WiggleTools: parallel processing of large collections of genome-wide datasets for visualization and statistical analysis.

WiggleTools: parallel processing of large collections of genome-wide datasets for visualization and statistical analysis.
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DOI:
10.1093/bioinformatics/btt737
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发表时间:
2014-04-01
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
通讯作者:
Flicek P
Flicek P
中科院分区:
其他
文献类型:
--
作者:
Zerbino DR;Johnson N;Juettemann T;Wilder SP;Flicek P

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动机: 利用高通量测序,研究人员现在正在生成数以百计的全基因组分析,以测量各种特征,如转录因子结合、组蛋白标记、dna甲基化或rna转录。显示如此多的数据通常会导致令人困惑的曲线图积累。我们在这里描述了一个多线程库,它计算大量数据集(Wigger、Bigwig、Bed、BigBed和BAM)的统计数据,在几分钟内生成统计摘要,而内存需求有限,无论是整个基因组还是选定的区域。可获得性和实现:代码可在Apache2.0许可下免费获得,网址为:www.githorb.com/enSembl/wigglTools联系方式:zerbino@ebi.ac.uk或Flicek@ebi.ac.uk
Motivation: Using high-throughput sequencing, researchers are now generating hundreds of whole-genome assays to measure various features such as transcription factor binding, histone marks, DNA methylation or RNA transcription. Displaying so much data generally leads to a confusing accumulation of plots. We describe here a multithreaded library that computes statistics on large numbers of datasets (Wiggle, BigWig, Bed, BigBed and BAM), generating statistical summaries within minutes with limited memory requirements, whether on the whole genome or on selected regions. Availability and Implementation: The code is freely available under Apache 2.0 license at www.github.com/Ensembl/Wiggletools Contact: zerbino@ebi.ac.uk or flicek@ebi.ac.uk
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影响因子: --
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