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peak identification for ChIP-seq data with no controls

Zoological Research 2012年第S03期33卷 121-128页

作者： Yanfeng ZHANG Bing SU State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoologythe Chinese Academy of SciencesKunming 650223China

Chromatin immunoprecipitation followed by sequencing(ChIP-seq)is increasingly being used for genome-wide profiling of transcriptional regulation,as this technique enables dissection of the gene regulatory *** input as control,a variety of statistical methods have been proposed for identifying the enriched regions in the genome,i.e.,the transcriptional factor binding sites and chromatin ***,when there are no controls,whether peak calling is still reliable awaits systematic *** address this question,we used a Bayesian framework approach to show the effectiveness of peak calling without controls(PCWC).Using several different types of ChIP-seq data,we demonstrated the relatively high accuracy of PCWC with less than a 5%false discovery rate(FDR).Compared with previously published methods,e.g.,the model-based analysis of ChIP-seq(MACS),PCWC is reliable with lower ***,to interpret the biological significance of the called peaks,in combination with microarray gene expression data,gene ontology annotation and subsequent motif discovery,our results indicate PCWC possesses a high ***,using in silico data,only a small number of peaks were identified,suggesting the significantly low FDR for PCWC.

关键词： ChIP-seq Bayesian peak calling Gene regulation

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RECOGNICER:A coarse-graining approach for identifying broad domains from ChIP-seq data

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Quantitative Biology 2020年第4期8卷 359-368页

作者： Chongzhi Zang Yiren Wang Weiqun Peng Center for Public Health Genomics University of VirginiaCharlottesvilleVA 22908USA Department of Public Health Sciences University of VirginiaCharlottesvilleVA 22908USA Department of Physics The George Washington UniversityWashingtonDC 20052USA

Background:Histone modifications are major factors that define chromatin states and have functions in regulating gene expression in eukaryotic *** immunoprecipitation coupled with high-throughput sequencing(ChIP-seq)technique has been widely used for profiling the genome-wide distribution of chromatin-associating protein *** histone modifications,such as H3K27me3 and H3K9me3,usually mark broad domains in the genome ranging from kilobases(kb)to megabases(Mb)long,resulting in diffuse patterns in the ChIP-seq data that are challenging for signal *** most existing ChIP-seq peak-calling algorithms are based on local statistical models without account of multi-scale features,a principled method to identify scale-free board domains has been ***:Here we present RECOGNICER(Recursive coarse-graining identification for ChIP-seq enriched regions),a computational method for identifying ChIP-seq enriched domains on a large range of *** algorithm is based on a coarse-graining approach,which uses recursive block transformations to determine spatial clustering of local enriched elements across multiple length ***:We apply RECOGNICER to call H3K27me3 domains from ChIP-seq data,and validate the results based on H3K27me3's association with repressive gene *** show that RECOGNICER outperforms existing ChIP-seq broad domain calling tools in identifying more whole domains than separated ***:RECOGNICER can be a useful bioinformatics tool for next-generation sequencing data analysis in epigenomics research.

关键词： coarse-graining ChIP-seq peak calling histone modification

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An introduction to computational tools for differential binding analysis with ChlP-seq data

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Frontiers of Electrical and Electronic Engineering in China 2017年第3期5卷 226-235页

作者： Shiqi Tu Zhen Shao CAS Key Laboratory of Computational Biology Collaborative Innovation Center for Genetics and Developmental Biology CAS- Max Planck Society Partner Institute for Computational Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai 200031 China. Graduate university of Chinese Academy of Sciences Beijing 100049 China

Background： Gene transcription in eukaryotie cells is collectively controlled by a large panel of ehromatin associated proteins and ChIP-seq is now widely used to locate their binding sites along the whole genome. Inferring the differential binding sites of these proteins between biological conditions by comparing the corresponding ChIP-seq samples is of general interest, yet it is still a computationally challenging task. Results： Here, we briefly review the computationhl tools developed in recent years for differential binding analysis with ChIP-seq data. The methods are extensively classified by their strategy of statistical modeling and s＇cope of application. Finally, a decision tree is presented for choosing proper tools based on the specific dataset. Conclusions： Computational tools for differential binding analysis with ChIP-seq data vary significantly with respect to their applicability and performance. This review can serve as a practical guide for readers to select appropriate tools for their own datasets.

关键词： ChIP-seq peak calling differential binding analysis computational tools

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Computational methodology for ChlP-seq analysis

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Frontiers of Electrical and Electronic Engineering in China 2013年第1期8卷 54-70页

作者： Hyunjin Shin Tao Liu Xikun Duan Yong Zhang X. Shirley Liu Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute/Harvard School of Public Health Boston MA 02115 USA Department of Bioinformatics School of Life Science and Technology Tongji University Shanghai 200092 China

Chromatin immunoprecipitation coupled with massive parallel sequencing （ChlP-seq） is a powerful technology to identify the genome-wide locations of DNA binding proteins such as transcription factors or modified histones. As more and more experimental laboratories are adopting ChlP-seq to unravel the transcriptional and epigenetic regulatory mechanisms, computational analyses of ChlP-seq also become increasingly comprehensive and sophisticated. In this article, we review current computational methodology for ChlP-seq analysis, recommend useful algorithms and workflows, and introduce quality control measures at different analytical steps. We also discuss how ChlP-seq could be integrated with other types of genomic assays, such as gene expression profiling and genome-wide association studies, to provide a more comprehensive view of gene regulatory mechanisms in important physiological and pathological processes.

关键词： Chromatin Signature Histone Mark Nucleosome Occupancy peak calling Target Gene Prediction

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peak identification for ChlP-seq data with no controls

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Zoological Research 2012年第6期33卷 I0059-I0066页

作者： Yanfeng ZHANG Bing SU State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoologythe Chinese Academy of SciencesKunming 650223China

Chromatin immtmoprecipitation followed by sequencing （ChlP-sec0 is increasingly being used for genome-wide profiling of transcriptional regulation, as this technique enables dissection of the gene regulatory networks. With input as control, a variety of statistical methods have been proposed for identifying the enriched regions in the genome, i.e., the transcriptional factor binding sites and chromatin modifications. However, when there are no controls, whether peak calling is still reliable awaits systematic evaluations. To address this question, we used a Bayesian framework approach to show the effectiveness of peak calling without controls （PCWC）. Using several different types of ChlP-seq data, we demonstrated the relatively high accuracy of PCWC with less than a 5% false discovery rate （FDR）. Compared with previously published methods, e.g., the model-based analysis of ChlP-seq （MACS）, PCWC is reliable with lower FDR. Furthermore, to interpret the biological significance of the called peaks, in combination with microarray gene expression data, gene ontology annotation and subsequent motif discovery, our results indicate PCWC possesses a high efficiency. Additionally, using in silico data, only a small number of peaks were identified, suggesting the significantly low FDR for PCWC.

关键词： ChIP-seq Bayesian peak calling Gene regulation

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