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一篇文章学会ChIP-seq分析(上)

2017-05-10 jimmy 生信技能树

写在前面:《一篇文章学会ChIP-seq分析(上)》《一篇文章学会ChIP-seq分析(下)》为生信菜鸟团博客相关文章合集,共九讲内容。带领你从相关文献解读、资料收集和公共数据下载开始,通过软件安装、数据比对、寻找并注释peak、寻找motif等ChIP-seq分析主要步骤入手学习,最后还会介绍相关可视化工具。

第一讲:文献选择与解读

文献;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门道很多,尤其是ChIP-seq的实验基础和表观遗传学的生物学基础知识。

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,说明只需要极少量的CARM1就可以发挥很好的作用,因此作者通过zinc finger nuclease这种基因组编辑技术设计了100%敲除CARM1的实验材料。(当然,现在有更好的基因编辑技术啦)

这样就能比较CARM1有无时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, 所以作者也很自然想探究BAF155在癌症的功能详情,这里作者选择的是ChIP-seq技术。BAF155是作为SWI/SNF(BAF) chromatin remodeling complex 染色质重构复合物的一个组分,必然neng 直接或者间接的结合DNA咯而ChIP-seq技术最适合来探究能直接或者间接结合DNA的蛋白的功能,所以作者构造了一种细胞系(MCF7),它的BAF155蛋白的第1064位氨基酸(R) 突变而无法被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的两个ChIP-seq结果,这样就可以研究BAF155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被甲基化。

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,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。

结果显示,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,还有它们距离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。

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

表达矩阵下载地址:

我简单摘抄作者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 [] and QuEST [].

用到的软件有

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

    kdebandwith=30, regionsize=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

以上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,但主要是实验是如何设计

这里有一个文章发表了关于CHIP-seq的流程的:

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. 

  • Mapping and analysis of chromatin state dynamics in nine human cell types(GSE26386): 

  • Promiscuous RNA binding by Polycomb Repressive Complex 2 


第二讲:资料收集

CHIP-seq的确是非常完善的NGS流程,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. ~valouev/QuEST/QuEST.html

  • peak calling 阈值的选择: 

  • MeDIP-seq and histone modification ChIP-seq analysis 

  • 2011-review-CHIP-seq-high-quaility-data: 

  • 不同处理条件的CHIP-seq的差异peaks分析: 

  • 一个实际的CHIP-seq数据分析例子: ~mthomas/other/chip-seq-training/

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

  • ppt : 

  • best practise: 

  • pipeline : 

  •  ## samtools view -b -F 1548 -q 30 chipSampleRep1.bam

  • pipeline : 

  • pipeline : 

  • Hands-on introduction to ChIP-seq analysis - VIB Training ~mthomas/other/chip-seq-training/

  • video(A Step-by-Step Guide to ChIP-Seq Data Analysis Webinar) : 

  • Using ChIP-Seq to identify and/or quantify bound regions (peaks)

  • 公开课: 

  • EBI的教程:

  • 台湾教程: 徐唯哲 Paul Wei-Che HSU

  • peak finder软件大全: 

  • paper: Large-Scale Quality Analysis of Published ChIP-seq Data 

  • paper: Chip-seq data analysis: from quality check to motif discovery and more 

  • Workshop hands on session(RNA-Seq / ChIP-Seq ) : 

  • paper supplement : 

  •  "Analyzing ChIP-seq data: preprocessing, normalization, differential identification, and binding pattern characterization."

  •  "Normalization, bias correction, and peak calling for ChIP-seq." (stat heavy)

  •  "Practical guidelines for the comprehensive analysis of ChIP-seq data."

  •  "Identifying and mitigating bias in next-generation sequencing methods for chromatin biology."

  •  "Impact of sequencing depth in ChIP-seq experiments."

  • figures: 

可视化工具

bioconductor系列工具和教程 :

  • ~tgirke/HTMLPresentations/Manuals/WorkshopDec610_2012/Rchipseq/Rchipseq.pdf

公司教程


第三讲:公共数据下载

这一步跟自学其它高通量测序数据处理一样,就是仔细研读paper,在里面找到作者把原始测序数据放在了哪个公共数据库里面,一般是NCBI的GEO,SRA,本文也不例外,然后解析样本数,找到下载链接规律。

  1. ## step1 : download raw data

  2. > cd ~

  3. > mkdir CHIPseq_test && cd CHIPseq_test

  4. > mkdir rawData && cd rawData

  5. > ## batch download the raw data by shell script :

  6. > for ((i=593;i<601;i++)) ;do wget [ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByStudy/sra/SRP/SRP033/SRP033492/SRR1042](ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByStudy/sra/SRP/SRP033/SRP033492/SRR1042)$i/SRR1042$i.sra;done

