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7. ChIP-seq Analysis; DNA-protein Interactions. Chipseq. Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data. Citation: Bailey T, Krajewski P, Ladunga I, Lefebvre C, Li Q, et al. (2013) Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data.

Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data

PLoS Comput Biol 9(11): e1003326. doi:10.1371/journal.pcbi.1003326 Editor: Fran Lewitter, Whitehead Institute, United States of America Published: November 14, 2013 Copyright: © 2013 Bailey et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: TLB is funded by National Institutes of Health grant R0-1 RR021692-05. Competing interests: The authors have declared that no competing interests exist. The Analysis of ChIP-seq Data Sequencing Depth Effective analysis of ChIP-seq data requires sufficient coverage by sequence reads (sequencing depth). Read Mapping and Quality Metrics Box 1. Box 2. Peak Calling Box 3. How to learn R: A flow chart.

I often find myself giving people suggestions about how to learn R, so I decided to put together a flow chart.

How to learn R: A flow chart

This is geared toward typical psychology or cognitive science researchers planning to do basic data analysis in R. This is how to get started -- it won't make you an expert, but it should get you past your SPSS/Excel addiction. One day I'll expand it to include advanced topics. Editor's note: Links from the figure (they are all free) - To leave a comment for the author, please follow the link and comment on his blog: Minding the Brain. How to Stay Current in Bioinformatics/Genomics. A few folks have asked me how I get my news and stay on top of what's going on in my field, so I thought I'd share my strategy.

How to Stay Current in Bioinformatics/Genomics

With so many sources of information begging for your attention, the difficulty is not necessarily finding what's interesting, but filtering out what isn't. What you don't read is just as important as what you do, so when it comes to things like RSS, Twitter, and especially e-mail, it's essential to filter out sources where the content consistently fails to be relevant or capture your interest. I run a bioinformatics core, so I'm more broadly interested in applied methodology and study design rather than any particular phenotype, model system, disease, or method. With that in mind, here's how I stay current with things that are relevant to me. Please leave comments with what you're reading and what you find useful that I omitted here.

I get the majority of my news from RSS feeds from blogs and journals in my field. Journals. Blogs. Email Alerts & Subscriptions. The Galaxy Project: Online bioinformatics analysis for everyone. Bowtie: An ultrafast, memory-efficient short read aligner. Bioconductor - Home. Chipseq. To install this package, start R and enter: source(" biocLite("chipseq") In most cases, you don't need to download the package archive at all.

chipseq

Bioconductor version: Release (3.0) Tools for helping process short read data for chipseq experiments Author: Deepayan Sarkar, Robert Gentleman, Michael Lawrence, Zizhen Yao Maintainer: Bioconductor Package Maintainer <maintainer at bioconductor.org> Bowtie: An ultrafast, memory-efficient short read aligner. Homer Software and Data Download. The UCSC Genome Browser is quite possibly one of the best computational tools ever developed.

Homer Software and Data Download

Not only does it contain an incredible amount of data in a single application, it allows users to upload custom information such as data from their ChIP-Seq experiments so that they can be easily visualized and compared to other information. There are also other genome browsers that are available, and each has a different strength: UCSC Genome Browser Truly a unique resource, logs of data preloaded and annotations. WashU Epigenome Browser Capable of visualizing long-range interactions (great for data sets like Hi-C), also has a lot of preloaded data. The Integrated Genomics Viewer (IGV), great for looking at reads locally instead of needing to load them to a server/cloud based solution. Most of the tools that are part of HOMER cater to the strengths of the UCSC Genome Browser - however, the bedGraph and other files generated by HOMER can be normally be used in the other genome browsers as well. Homer Software and Data Download. This is the old version of the documentation: New Version ChIP-Seq is the best thing that happened to ChIP since the antibody.

Homer Software and Data Download

It is 100x better than ChIP-Chip since it escapes most of the problems of microarray probe hybridization. Plus it is cheaper, and genome wide. Chuck would be pleased if he came up with the idea. HOMER offers solid tools and methods for interpreting ChIP-Seq experiments. In addition to UCSC visualization support and peak finding [and motif finding of course], HOMER can help assemble data across multiple experiments and look at positional specific relationships between sequencing tags, motifs, and other features. Bowtie: An ultrafast, memory-efficient short read aligner. Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data.