Here are some resources for finding more help when performing data analysis for genomics.

SEQanswers and Biostars

These are both very useful for getting feedback on genomics experiments and getting data analysis tips.

  • SEQanswers is “an information resource and user-driven community focused on all aspects of next-generation genomics”. They say “The site will always attempt to cater to everyone, regardless of scientific background or knowledge.” SEQanswers also has a wiki listing many various software packages for genomics data analysis.

  • Biostars is a website focused on “bioinformatics, computational genomics and biological data analysis” They say: “No question is too trivial or too ‘newbie’.”

Bioconductor support site

If your question is about Bioconductor software, you can post a question to the Bioconductor support site. This is answered by 100s of statisticians, bioinformaticians and developers around the world. See the posting guide for instructions on how to optimally write a post for getting the best feedback. The support site is searchable and extends back to the first post ever on 26 November, 2001. If you directly email a Bioconductor developer with a support question, they will most likely send an auto-response for you to post to the support site.

In addition to the Posting Guide, there are four must-do’s for submitting an email to the Bioconductor mailing list:

  1. Read the help pages and vignette, especially for the function you are using: ?functionName. While not all questions are answered in the vignette, many are. Package maintainers can get grumpy (they are human) if you ask a question which is clearly answered in the vignette or the help page. Remember, typing browseVignettes("pkgName") will bring you to the software vignettes. More on how to find help within R.
  2. Explain what you are trying to do. If the question is not simply reporting a bug, i.e., you are not sure exactly what kind of analysis to run, then you should explain what biological question you are trying to answer with your experiment/data, and what the experimental design is. You don’t have to give away secrets, e.g., “we are interested in looking at differences in protein binding for 2 treatments and control, with 3 biological replicates in each condition.”
  3. Always paste the output of sessionInfo() at the bottom of your email. This gives the package maintainers lots of useful information, like what version of R you are running, your platform, and the versions of all the packages.
  4. Paste in all of your code, and any errors or warnings if they occurred.

If you have a general R question, there is a page for R questions on StackOverflow.