CLIMP: Clustering Motifs via Maximal Cliques with Parallel Computing Design.
CLIMP: Clustering Motifs via Maximal Cliques with Parallel Computing Design.
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CLIMP：通过并行计算设计的最大派系对主题进行聚类
DOI：
10.1371/journal.pone.0160435
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Chen Y
中科院分区：
文献类型：
--
作者：
Zhang S;Chen Y
转录因子识别的一组保守结合位点被称为基序，可以通过比较基因组学的许多应用来识别过度代表的片段。此外，当从全基因组数据集合中预测了许多假定的基序时，它们的相似性数据可以表示为一个大图，其中这些基序彼此相连。然而，需要一种有效的聚类算法来聚类属于同一组的基元，分离属于不同组的基元，甚至删除大量的虚假基元。本文提出了一种新的基序聚类算法clip，该算法利用最大团块，并通过并行化程序来加快算法的速度。利用JASPAR数据库的合成基序数据集、系统发育足迹数据集的假设基序数据集和ChIP数据集的假设基序数据集，比较了CLIMP算法和其他两种高性能算法在基序聚类方面的性能，结果表明，在三种数据集上，CLIMP算法在基序聚类方面的性能明显优于这两种算法，因此它可以作为一些全基因组基序预测管道聚类程序的有益补充。clip可在http://sqzhang.cn/climp.html上获得。
A set of conserved binding sites recognized by a transcription factor is called a motif, which can be found by many applications of comparative genomics for identifying over-represented segments. Moreover, when numerous putative motifs are predicted from a collection of genome-wide data, their similarity data can be represented as a large graph, where these motifs are connected to one another. However, an efficient clustering algorithm is desired for clustering the motifs that belong to the same groups and separating the motifs that belong to different groups, or even deleting an amount of spurious ones. In this work, a new motif clustering algorithm, CLIMP, is proposed by using maximal cliques and sped up by parallelizing its program. When a synthetic motif dataset from the database JASPAR, a set of putative motifs from a phylogenetic foot-printing dataset, and a set of putative motifs from a ChIP dataset are used to compare the performances of CLIMP and two other high-performance algorithms, the results demonstrate that CLIMP mostly outperforms the two algorithms on the three datasets for motif clustering, so that it can be a useful complement of the clustering procedures in some genome-wide motif prediction pipelines. CLIMP is available at http://sqzhang.cn/climp.html.
影响因子：
3
作者：
Zhang S;Li S;Pham PT;Su Z
通讯作者：
Su Z
影响因子：
14.9
作者：
Elo LL;Kallio A;Laajala TD;Hawkins RD;Korpelainen E;Aittokallio T
通讯作者：
Aittokallio T