Bioconductor

Updating R

As you might know by now, the latest R version was recently released (R 3.4.0). That means that you are highly encouraged to update your R installation. There are several ways to do this some of which are documented in these other blog posts: Tal Galili, 2013, Kris Eberwein, 2015. You would think that it’s just a matter of downloading the latest R installer for your OS, installing it, and continuing your analysis.

Are you doing parallel computations in R? Then use BiocParallel

It’s the morning of the first day of oral conferences at #ENAR2016. I feel like I have a spidey sense since I woke up 3 min after an email from Jeff Leek; just a funny coincidence. Anyhow, I promised Valerie Obenchain at #Bioc2014 that I would write a post about one of my favorite Bioconductor packages: BiocParallel (Morgan, Obenchain, Lang, and Thompson, 2016). By now it’s on the top 5% of downloaded Bioconductor packages, so many people know about it or are unaware that their favorite package uses it behind the scenes.

Where do I start using Bioconductor?

I was recently asked where do I get started with Bioconductor? and thought this would be a good short post. What is BioC? Briefly, Bioconductor (Gentleman, Carey, Bates, and others, 2004) is an open source project that hosts a wide range of tools for analyzing biological data with R (R Core Team, 2014). These analysis tools are bundled into packages which are designed to answer specific questions or to provide key infrastructure.

Setting up your computer for bioinformatics/biostatistics and a compedium of resources

Jumping on the train set by Hilary Parker “The Setup (Part 1)” and Alyssa Frazee “my software/hardware setup”, I’m going to share my setup and hopefully add something new. They both did a great job already, so make sure you read their posts! I have some experience with all three main OS: Windows, Linux and Mac. That being said, I know some of the basic stuff for each but I surely use Google very frequently to get help.

The new visualization package for genome data in Bioconductor: ggbio

It’s been a while since I’ve been waiting for the release of a visualization package in Bioconductor. Back in 2008 I was really impressed by the power ofGenomeGraphs and I have used it in multiple occasions. Yet from both the Bioconductor Developer Meeting in Heidelberg 2010 and BioC2011 I’ve been waiting for the release of the visualization tools developed by Michael Lawrence and Tengfei Yin at Genentech. So, after a long hiatus where I didn’t browse the biocviews in Bioconductor, I found out that Lawrence and Yin released ggbio and biovizBase (it’s more of an infrastructure package for ggbio) .

Introducing Biostatistics to first year LCG students

Around two weeks ago I gave a talk via skype to the first year students from the Undergraduate Program on Genomic Sciences (LCG in Spanish) from the National Autonomous University of Mexico (UNAM in Spanish). The talk was under the context of the Introduction to Bioinformatics Seminar Series whose goal is to familiarize the new students with the bioinformatics world. It used to be a course heavy on exploring database websites, some basic theory, and lots of new concepts and algorithm names.