RNA-seq has been performed routinely for at least 5+ years, yet there is no consensus on the best methodology for analyzing this data.

I archived and anlayzed all Tweets with the hashtag #ASHG2013 using my previously mentioned code. Number of Tweets by date shows Wednesday was the most Tweeted day: The top used hashtags other than #ASHG2013: The most prolific users: And what Twitter analysis would be complete without the widely loved, and more widely hated word cloud: Edit 8:24am : I have gotten notes that some Tweets were not captured in this archive.

Several post-publication peer review forums already exist, such as Faculty of 1000 or PubPeer, that facilitate discussion of papers after they have already been published. F1000 only allows a small number of "faculty" to comment on articles, and access to read commentary requires a paid subscription. PubPeer and similar startup services lack a critical mass of participants to make such a community truly useful.

Much of the work that bioinformaticians do is munging and wrangling around massive amounts of text. While there are some "standardized" file formats (FASTQ, SAM, VCF, etc.) and some tools for manipulating them (fastx toolkit, samtools, vcftools, etc.), there are still times where knowing a little bit of Unix/Linux is extremely helpful, namely awk, sed, cut, grep, GNU parallel, and others.

One of the clearest advantages RNA-seq has over array-based technology for studying gene expression is not needing a reference genome or a pre-existing oligo array. De novo transcriptome assembly allows you to study non-model organisms, cancer cells, or environmental metatranscriptomes.

Torsten Seemann compiled a list of minimum standards for bioinformatics command line tools, things like printing help when no commands are specified, including version info, avoid hardcoded paths, etc. These should be obvious to any seasoned software engineer, but many of these standards are not followed in bioinformatics.

ENSEMBL is a frequently used resource for various genomics and transcriptomics tasks.  The ENSEMBL website and MART tools provide easy access to their rich database, but ENSEMBL also provides flat-file downloads of their entire database and a public MySQL portal.  You can access this using the MySQL Workbench using the following: Once inside, you can get a sense for what the ENSEMBL schema (or data model) is like.

Google Developers recognized that most developers learn R in bits and pieces, which can leave significant knowledge gaps. To help fill these gaps, they created a series of introductory R programming videos. These videos provide a solid foundation for programming tools, data manipulation, and functions in the R language and software.

As the 2013 ISMB/ECCB meeting is winding down, I archived and analyzed the 2000+ tweets from the meeting using a set of bash and R scripts I previously blogged about. The archive of all the tweets tagged #ISMBECCB from July 19-24, 2013 is and will forever remain here on Github. You'll find some R code to parse through this text and run the analyses below in the same repository, explained in more detail in my previous blog post.

At last week's 2013 useR! conference in Albacete, Spain, Martin Morgan and Marc Carlson led a course on using R/Bioconductor for analyzing next-gen sequencing data, covering alignment, RNA-seq, ChIP-seq, and sequence annotation using R. The course materials are online here, including R code for running the examples, the PDF vignette tutorial, and the course material itself as a package. Course Materials from useR!