Hypergeometric analysis of tiling-array and sequence data: detection and interpretation of peaks.

Q2 Biochemistry, Genetics and Molecular Biology
Advances and Applications in Bioinformatics and Chemistry Pub Date : 2013-10-25 eCollection Date: 2013-01-01 DOI:10.2147/AABC.S51271
Erdogan Taskesen, Remco Hoogeboezem, Ruud Delwel, Marcel Jt Reinders
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引用次数: 2

Abstract

Probing protein-deoxyribonucleic acid (DNA) is gaining popularity as it sheds light on molecular mechanisms that regulate the expression of genes. Currently, tiling-arrays and next-generation sequencing technology can be used to measure these interactions. Both methods generate a signal over the genome in which contiguous regions of peaks on the genome represent the presence of an interacting molecule. Many methods do exist to identify functional regions of interest (ROIs) on the genome. However the detection of ROIs are often not an end-point in research questions and it therefore requires data dragging between tools to relate the ROIs to information present in databases, such as gene-ontology, pathway information, or enrichment of certain genomic content. We introduce hypergeometric analysis of tiling-array and sequence data (HATSEQ), a powerful tool that accurately identifies functional ROIs on the genome where a genomic signal significantly deviates from the general genome-wide behavior. HATSEQ also includes a number of built-in post-analyses with which biological meaning can be attached to the detected ROIs in terms of gene pathways and de-novo motif analysis, and provides different visualizations and statistical summaries for the detected ROIs. In addition, HATSEQ has an intuitive graphic user interface that lowers the barrier for researchers to analyze their data without the need of scripting languages. We compared the results of HATSEQ against two other popular chromatin immunoprecipitation sequencing (ChIP-Seq) methods and observed overlap in the detected ROIs but HATSEQ is more specific in delineating the peak boundaries. We also discuss the versatility of HATSEQ by using a Signal Transducer and Activator of Transcription 1 (STAT1) ChIP-Seq data-set, and show that the detected ROIs are highly specific for the expected STAT1 binding motif. HATSEQ is freely available at: http://hema13.erasmusmc.nl/index.php/HATSEQ.

Abstract Image

Abstract Image

Abstract Image

叠片阵列和序列数据的超几何分析:峰的检测和解释。
探测蛋白质脱氧核糖核酸(DNA)越来越受欢迎,因为它揭示了调节基因表达的分子机制。目前,平铺阵列和下一代测序技术可用于测量这些相互作用。这两种方法产生的信号在基因组上的连续区域峰基因组表示相互作用分子的存在。许多方法确实存在,以确定感兴趣的功能区域(roi)的基因组。然而,roi的检测通常不是研究问题的终点,因此需要在工具之间拖动数据,将roi与数据库中存在的信息联系起来,例如基因本体,途径信息或某些基因组内容的丰富。我们介绍了平铺阵列和序列数据的超几何分析(HATSEQ),这是一个强大的工具,可以准确识别基因组上的功能roi,其中基因组信号显着偏离了一般的全基因组行为。HATSEQ还包括许多内置的后期分析,可以根据基因途径和去novo基序分析将生物学意义附加到检测到的roi上,并为检测到的roi提供不同的可视化和统计摘要。此外,HATSEQ具有直观的图形用户界面,降低了研究人员在不需要脚本语言的情况下分析数据的障碍。我们将HATSEQ的结果与另外两种流行的染色质免疫沉淀测序(ChIP-Seq)方法进行了比较,发现检测到的roi有重叠,但HATSEQ在描绘峰边界方面更具体。我们还通过使用信号传感器和转录激活器1 (STAT1)芯片- seq数据集讨论了HATSEQ的多功能性,并表明检测到的roi对预期的STAT1结合基序具有高度特异性。HATSEQ可在http://hema13.erasmusmc.nl/index.php/HATSEQ免费获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advances and Applications in Bioinformatics and Chemistry
Advances and Applications in Bioinformatics and Chemistry Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (miscellaneous)
CiteScore
6.50
自引率
0.00%
发文量
7
审稿时长
16 weeks
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