生信菜鸟团 » Motif

用网页版工具ChIPseek来可视化CHIP-seq的peaks结果

ulwvfje — Thu, 07 Jul 2016 12:56:10 +0000

一般做完一个CHIP-seq测序，如果实验设计没有问题，测序质量也OK的话，很容易了根据序列call到符合要求的peaks，或者可以去很多文章或者roadmap里面下载到非常多有意义的peaks文件，一般是BED格式文件，这是就需要对这些peaks进行各种各样的注释以及可视化了，此时不得不强烈推荐一款网页版工具，是台湾学者开发的ChIPseek：

该工具首页就show了8张图片，就说明了该软件的功能：http://chipseek.cgu.edu.tw/index_show.py

该工具本质是就是后台调用 HOMER 和BEDTools, 这两个软件，使得那些不会编程的生物学家可以更方便快捷的理解自己的CHIP-seq结果，功能包括：

annotate the peaks
link to UCSC genome browser
provide pie charts, histograms and bar charts for peak location distribution
apply filter criteria by peak length to get a subset of peaks
apply filter criteria by distance to nearest TSS to get a subset of peaks
apply filter criteria by location of the peaks
apply filter criteria by list(s) of genes
apply filter criteria by GO terms
apply filter criteria by KEGG pathway annotations
compare two datasets
compare dataset with ENCODE transcription factor dataset
identify enriched motif
plot peaks on chromosome ideograms
allow users to download figures or tables

大部分功能自己写脚本也能实现，我就不多说了。

使用方法非常简单：

首先进入分析界面：http://chipseek.cgu.edu.tw/analysis_form.php

然后上传自己想要分析的peaks文件

比如GSE50177里面的GSE50177_RAW.tar：http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50177

我拿了四个peaks文件测试了一下：

提交任务后，文件就会上传，然后网页会给一个job ID号，如果你是在一个月之内看到这篇文章，你可以直接拿我的ID号去看结果，不需要自己上传自己的文件了，当然，你肯定是需要分析自己的peaks结果的。

ChIPseek is annotating your file(s).

This page will automatically refresh every 60 seconds.

Alternatively, You may use the job ID: 1467890358.407 to visit ChIPseek latter.

一会儿就可以看到结果了，因为网页版工具的服务器容量有限，所以这个结果一个月内是有效的。

http://chipseek.cgu.edu.tw/main_menu.py?job_id=1467890358.407

GSM1278641_Xu_MUT_rep1_BAF155_MUT (a total of 6733 peaks) (Download all annotation results)

GSM1278643_Xu_MUT_rep2_BAF155_MUT (a total of 3625 peaks) (Download all annotation results)

GSM1278645_Xu_WT_rep1_BAF155 (a total of 10987 peaks) (Download all annotation results)

GSM1278647_Xu_WT_rep2_BAF155 (a total of 5225 peaks) (Download all annotation results)

把每个文件的每个peaks都注释了，而且提供带链接的下载结果，tab分割的纯文本文件，用excel打开可能看起来舒服一点

还有4个可视化图片是我们可能会比较感兴趣的：

Peak location (pie chart)

Peak location (bar chart)

Distance to TSS

Peak length distribution

以及它可以把我们上传的bed格式peaks区域文件转为fasta序列 Peak sequences

本质是根据坐标从参考基因组里面提取序列而已，我把所有的序列都下载下来了，可以用来直接做motif查找

$ ls -lh *fasta

-rw-r–r– 1 Jimmy 197121 18M Jul 7 19:40 GSM1278641_Xu_MUT_rep1_BAF155_MUT_sequence.fasta

-rw-r–r– 1 Jimmy 197121 9.9M Jul 7 19:38 GSM1278643_Xu_MUT_rep2_BAF155_MUT_sequence.fasta

-rw-r–r– 1 Jimmy 197121 26M Jul 7 19:41 GSM1278645_Xu_WT_rep1_BAF155_sequence.fasta

-rw-r–r– 1 Jimmy 197121 14M Jul 7 19:41 GSM1278647_Xu_WT_rep2_BAF155_sequence.fasta

自学CHIP-seq分析第二讲~学习资料的搜集

ulwvfje — Tue, 05 Jul 2016 00:20:00 +0000

我只能说，CHIP-seq的确是非常完善的NGS流程了，各种资料层出不穷，大家首先可以看下面几个完整流程的PPT来对CHIP-seq流程有个大致的印象，我对前面提到的文献数据处理的几个要点，就跟下面这个图片类似：

