CAGE analysis using the R Bioconductor package CAGEr

This post is outdated; please refer to the official documentation.

Cap Analysis Gene Expression (CAGE) is a molecular technique, developed at RIKEN, which captures all transcription starting sites (TSSs) of an RNA population. The C in CAGE refers to the altered nucleotide at the 5' site of precursor messenger RNA, termed the cap, which CAGE targets and pulls down. The vignette of the CAGEr package has a very nice introduction to CAGE. I'd just like to add that several other CAGE protocol exists, such as HeliScopeCAGE and nanoCAGE. While these protocols all capture TSSs, the biochemical steps are different, especially nanoCAGE, which does not use CAP trapping but template switching. If you're interested in template switching with respect to transcriptome studies, have a look at the introduction of this paper, which I wrote.

In this post I will go through the workflow of the CAGEr package. If you perform CAGE analysis, I highly recommend using this package. It provides the methods/analysis steps that are commonly used in CAGE analyses and eliminates the use of in house scripts/methods. For the first part I will use publicly available FANTOM3 and FANTOM4 data that is conveniently packaged in Bioconductor as part of CAGEr. The second part shows an example session using ENCODE CAGE data, which is conveniently provided as BAM files.

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Using the R twitteR package

  • Updated 2014 November 26th to reflect changes in the tm package
  • Updated 2015 February 18th to reflect changes in the twitteR package

A short post on using the R twitteR package for text mining and using the R wordcloud package for visualisation. I did this on my Windows machine, which has this problem. I've updated the code due to changes in the recent update of the twitteR package. In addition, I have included a screenshot below from my Twitter Apps Keys and Access Tokens page to indicate where to get the consumer_key, consumer_secret, access_token, and access_secret values.

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Student journal club

Updated 2014 March 6th: I have begun to provide summaries of papers I've read at my second Wiki.

I have recently volunteered to organise the student journal club at my institute. I asked for the job because I am not particularly well organised, so I thought I could improve on this aspect. Another reason was that I wanted some change. In the past, we chose a paper of our choice, explained the paper and the audience asked some questions. It became a weekly chore for us and the energy level at these meetings sometimes gets a bit low. Now I work in Japan and people are much less vocal here as compared to Australia; I guess it's in the culture. I would like to ask them for new ideas for some changes but anticipate that there won't be as much discussion as I'd hoped. I have many ideas on how I can run the club but I don't want to impose them on everyone since I may be the only one that thinks it's cool. For example when I read a paper I try to understand every aspect of the methods (most times to no avail). I actually would perfer if people spent more time discussing the methods. But I know those who just want the gist of the methods and want to focus more on the results.

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