很容易就下载了8个测序文件,每个样本的数据大小,测序量如下

  1. > 621M Jun 27 14:03 SRR1042593.sra (16.9M reads)

  2. > 2.2G Jun 27 15:58 SRR1042594.sra (60.6M reads)

  3. > 541M Jun 27 16:26 SRR1042595.sra (14.6M reads)

  4. > 2.4G Jun 27 18:24 SRR1042596.sra (65.9M reads)

  5. > 814M Jun 27 18:59 SRR1042597.sra (22.2M reads)

  6. > 2.1G Jun 27 20:30 SRR1042598.sra (58.1M reads)

  7. > 883M Jun 27 21:08 SRR1042599.sra (24.0M reads)

  8. > 2.8G Jun 28 11:53 SRR1042600.sra (76.4M reads)

虽然下载的SRA格式数据也是一个很流行的标准,但它只是数据压缩的标准,几乎没有软件能直接跟SRA的格式的测序数据来进行分析,我们需要转成fastq格式,代码如下:

  1. > ## step2 :  change sra data to fastq files.

  2. > ## cell line: MCF7 //  Illumina HiSeq 2000 //  50bp // Single ends // phred+33

  3. > ## [http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE52964](http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE52964)

  4. > ## [ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByStudy/sra/SRP/SRP033/SRP033492](ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByStudy/sra/SRP/SRP033/SRP033492)

  5. > ls *sra |while read id; do ~/biosoft/sratoolkit/sratoolkit.2.6.3-centos_linux64/bin/fastq-dump $id;done

  6. > rm *sra

解压的详情如下,可以看到SRA格式有6~9倍的压缩了,比zip格式压缩的2~3倍高多了

  1. ##  621M --> 3.9G

  2. ##  2.2G --> 14G

  3. ##  541M --> 3.3G

  4. ##  2.4G --> 15G


第四讲:必要软件安装及结果下载

博文的顺序有点乱,因为怕读到前面的公共测序数据下载这篇文章的朋友搞不清楚,我如何调用各种软件的,所以我这里强势插入一篇博客来描述这件事,当然也只是略过,我所有的软件理论上都是安装在我的home目录下的biosoft文件夹,所以你看到我一般安装程序都是:

  1. cd ~/biosoft

  2. mkdir macs2 && cd macs2 ##指定的软件安装在指定文件夹里面

这只是我个人的安装习惯,因为我不是root,所以不能在linux系统下做太多事,我这里贴出我所有的软件安装代码:

  1. ## pre-step: download sratoolkit /fastx_toolkit_0.0.13/fastqc/bowtie2/bwa/MACS2/HOMER/QuEST/mm9/hg19/bedtools

  2. ## http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=software

  3. ## http://www.ncbi.nlm.nih.gov/books/NBK158900/

  4. ## Download and install sratoolkit

  5. cd ~/biosoft

  6. mkdir sratoolkit && cd sratoolkit

  7. wget http://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/2.6.3/sratoolkit.2.6.3-centos_linux64.tar.gz

  8. ##

  9. ## Length: 63453761 (61M) [application/x-gzip]

  10. ## Saving to: "sratoolkit.2.6.3-centos_linux64.tar.gz"

  11. tar zxvf sratoolkit.2.6.3-centos_linux64.tar.gz

  12. ## Download and install bedtools

  13. cd ~/biosoft

  14. mkdir bedtools && cd bedtools

  15. wget https://github.com/arq5x/bedtools2/releases/download/v2.25.0/bedtools-2.25.0.tar.gz

  16. ## Length: 19581105 (19M) [application/octet-stream]

  17. tar -zxvf bedtools-2.25.0.tar.gz

  18. cd bedtools2

  19. make

  20. ## Download and install PeakRanger

  21. cd ~/biosoft

  22. mkdir PeakRanger && cd PeakRanger

  23. wget https://sourceforge.net/projects/ranger/files/PeakRanger-1.18-Linux-x86_64.zip/

  24. ## Length: 1517587 (1.4M) [application/octet-stream]

  25. unzip PeakRanger-1.18-Linux-x86_64.zip

  26. ~/biosoft/PeakRanger/bin/peakranger -h

  27. ## Download and install bowtie

  28. cd ~/biosoft

  29. mkdir bowtie && cd bowtie

  30. wget https://sourceforge.net/projects/bowtie-bio/files/bowtie2/2.2.9/bowtie2-2.2.9-linux-x86_64.zip/download

  31. #Length: 27073243 (26M) [application/octet-stream]

