Going forward

Look into

• Practical Computing for Biologists book by Haddock and Dunn
• Interactive notebooks: Sharing the code by Helen Shen. Nature. 2014 Nov 6;515(7525):151-2. doi: 10.1038/515151a. PMID: 25373681
• Programming tools: Adventures with R by Sylvia Tippmann. Nature. 2015 Jan 1;517(7532):109-10. doi: 10.1038/517109a. PMID: 25557714
• a two year-old screencast intro of IPython notebook by Titus Brown (Skip to the three-minute mark since we aren’t necessarily interested in running it on Amazon web services right now.) A non-interactive version of the notebook he demonstrates is here.
• Another take on the wonders of the IPython notebook, from a blog.
• Orchestrating high-throughput genomic analysis with Bioconductor. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M. Nat Methods. 2015 Jan 29;12(2):115-21. doi: 10.1038/nmeth.3252. PMID: 25633503
• Programming: Pick up Python by Jeffrey M. Perkel. Nature. 2015 February 5;518:125–6.doi:10.1038/518125a. PMID:25653001 too
• March 2015 blog post suggesting mandatory primer courses for basic skills for students in cellular & molecular biology, genetics, and related subfields
• A History of Bioinformatics (in the Year 2039), a presentation by Titus Brown encouraging good data practices in his unique way

Regular Expressions

• Chapters 2 & 3 of Practical Computing for Biologists book by Haddock and Dunn. The related appendix #2 is freely available as part of tables of Appendices from Practical Computing for Biologists book by Haddock and Dunn
• Regular Expressions Primer
• Regular Expressions 101: online regex editor and debugger tool (Seems best with g global modifier on.)
• RegExr v2.0: online tool to learn, build, & test Regular Expressions
• regex tester
• Python Regular Expression Testing Tool
• For using regular expressions in Sublime Text, you need to click on the box .* next to Find What when Find tool open to activate. See here for more information.
• In TextWrangler the trick to activate Regular Expressions is to toggle on Grep box under Matching in the Find and Replace panel. See here about 1 and half minutes into the video.
• BBEdit-TextWrangler_RegEx_Cheat_Sheet.txt
• See here under ‘Search and replace with special characters (regular expressions)’ for using Regular Expressions in AquaMacs.

IPython Notebook

• Interactive notebooks: Sharing the code by Helen Shen. Nature. 2014 Nov 6;515(7525):151-2. doi: 10.1038/515151a. PMID: 25373681
• a two year-old screencast intro of IPython notebook by Titus Brown (Skip to the three-minute mark since we aren’t necessarily interested in running it on Amazon web services right now.) A non-interactive version of the notebook he demonstrates is here.
• Another take on the wonders of the IPython notebook, from a blog
• The future of the IPython Notebook is the Jupyter project
• Analyzing data with R in the IPython notebook

R and Bioconductor in general

• Programming tools: Adventures with R by Sylvia Tippmann. Nature. 2015 Jan 1;517(7532):109-10. doi: 10.1038/517109a. PMID: 25557714
• Orchestrating high-throughput genomic analysis with Bioconductor. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M. Nat Methods. 2015 Jan 29;12(2):115-21. doi: 10.1038/nmeth.3252. PMID: 25633503

Learning R

• See the box in the article Programming tools: Adventures with R by Sylvia Tippmann. Nature. 2015 Jan 1;517(7532):109-10. doi: 10.1038/517109a. PMID: 25557714.
• The Coursera courses in Johns Hopkins’ Data Science Specialization, in particular the R Programming and Getting and Cleaning Data courses. If you are brand new to this and don’t yet know how to use Github, The Data Scientist’s Toolbox would probably be helpful as a starting point.
• You do get a bit of flavor for the use of R in data analysis in the Coursera courses Bioinformatic Methods I and Bioinformatic Methods II
• Comparing Python and R for Data Science
• How to Transition from Excel to R: An Intro to R for Microsoft Excel Users

ChIP-seq data analysis

• Titus Brown and Colleague’s Next-Gen Sequence Analysis Workshops, most recent is [Next-Gen Sequence Analysis Workshop (2014)(http://angus.readthedocs.org/en/2014/) Particularly pertinent are the sections Istvan Albert’s 2012 ChIP-Seq lecture, Day 7: ChIP-seq: Peak Predictions and Cis-regulatory Element Annotations, Using MEME to identify TF binding motif from ChIP-seq data and here.
• ChIP- and DNase-seq data analysis workshop 2014
• Cis-regulatory Element Annotation System by Hyunjin Shin and Tao Liu from Xiaole Shirley Liu’s Lab
• ab initio motif finder MEME and the related MEME suite
• MEME-LaB wraps the popular ab initio motif finder in a web tool
• Motif enrichment tool. Blatti C, Sinha S. Nucleic Acids Res. 2014 Jul;42(Web Server issue):W20-5. doi: 10.1093/nar/gku456. Epub 2014 May 23. PMID: 24860165
• Motif-based analysis of large nucleotide data sets using MEME-ChIP

Plus see the ‘literature’ page in this collection of pages from the session.

Python and ChIP-Seq and related analyses

• Feng et al. 2012. Identifying ChIP-seq enrichment using MACS.Nat Protoc. 2012 Sep; 7(9): 10.1038/nprot.2012.101.

• Metaseq is a Python modle and covers Determining Average ChIP-seq signal over promoters with Metaseq.

• NUCwave as described in Comparative analysis of methods for genome-wide nucleosome cartography. Luis Quintales, Enrique Vázquez and Francisco Antequera. Briefings in Bioinformatics 2014. doi: 10.1093/bib/bbu037

• pyDNase - a library for analyzing DNase-seq data as described in Piper et al. 2013. Wellington: A novel method for the accurate identification of digital genomic footprints from DNase-seq data, Nucleic Acids Research 2013; doi: 10.1093/nar/gkt850

  Many people currently analyzing DNase-seq data are using tools designed for ChIP-seq work, but may be inappropriate for DNase-seq data where one is less interested in the overlaps of sequenced fragments, but the site at which the cut occurs (the 5’ most end of the aligned sequence fragment). We have developed pyDNase to interface with a sorted and indexed BAM file from a DNase-seq experiment, allowing efficient and easy random access of DNase-seq cut data from any genomic location, e.g. Piper et al. 2013. Wellington: A novel method for the accurate identification of digital genomic footprints from DNase-seq data, Nucleic Acids Research 2013; doi: 10.1093/nar/gkt850

R and ChIP-seq

• ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. Lihua J Zhu, Claude Gazin, Nathan D Lawson, Hervé Pagès, Simon M Lin, David S Lapointe and Michael R Green.

I need to add the other main ones I saw here still.

Git and Github

• Github bootcamp
• main Git resource. You can even learn git right in your browser
• Simple guide to git
• About Gists

Questions

• Try Google, probably will lead you to one of my listed resources or...
• Biostars
• Stackoverflow for general scripting and computing
• SEQanswers - a high throughput sequencing community
• Try Twitter - for example this