍体肿瘤样品和被正常细胞污染的样品。使用提供的可映射性轨迹,可将低可映射性区域排除在分析之外。

著录项

  • 来源
    《Bioinformatics》 |2012年第3期|p.423-425|共3页
  • 作者

    Emmanuel Barillot;

  • 作者单位
  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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