QuEST is a statistical software for analysis of ChIP-Seq data with data and analysis results visualization through UCSC Genome Browser. http://www-hsc.usc.edu/~valouev/QuEST/QuEST.html

peak calling 阈值的选择： http://www.nature.com/nprot/journal/v7/n1/fig_tab/nprot.2011.420_F2.html

MeDIP-seq and histone modification ChIP-seq analysis http://crazyhottommy.blogspot.com/2014/01/medip-seq-and-histone-modification-chip.html

2011-review-CHIP-seq-high-quaility-data: http://www.nature.com/ni/journal/v12/n10/full/ni.2117.html?message-global=remove

不同处理条件的CHIP-seq的差异peaks分析： http://www.slideshare.net/thefacultyl/diffreps-automated-chipseq-differential-analysis-package

一个实际的CHIP-seq数据分析例子： http://www.biologie.ens.fr/~mthomas/other/chip-seq-training/

http://biow.sb-roscoff.fr/ecole_bioinfo/training_material/chip-seq/documents/presentation_chipseq.pdf

http://ecole-bioinfo-aviesan.sb-roscoff.fr/sites/ecole-bioinfo-aviesan.sb-roscoff.fr/files/files/chipseq_CarlHerrmann_Roscoff2015.pdf

http://ecole-bioinfo-aviesan.sb-roscoff.fr/sites/ecole-bioinfo-aviesan.sb-roscoff.fr/files/files/defrance-ChIP-seq_annotation.pdf

然后下面的各种资料，是针对CHIP-seq流程的各个环境的，还有一些是针对于表观遗传学知识的

## ppt : http://159.149.160.51/epigen_milano/epigen_barozzi.pdf

## best practise: http://bioinformatics-core-shared-training.github.io/cruk-bioinf-sschool/

## pipeline : https://github.com/shenlab-sinai/chip-seq_preprocess

## https://sites.google.com/site/anshul...e/projects/idr ## samtools view -b -F 1548 -q 30 chipSampleRep1.bam

## pipeline : http://daudin.icmb.utexas.edu/wiki/index.php/ChIPseq_prep_and_map

## pipeline : https://github.com/BradyLab/ChipSeq/blob/master/chipseq.sh

## https://github.com/crukci-bioinformatics/chipseq-pipeline

## https://github.com/ENCODE-DCC/chip-seq-pipeline

## Hands-on introduction to ChIP-seq analysis - VIB Training http://www.biologie.ens.fr/~mthomas/other/chip-seq-training/

## video(A Step-by-Step Guide to ChIP-Seq Data Analysis Webinar) : http://www.abcam.com/webinars/a-step-by-step-guide-to-chip-seq-data-analysis-webinar

## Using ChIP-Seq to identify and/or quantify bound regions (peaks) http://barcwiki.wi.mit.edu/wiki/SOPs/chip_seq_peaks

## http://jura.wi.mit.edu/bio/education/hot_topics/ChIPseq/ChIPSeq_HotTopics.pdf

## http://pedagogix-tagc.univ-mrs.fr/courses/ASG1/practicals/chip-seq/mapping_tutorial.html

## 公开课： https://www.coursera.org/learn/galaxy-project/lecture/FUzcg/chip-sequence-analysis-with-macs

##ＥＢＩ的教程：https://www.ebi.ac.uk/training/online/course/ebi-next-generation-sequencing-practical-course/chip-seq-analysis/chip-seq-practical

## 日语教程：http://genomejack.net/download/GenomeJackBrowserAppendix/browser_appendix_j/tutorials/chipSeq.html

## 台湾教程：http://lsl.sinica.edu.tw/Services/Class/files/20151118475_2.pdf 徐唯哲 Paul Wei-Che HSU

中央研究院分子生物研究所

研究助技師

## peak finder软件大全： http://wodaklab.org/nextgen/data/peakfinders.html

## https://www.encodeproject.org/documents/049704a4-5c58-4631-acf1-4ef152bdb3ef/@@download/attachment/Learning_Chromatin_States_from_ChIP-seq_data.pdf

