Target analysis by integration of transcriptome and ChIP-seq data with BETA
Su Wang,Hanfei Sun,Jian Ma,Chongzhi Zang,Chenfei Wang,Juan Wang,Qianzi Tang,Clifford A. Meyer,Yong Zhang,X. Shirley Liu +9 more
TLDR
BETA is a software package that integrates ChIP-seq of TFs or chromatin regulators with differential gene expression data to infer direct target genes and identifies the motif of the factor and its collaborators, which might modulate the factor's activating or repressive function.Abstract:
The combination of ChIP-seq and transcriptome analysis is a compelling approach to unravel the regulation of gene expression. Several recently published methods combine transcription factor (TF) binding and gene expression for target prediction, but few of them provide an efficient software package for the community. Binding and expression target analysis (BETA) is a software package that integrates ChIP-seq of TFs or chromatin regulators with differential gene expression data to infer direct target genes. BETA has three functions: (i) to predict whether the factor has activating or repressive function; (ii) to infer the factor's target genes; and (iii) to identify the motif of the factor and its collaborators, which might modulate the factor's activating or repressive function. Here we describe the implementation and features of BETA to demonstrate its application to several data sets. BETA requires ~1 GB of RAM, and the procedure takes 20 min to complete. BETA is available open source at http://cistrome.org/BETA/.read more
Citations
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A survey of best practices for RNA-seq data analysis
Ana Conesa,Pedro Madrigal,Pedro Madrigal,Sonia Tarazona,David Gomez-Cabrero,Alejandra Cervera,Andrew McPherson,Michał Wojciech Szcześniak,Daniel J. Gaffney,Laura L. Elo,Xuegong Zhang,Ali Mortazavi +11 more
TL;DR: All of the major steps in RNA-seq data analysis are reviewed, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping.
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A major chromatin regulator determines resistance of tumor cells to T cell–mediated killing
Deng Pan,Aya Kobayashi,Aya Kobayashi,Peng Jiang,Lucas Ferrari de Andrade,Rong En Tay,Adrienne M. Luoma,Daphne Tsoucas,Xintao Qiu,Klothilda Lim,Prakash Rao,Henry W. Long,Guo-Cheng Yuan,John G. Doench,Myles Brown,X. Shirley Liu,Kai W. Wucherpfennig +16 more
TL;DR: A genome-scale CRISPR-Cas9 screen was used to identify mechanisms of tumor cell resistance to killing by cytotoxic T cells, the central effectors of antitumor immunity.
Journal ArticleDOI
Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis.
Rongbin Zheng,Changxin Wan,Shenglin Mei,Qian Qin,Qiu Wu,Hanfei Sun,Chen-Hao Chen,Myles Brown,Xiaoyan Zhang,Clifford A. Meyer,X. Shirley Liu,X. Shirley Liu +11 more
TL;DR: The Cistrome DB has a new Toolkit module with several features that allow users to better utilize the large-scale ChIP-seq, DNase-seq and ATAC-seq data, and the new tools will greatly benefit the biomedical research community.
Journal ArticleDOI
Cistrome Data Browser: a data portal for ChIP-Seq and chromatin accessibility data in human and mouse
Shenglin Mei,Qian Qin,Qiu Wu,Hanfei Sun,Rongbin Zheng,Chongzhi Zang,Muyuan Zhu,Jiaxin Wu,Xiaohui Shi,Len Taing,Tao Liu,Myles Brown,Clifford A. Meyer,X. Shirley Liu +13 more
TL;DR: The Cistrome database is built, a collection of ChIP-seq and chromatin accessibility data published before January 1, 2016, including 13 366 human and 9953 mouse samples that are expected to become a valuable resource for transcriptional and epigenetic regulation studies.
Journal ArticleDOI
Cross-tissue organization of the fibroblast lineage.
Matthew B. Buechler,Rachana N. Pradhan,Akshay T. Krishnamurty,Christian Cox,Aslihan Karabacak Calviello,Amber W. Wang,Yeqing Angela Yang,Lucinda Tam,Roger Caothien,Merone Roose-Girma,Zora Modrusan,Joseph R. Arron,Richard Bourgon,Sören Müller,Shannon J. Turley +14 more
TL;DR: In this paper, a cross-species and pan-tissue approach to transcriptomics at single-cell resolution has identified key organizing principles of the fibroblast lineage in health and disease.
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