MuSERA: Multiple Sample Enriched Region Assessment
MuSERA: Multiple Sample Enriched Region Assessment
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DOI：
10.1093/bib/bbw029
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发表时间：
2017-05-01
影响因子：
9.5
通讯作者：
Masseroli, Marco
中科院分区：
文献类型：
--
作者：
Jalili, Vahid;Matteucci, Matteo;Masseroli, Marco
富集区（ER）鉴定是几种下一代测序（NGS）实验类型中的基本步骤。然而，尽管NGS实验方案建议为每种评价条件制备重复样品，并且通常评估其一致性，但用于ER鉴定的管道通常不考虑可用的NGS重复样品。这可能会改变ER的全基因组描述，阻碍对检测到的ER进行后续分析的意义，并最终排除重复证据可以支持的生物学发现。MuSERA是一个广泛有用的独立工具，用于对来自ER的多个ChIP-seq或DNase-seq重复的组合证据进行交互式和批量分析。除了严格组合样本重复以增加检测到的ER的统计显著性之外，它还提供定量评估和图形特征以评估每个确定的ER集在其基因组背景下的生物相关性;它们包括确定的ER的基因组注释、最近的ER距离分布、ER的全局相关性评估和集成的基因组浏览器。我们回顾MuSERA的原理和实施，并说明如何通过将MuSERA应用于几种类型的NGS数据的重复来扩展重要的ER集，包括转录因子或组蛋白标记的ChIP-seq和DNase-seq超敏位点。我们表明，MuSERA可以通过局部组合重复的证据来确定每个样本的一组新的增强ER，并证明MuSERA提供的易于使用的交互式图形显示和定量评估如何有效地支持对所得结果的彻底检查和对其生物学内容的评估，促进其理解和生物学解释。MuSERA可在http：//www.生物信息学deib.polimi.it/MuSERA/.
Enriched region (ER) identification is a fundamental step in several next-generation sequencing (NGS) experiment types. Yet, although NGS experimental protocols recommend producing replicate samples for each evaluated condition and their consistency is usually assessed, typically pipelines for ER identification do not consider available NGS replicates. This may alter genome-wide descriptions of ERs, hinder significance of subsequent analyses on detected ERs and eventually preclude biological discoveries that evidence in replicate could support. MuSERA is a broadly useful stand-alone tool for both interactive and batch analysis of combined evidence from ERs in multiple ChIP-seq or DNase-seq replicates. Besides rigorously combining sample replicates to increase statistical significance of detected ERs, it also provides quantitative evaluations and graphical features to assess the biological relevance of each determined ER set within its genomic context; they include genomic annotation of determined ERs, nearest ER distance distribution, global correlation assessment of ERs and an integrated genome browser. We review MuSERA rationale and implementation, and illustrate how sets of significant ERs are expanded by applying MuSERA on replicates for several types of NGS data, including ChIP-seq of transcription factors or histone marks and DNase-seq hypersensitive sites. We show that MuSERA can determine a new, enhanced set of ERs for each sample by locally combining evidence on replicates, and prove how the easy-to-use interactive graphical displays and quantitative evaluations that MuSERA provides effectively support thorough inspection of obtained results and evaluation of their biological content, facilitating their understanding and biological interpretations. MuSERA is freely available at http://www. bioinformatics. deib.polimi.it/MuSERA/.