a global clustering algorithm to identify long intergenic non-coding rna - with applications in mouse macrophages全局聚类算法确定长基因间的非编码rna,应用程序在小鼠巨噬细胞.pdfVIP

A Global Clustering Algorithm to Identify Long Intergenic Non-Coding RNA - with Applications in Mouse Macrophages 1 2 3 3 3 Lana X. Garmire , David G. Garmire , Wendy Huang , Joyee Yao , Christopher K. Glass , Shankar Subramaniam1,3* 1 Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, La Jolla, California, United States of America, 2 Department of Electrical Engineering, University of Hawaii at Manoa, Honolulu, Hawaii, United States of America, 3 Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, California, United States of America Abstract Identification of diffuse signals from the chromatin immunoprecipitation and high-throughput massively parallel sequencing (ChIP-Seq) technology poses significant computational challenges, and there are few methods currently available. We present a novel global clustering approach to enrich diffuse CHIP-Seq signals of RNA polymerase II and histone 3 lysine 4 trimethylation (H3K4Me3) and apply it to identify putative long intergenic non-coding RNAs (lincRNAs) in macrophage cells. Our global clustering method compares favorably to the local clustering method SICER that was also designed to identify diffuse CHIP-Seq signals. The validity of the algorithm is confirmed at several levels. First, 8 out of a total of 11 selected putative lincRNA regions in primary macrophages respond to lipopolysaccharides (LPS) treatment as predicted by our computational method. Second, the genes nearest to lincRNAs are enriched with biological functions related to metabolic processes under resting co