ASEQ: fast allele-specific studies from next-generation sequencing data.
ASEQ: fast allele-specific studies from next-generation sequencing data.
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DOI：
10.1186/s12920-015-0084-2
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发表时间：
2015-03-01
影响因子：
2.7
通讯作者：
Demichelis F
中科院分区：
文献类型：
--
作者：
Romanel A;Lago S;Prandi D;Sboner A;Demichelis F
来自下一代测序（NGS）的单个碱基水平信息允许定量评估生物现象，例如健康和患病细胞中的嵌合现象或等位基因特异性特征。这类研究通常会带来具有挑战性的计算负担，阻碍了对大型数据集的全基因组研究，这些数据集现在通过1,000个基因组计划和癌症基因组图谱（TCGA）计划变得可用。我们提出ASEQ，一种从配对的基因组和转录组NGS数据进行基因水平等位基因特异性表达（ASE）分析的工具，而不需要父亲和母亲的基因组数据。ASEQ提供了一套易于使用的模式，对用户透明，充分利用了内置的快速计算引擎。我们报告了它对来自1,000个基因组计划的20个个体的表现，并展示了它对印记基因的检测能力。接下来，我们将其与AlleleSeq和MBASED工具进行比较时，展示了高水平的ASE调用一致性。最后，使用前列腺癌数据集，我们报告了相对于健康个体的较高比例的ASE基因，并显示了与疾病有关的基因中由ASEQ提名的等位基因特异性事件。ASEQ可用于快速可靠地筛选大型NGS数据集，以识别等位基因特异性特征。它可以集成在任何NGS管道中，并在具有多个CPU的计算机系统上运行，CPU具有多个核心或跨机器集群。本文的在线版本（doi：10.1186/s12920-015-0084-2）包含补充材料，可供授权用户使用。
Single base level information from next-generation sequencing (NGS) allows for the quantitative assessment of biological phenomena such as mosaicism or allele-specific features in healthy and diseased cells. Such studies often present with computationally challenging burdens that hinder genome-wide investigations across large datasets that are now becoming available through the 1,000 Genomes Project and The Cancer Genome Atlas (TCGA) initiatives. We present ASEQ, a tool to perform gene-level allele-specific expression (ASE) analysis from paired genomic and transcriptomic NGS data without requiring paternal and maternal genome data. ASEQ offers an easy-to-use set of modes that transparently to the user takes full advantage of a built-in fast computational engine. We report its performances on a set of 20 individuals from the 1,000 Genomes Project and show its detection power on imprinted genes. Next we demonstrate high level of ASE calls concordance when comparing it to AlleleSeq and MBASED tools. Finally, using a prostate cancer dataset we report on a higher fraction of ASE genes with respect to healthy individuals and show allele-specific events nominated by ASEQ in genes that are implicated in the disease. ASEQ can be used to rapidly and reliably screen large NGS datasets for the identification of allele specific features. It can be integrated in any NGS pipeline and runs on computer systems with multiple CPUs, CPUs with multiple cores or across clusters of machines. The online version of this article (doi:10.1186/s12920-015-0084-2) contains supplementary material, which is available to authorized users.
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