  32. #Saving to: "download" ## I made a mistake here for downloading the bowtie2

  33. mv download bowtie2-2.2.9-linux-x86_64.zip

  34. unzip bowtie2-2.2.9-linux-x86_64.zip

  35. mkdir -p ~/biosoft/bowtie/hg19_index

  36. cd ~/biosoft/bowtie/hg19_index

  37. # download hg19 chromosome fasta files

  38. wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/chromFa.tar.gz

  39. # unzip and concatenate chromosome and contig fasta files

  40. tar zvfx chromFa.tar.gz

  41. cat *.fa > hg19.fa

  42. rm chr*.fa

  43. ## ~/biosoft/bowtie/bowtie2-2.2.9/bowtie2-build ~/biosoft/bowtie/hg19_index/hg19.fa ~/biosoft/bowtie/hg19_index/hg19

  44. ## Download and install BWA

  45. cd ~/biosoft

  46. mkdir bwa && cd bwa

  47. http://sourceforge.net/projects/bio-bwa/files/

  48. tar xvfj bwa-0.7.12.tar.bz2 # x extracts, v is verbose (details of what it is doing), f skips prompting for each individual file, and j tells it to unzip .bz2 files

  49. cd bwa-0.7.12

  50. make

  51. export PATH=$PATH:/path/to/bwa-0.7.12 # Add bwa to your PATH by editing ~/.bashrc file (or .bash_profile or .profile file)

  52. # /path/to/ is an placeholder. Replace with real path to BWA on your machine

  53. source ~/.bashrc

  54. # bwa index [-a bwtsw|is] index_prefix reference.fasta

  55. bwa index -p hg19bwaidx -a bwtsw ~/biosoft/bowtie/hg19_index/hg19.fa

  56. # -p index name (change this to whatever you want)

  57. # -a index algorithm (bwtsw for long genomes and is for short genomes)

  58. ## Download and install macs2

  59. ## // https://pypi.python.org/pypi/MACS2/

  60. cd ~/biosoft

  61. mkdir macs2 && cd macs2

  62. wget ~~~~~~~~~~~~~~~~~~~~~~MACS2-2.1.1.20160309.tar.gz

  63. tar zxvf MACS2-2.1.1.20160309.tar.gz

  64. cd MACS2-2.1.1.20160309

  65. python setup.py install --user

  66. #################### The log for installing MACS2:

  67. Creating ~/.local/lib/python2.7/site-packages/site.py

  68. Processing MACS2-2.1.1.20160309-py2.7-linux-x86_64.egg

  69. Copying MACS2-2.1.1.20160309-py2.7-linux-x86_64.egg to ~/.local/lib/python2.7/site-packages

  70. Adding MACS2 2.1.1.20160309 to easy-install.pth file

  71. Installing macs2 script to ~/.local/bin

  72. Finished processing dependencies for MACS2==2.1.1.20160309

  73. ############################################################

  74. ~/.local/bin/macs2 --help

  75. Example for regular peak calling:

  76. macs2 callpeak -t ChIP.bam -c Control.bam -f BAM -g hs -n test -B -q 0.01

  77. Example for broad peak calling:

  78. macs2 callpeak -t ChIP.bam -c Control.bam --broad -g hs --broad-cutoff 0.1

  79. ## Download and install homer (Hypergeometric Optimization of Motif EnRichment)

  80. ## // http://homer.salk.edu/homer/

  81. ## // http://blog.qiubio.com:8080/archives/3024

  82. ## pre-install: Ghostscript,seqlogo,blat

  83. cd ~/biosoft

  84. mkdir homer && cd homer

  85. wget http://homer.salk.edu/homer/configureHomer.pl

  86. perl configureHomer.pl -install

  87. perl configureHomer.pl -install hg19

一般来说,对我这样水平的人来说,软件安装就跟家常便饭一样,没有什么问题了,但如果你是初学者呢,肯定没那么轻松,所以请加强学习,我无法在这里讲解太具体的知识了。

所有软件安装完毕后就可以下载文章对这些ChIP-seq的处理结果了,这个很重要,检验我们是否重复了人家的数据分析过程。

  1. ## step3 : download the results from paper

  2. ## http://www.bio-info-trainee.com/1571.html

  3. mkdir paper_results && cd paper_results

  4. wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE52nnn/GSE52964/suppl/GSE52964_RAW.tar

  5. tar xvf GSE52964_RAW.tar

  6. ls *gz |xargs gunzip

  7. ## step4 : run FastQC to check the sequencing quality.

  8. ##这里可以看到我们下载的原始数据已经被作者处理好了,去了接头,去了低质量序列

  9. ls *.fastq | while read id ; do ~/biosoft/fastqc/FastQC/fastqc $id;done

  10. ## Sequence length 51

  11. ## %GC 39

  12. ## Adapter Content passed

  13. The quality of the reads is pretty good, we don't need to do any filter or trim

  14. mkdir QC_results

  15. mv *zip *html QC_results/


编辑校对:思考问题的熊



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