## https://bioshare.bioinformatics.ucdavis.edu/bioshare/download/47aq5pp5mzza5vb/PDFs/Tuesday_MB_ChIP-Seq_Intro.pdf

## paper： Large-Scale Quality Analysis of Published ChIP-seq Data http://www.g3journal.org/content/4/2/209.full

## paper： Chip-seq data analysis: from quality check to motif discovery and more http://ccg.vital-it.ch/var/sib_april15/cases/landt12/strand_correlation.html

## Workshop hands on session(RNA-Seq / ChIP-Seq ) : https://hpc.oit.uci.edu/biolinux/handson.docx

## http://www.gqinnovationcenter.com/documents/bioinformatics/ChIPseq.pptx

## paper supplement : http://genome.cshlp.org/content/suppl/2015/10/02/gr.192005.115.DC1/Supplemental_Information.docx

http://www.illumina.com/documents/products/datasheets/datasheet_chip_sequence.pdf

http://www.ncbi.nlm.nih.gov/pubmed/22130887 "Analyzing ChIP-seq data: preprocessing, normalization, differential identification, and binding pattern characterization."

http://www.ncbi.nlm.nih.gov/pubmed/22499706 "Normalization, bias correction, and peak calling for ChIP-seq." (stat heavy)

http://www.ncbi.nlm.nih.gov/pubmed/24244136 "Practical guidelines for the comprehensive analysis of ChIP-seq data."

http://www.ncbi.nlm.nih.gov/pubmed/25223782 "Identifying and mitigating bias in next-generation sequencing methods for chromatin biology."

A quick search also turned up this recent paper (which I haven't read) that might be of interest to you

http://www.ncbi.nlm.nih.gov/pubmed/24598259 "Impact of sequencing depth in ChIP-seq experiments."

## figures: https://github.com/shenlab-sinai/ngsplot

https://github.com/daler/metaseq

http://liulab.dfci.harvard.edu/CEAS/usermanual.html

还有两个ｗｅｂ－ｔｏｏｌｓ也是可视化

bioconductor系列工具和教程 :

http://faculty.ucr.edu/~tgirke/HTML_Presentations/Manuals/Workshop_Dec_6_10_2012/Rchipseq/Rchipseq.pdf

http://bioinformatics-core-shared-training.github.io/cruk-bioinf-sschool/Day4/chipqc_sweave.pdf

http://bioconductor.org/packages/release/bioc/html/chipseq.html

http://bioconductor.org/help/workflows/chipseqDB/

http://bioconductor.org/help/workflows/generegulation/

http://bioconductor.org/help/course-materials/2009/EMBLJune09/Practicals/chipseq/BasicChipSeq.pdf

## 公司教程： http://www.partek.com/Tutorials/microarray/Tiling/ChipSeqTutorial.pdf

自学CHIP-seq分析第一讲~文献选择与解读

ulwvfje — Tue, 05 Jul 2016 00:14:58 +0000

文章：CARM1 Methylates Chromatin Remodeling Factor BAF155 to Enhance Tumor Progression and Metastasis

我很早以前想自学CHIP-seq的时候就关注过这篇文章，那时候懂得还不多，甚至都没有仔细看这篇文章就随便下载了数据进行分析，也只是跑一些软件而已，这次仔细阅读这篇文章才发现里面的门道很多，尤其是CHIP-seq的实验基础，以及表观遗传学的生物学基础知识，我有时间一定要把这篇文章翻译一下。学习这篇文章前一定要温习一些生物学知识，见我上一篇博客

作者首先实验证明了用small haripin RNA来knockout CARM1 只能达到90%的敲除效果，有趣的是，对CARM1的功能影响非常小，说明只需要极地量的CARM1就可以很好的发挥作用，所以作者设计了100%敲除CARM1的实验材料，通过zinc finger nuclease这种基因组编辑技术( 缩写成ZFN技术)。

这样就能比较CARM1有无的机体种各种蛋白被催化状态了，其中SWI/SNF(BAF) chromatin remodeling complex 染色质重构复合物的一个亚基 BAF155，非常明显的只有在CARM1这个基因完好无损的细胞系里面才能被正常的甲基化。作者证明了BAF155是CARM1这个基因非常好(拉丁语 bona fide)的一个底物，而且通过巧妙的实验设计，证明了BAF155这个蛋白的第1064位氨基酸(R) 是 CARM1的作用位点。

