Three years ago I wrote a blog post on how to create manhattan plots in R. After hundreds of comments pointing out bugs and other issues, I've finally cleaned up this code and turned it into an R package.
Three years ago I wrote a blog post on how to create manhattan plots in R. After hundreds of comments pointing out bugs and other issues, I've finally cleaned up this code and turned it into an R package.
Boston-based startup Curoverse has announced $1.5 million in funding to develop and support the open-source Arvados platform for cloud-based bioinformatics & genomics data analysis. The Arvados platform was developed in George Church's lab by scientists and engineers led by Alexander Wait Zaranek, now scientific director at Curoverse.
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.
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.
I collaborate with several investigators on gene expression projects using both microarray and RNA-seq. After I show a collaborator which genes are dysregulated in a particular condition or tissue, the most common question I get is " what are the transcription factors regulating these genes? " This isn't the easiest question to answer.
Since the near beginning of genome-wide association studies, the PLINK software package (developed by Shaun Purcell’s group at the Broad Institute and MGH) has been the standard for manipulating the large-scale data produced by these studies.
A couple of weeks ago I, with the help of others here at UVA, organized a Software Carpentry bootcamp, instructed by Steve Crouch, Carlos Anderson, and Ben Morris. The day before the course started, Charlottesville was racked by nearly a foot of snow, widespread power outages, and many cancelled incoming flights. Luckily our instructors arrived just in time, and power was (mostly) restored shortly before the boot camp started.
Metagenomics is the study of DNA collected from environmental samples (e.g., seawater, soil, acid mine drainage, the human gut, sputum, pus, etc.). While traditional microbial genomics typically means sequencing a pure cultured isolate, metagenomics involves taking a culture-free environmental sample and sequencing a single gene (e.g. the 16S rRNA gene), multiple marker genes, or shotgun sequencing everything in the sample in order to
It's happened to all of us. You read about a new tool, database, webservice, software, or some interesting and useful data, but when you browse to http://instititution.edu/~home/professorX/lab/data, there's no trace of what you were looking for. THE PROBLEM This isn't an uncommon problem. See the following two articles: The first gives us some alarming statistics.
I've said it before: Twitter makes me a lazy blogger. Lots of stuff came across my radar this week that didn't make it into a full blog post.