ChIP-seq-analysis:明堂的ChIP-seq分析笔记

# ChIP-seq-analysis ### Snakemake pipelines I developed a Snakemake based ChIP-seq pipeline: [pyflow-ChIPseq](https://github.com/crazyhottommy/pyflow-ChIPseq). and ATACseq pipeline: [pyflow-ATACseq](https://github.com/crazyhottommy/pyflow-ATACseq) ### Resources for ChIP-seq 1. [ENCODE: Encyclopedia of DNA Elements](https://www.encodeproject.org/) [ENCODExplorer](https://www.bioconductor.org/packages/release/bioc/html/ENCODExplorer.html): A compilation of metadata from ENCODE. A bioc package to access the meta data of ENCODE and download the raw files. 2. [ENCODE Factorbook](https://www.encodeproject.org/) 3. [ChromNet ChIP-seq interactions](http://chromnet.cs.washington.edu/#/?search=&threshold=0.5) paper: [Learning the human chromatin network using all ENCODE ChIP-seq datasets](http://biorxiv.org/content/early/2015/08/04/023911) 4. [The International Human Epigenome Consortium (IHEC) epigenome data portal](http://epigenomesportal.ca/ihec/index.html?as=1) 5. [GEO](http://www.ncbi.nlm.nih.gov/gds/?term=). Sequences are in .sra format, need to use sratools to dump into fastq. 6. [European Nucleotide Archive](http://www.ebi.ac.uk/ena). Sequences are available in fastq format. 7. [Data bases and software from Sheirly Liu's lab at Harvard](http://liulab.dfci.harvard.edu/WEBSITE/software.htm) 8. [Blueprint epigenome](http://dcc.blueprint-epigenome.eu/#/home) 9. [A collection of tools and papers for nucelosome positioning and TF ChIP-seq](http://generegulation.info/) 10. [review paper:Deciphering ENCODE](http://www.cell.com/trends/genetics/fulltext/S0168-9525(16)00017-2) 11. [EpiFactors](http://epifactors.autosome.ru/) is a database for epigenetic factors, corresponding genes and products. 12. [biostar handbook](https://read.biostarhandbook.com/). My [ChIP-seq chapter](https://read.biostarhandbook.com/chip-seq/chip-seq-analysis.html) is out April 2017! 13. [ReMap 2018](http://tagc.univ-mrs.fr/remap/) An integrative ChIP-seq analysis of regulatory regions. The ReMap atlas consits of 80 million peaks from 485 transcription factors (TFs), transcription coactivators (TCAs) and chromatin-remodeling factors (CRFs) from public data sets. The atlas is available to browse or download either for a given TF or cell line, or for the entire dataset. ### Papers on ChIP-seq 1. [ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia](http://www.ncbi.nlm.nih.gov/pubmed/22955991) 2. [Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003326) 3. [Systematic evaluation of factors influencing ChIP-seq fidelity](http://www.nature.com/nmeth/journal/v9/n6/full/nmeth.1985.html) 4. [ChIP–seq: advantages and challenges of a maturing technology](http://www.nature.com/nrg/journal/v10/n10/abs/nrg2641.html) 5. [ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions](http://www.nature.com/nrg/journal/v13/n12/abs/nrg3306.html) 6. [Beyond library size: a field guide to NGS normalization](http://biorxiv.org/content/early/2014/06/19/006403) 7. [ENCODE paper portol](http://www.nature.com/encode/threads) 8. [Enhancer discovery and characterization](http://www.nature.com/encode/threads/enhancer-discovery-and-characterization) 9. 2016 review [Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation](http://bib.oxfordjournals.org/content/early/2016/03/15/bib.bbw023.full) 10. [bioinformatics paper:Features that define the best ChIP-seq peak calling algorithms](http://bib.oxfordjournals.org/content/early/2016/05/10/bib.bbw035.short?rss=1) compares different peak callers for TFs and histones. 11. [Systematic comparison of monoclonal versus polyclonal antibodies for mapping histone modifications by ChIP-seq](http://biorxiv.org/content/early/2016/05/19/054387) The binding patterns for H3K27ac differed substantially between polyclonal and monoclonal antibodies. However, this was most likely due to the distinct immunogen used rather than the clonality of the antibody. Altogether, we found that monoclonal antibodies as a class perform as well as polyclonal antibodies. Accordingly, we recommend the use of monoclonal antibodies in ChIP-seq experiments. 