GibbsCluster: unsupervised clustering and alignment of peptide sequences
GibbsCluster: unsupervised clustering and alignment of peptide sequences
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DOI：
10.1093/nar/gkx248
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发表时间：
2017-07-03
影响因子：
14.9
通讯作者：
Nielsen, Morten
中科院分区：
文献类型：
--
作者：
Andreatta, Massimo;Alvarez, Bruno;Nielsen, Morten
受体与短线性肽片段（配体）的相互作用是许多生物信号传导过程的基础。保守和信息丰富的氨基酸模式，通常称为序列基序，形状和调节这些相互作用。由于受体-配体系统或用于询问它的测定的性质，实验数据通常包含多个序列基序。GibbsCluster是无监督基序发现的强大工具，因为它可以同时聚类和比对肽数据。这里介绍的GibbsCluster 2.0是一个改进的版本，它结合了插入和缺失，解释了肽输入中基序长度的变化。基本上，该程序将一组肽序列作为输入，并将它们聚类成有意义的组。它返回它识别的最佳聚类数，以及序列比对和表征每个聚类的序列基序。有几个参数可用于自定义聚类分析，包括针对小聚类和重叠组的可调整惩罚，以及用于删除离群值的垃圾聚类。作为一个示例应用程序，我们使用服务器来解卷积由质谱法生成的大规模肽组数据中的多种特异性。该服务器位于http://www.cbs.dtu.dk/services/GibbsCluster-2.0。
Receptor interactions with short linear peptide fragments (ligands) are at the base of many biological signaling processes. Conserved and information-rich amino acid patterns, commonly called sequence motifs, shape and regulate these interactions. Because of the properties of a receptor-ligand system or of the assay used to interrogate it, experimental data often contain multiple sequence motifs. GibbsCluster is a powerful tool for unsupervised motif discovery because it can simultaneously cluster and align peptide data. The GibbsCluster 2.0 presented here is an improved version incorporating insertion and deletions accounting for variations in motif length in the peptide input. In basic terms, the program takes as input a set of peptide sequences and clusters them into meaningful groups. It returns the optimal number of clusters it identified, together with the sequence alignment and sequence motif characterizing each cluster. Several parameters are available to customize cluster analysis, including adjustable penalties for small clusters and overlapping groups and a trash cluster to remove outliers. As an example application, we used the server to deconvolute multiple specificities in large-scale peptidome data generated by mass spectrometry. The server is available at http://www.cbs.dtu.dk/services/GibbsCluster-2.0.