因为早就有各种文献说明了SWI/SNF(BAF) chromatin remodeling complex 染色质重构复合物在癌症的重要作用，所以作者也很自然的想探究BAF155在癌症的功能详情。这里作者选择的是CHIP-seq技术，因为BAF155是转录因子的一种。（转录因子(transcription factor)是一群能与基因5`端上游特定序列专一性结合，从而保证目的基因以特定的强度在特定的时间与空间表达的蛋白质分子。）CHIP-seq技术最适合来探究BAF155这样转录因子的功能了，所以作者构造了一种细胞系（MCF7），它的BAF155这个蛋白的第1064位氨基酸(R) 突变了，这样就无法被CARM1这个基因催化而甲基化，然后比较突变的细胞系和野生型细胞系的BAF155的CHIP-seq结果，这样就可以研究BAF155这个转录因子，是否必须要被CARM1这个基因催化而甲基化后才能行使生物学功能。

作者用me-BAF155特异性抗体+western bloting 证明了正常的野生型MCF7细胞系里面有~74%的BAF155是被甲基化的！

有一个细胞系SKOV3，可以正常表达除了BAF155之外的其余14种SWI/SNF(BAF) chromatin remodeling complex 染色质重构复合物，而不管是把突变的细胞系和野生型细胞系的BAF155混在里面都可以促进染色质重构复合物的组装，所以甲基化与否并不影响这个染色质重构复合物的组装，我们重点应该研究的是甲基化会影响BAF155在基因组其它地方结合。

结果是，突变的细胞系和野生型细胞系种BAF155在基因组结合位置(peaks)还是有较大的overlap的，重点是看它们的peaks在各种基因组区域(基因上下游，5,3端UTR，启动子，内含子，外显子，基因间区域，microRNA区域)分布情况的差别，还有它们举例转录起始位点的距离的分布区别，还有它们注释到的基因区别，已经基因富集到什么通路，等等这样的分析。

虽然作者在人的细胞系(MCF7)上面做CHIP-seq，但是在老鼠细胞系(MDA-MB-231)做了mRNA芯片数据分析,BAF155这个蛋白的第1064位氨基酸(R) 突变细胞系，和野生型细胞系，用的是Affymetrix HG U133 Plus 2.0这个常用平台

which was hybridized to Affymetrix HG U133 Plus 2.0 microarrays containing 54,675 probesets for >47,000 transcripts and variants, including 38,500 human genes.

To identify genes differentially expressed between MDA-MB-231-BAF155WT and MDA-MB-231-BAF155R1064K

表达矩阵可以下载：## http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4004525/bin/NIHMS556863-supplement-03.xlsx

我简单摘抄作者的对CHIP-seq数据的生物信息学分析结果

## All samples were mapped from fastq files using BOWTIE [-m 1 -- best] to mm9 [UCSCmouse genome build 9]

## Sequences were mapped to the human genome (hg19) using BOWTIE (--best –m 1) to yield unique alignments

## Peaks were called by using HOMER [http://biowhat.ucsd.edu/homer/] and QuEST [http://mendel.stanford.edu/sidowlab/downloads/quest/].

QuEST 2.4 (Valouev et al., 2008) was run using the recommend settings for transcription factor (TF) like binding with the following exceptions:

kde_bandwith=30, region_size=600, ChIP threshold=35, enrichment fold=3, rescue fold=3.

HOMER (Heinz et al., 2010) analysis was run using the default settings for peak finding.

False Discovery Rate (FDR) cut off was 0.001 (0.1%) for all peaks.

The tag density for each factor was normalized to 1x107 tags and displayed using the UCSC genome browser.

Motif analysis (de novo and known), was performed using the HOMER software and Genomatix.

Peak overlaps were processed with HOMER and Galaxy (Giardine et al., 2005).

Peak comparisons between replicates were processed with EdgeR statistical package in R

也就是我们接下来需要学习的流程化分析步骤，下面我给一个主要流程的截图，但是主要还是要看实验是如何设计的，也有一个文章发表关于CHIP-seq的流程的：http://biow.sb-roscoff.fr/ecole_bioinfo/protected/jacques.van-helden/ThomasChollier_NatProtoc_2012_peak-motifs.pdf

同时我还推荐大家看几篇相关文献：

Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. http://www.nature.com/nature/journal/v448/n7153/pdf/nature06008.pdf

Mapping and analysis of chromatin state dynamics in nine human cell types(GSE26386): http://www.nature.com/nature/journal/v473/n7345/full/nature09906.html

Promiscuous RNA binding by Polycomb Repressive Complex 2 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3823624/pdf/nihms517229.pdf