12. A nice small review: [Unraveling the 3D genome: genomics tools for multiscale exploration](http://www.cell.com/trends/genetics/pdf/S0168-9525(15)00063-3.pdf) 13. Three very interesting papers, [Developmental biology: Panoramic views of the early epigenome](http://www.nature.com/nature/journal/v537/n7621/full/nature19468.html) 14. [ChIP off the old block: Beyond chromatin immunoprecipitation](https://www.sciencemag.org/features/2018/12/chip-old-block-beyond-chromatin-immunoprecipitation). A nice review of the past and future of ChIPseq. 15. [Histone Modifications: Insights into Their Influence on Gene Expression](https://www.sciencedirect.com/science/article/pii/S0092867418310481) **Protocols** 1. [A computational pipeline for comparative ChIP-seq analyses](http://www.ncbi.nlm.nih.gov/pubmed/22179591) 2. [Identifying ChIP-seq enrichment using MACS](http://www.nature.com/nprot/journal/v7/n9/full/nprot.2012.101.html) 3. [Spatial clustering for identification of ChIP-enriched regions (SICER) to map regions of histone methylation patterns in embryonic stem cells](http://www.ncbi.nlm.nih.gov/pubmed/24743992) 4. [ENCODE tutorials](http://www.genome.gov/27553900) 5. [A User's Guide to the Encyclopedia of DNA Elements (ENCODE)](http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001046) 6. [A toolbox of immunoprecipitation-grade monoclonal antibodies to human transcription factors](https://www.nature.com/articles/nmeth.4632) The data portal https://proteincapture.org/ ### Quality Control Data downloaded from GEO usually are raw fastq files. One needs to do quality control (QC) on them. * [fastqc](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) * [multiqc](http://multiqc.info/) Aggregate results from bioinformatics analyses across many samples into a single report. Could be very useful to summarize the QC report. ### Peak calling Be careful with the peaks you get: [Active promoters give rise to false positive ‘Phantom Peaks’ in ChIP-seq experiments](http://nar.oxfordjournals.org/content/early/2015/06/27/nar.gkv637.long) It is good to have controls for your ChIP-seq experiments. A DNA input control (no antibody is applied) is prefered. The IgG control is also fine, but because so little DNA is there, you might get many duplicated reads due to PCR artifact. **For cancer cells, an input control can be used to correct for copy-number bias.** * [tools used by IHEC consortium](http://ihec-epigenomes.org/research/tools/) [A quote from Tao Liu:](https://groups.google.com/forum/#!searchin/macs-announcement/h3k27ac/macs-announcement/9_LB5EsjS_Y/nwgsPN8lR-kJ) who develped MACS1/2 >I remember in a PloS One paper last year by Elizabeth G. Wilbanks et al., authors pointed out the best way to sort results in MACS is by -10*log10(pvalue) then fold enrichment. I agree with them. You don't have to worry about FDR too much if your input data are far more than ChIP data. MACS1.4 calculates FDR by swapping samples, so if your input signal has some strong bias somewhere in the genome, your FDR result would be bad. Bad FDR may mean something but it's just secondary. 1. The most popular peak caller by Tao Liu: [MACS2](https://github.com/taoliu/MACS/). Now `--broad` flag supports broad peaks calling as well. 2. [TF ChIP-seq peak calling using the Irreproducibility Discovery Rate (IDR) framework](https://sites.google.com/site/anshulkundaje/projects/idr) and many [Software Tools Used to Create the ENCODE Resource](https://genome.ucsc.edu/ENCODE/encodeTools.html) 3. [SICER](http://home.gwu.edu/~wpeng/Software.htm) for broad histone modification ChIP-seq 4. [HOMER](http://homer.salk.edu/homer/ngs/peaks