How to Stay Current in Bioinformatics/Genomics

A few folks have asked me how I get my news and stay on top of what's going on in my field, so I thought I'd share my strategy. With so many sources of information begging for your attention, the difficulty is not necessarily finding what's interesting, but filtering out what isn't. What you don't read is just as important as what you do, so when it comes to things like RSS, Twitter, and especially e-mail, it's essential to filter out sources where the content consistently fails to be relevant or capture your interest. I run a bioinformatics core, so I'm more broadly interested in applied methodology and study design rather than any particular phenotype, model system, disease, or method. With that in mind, here's how I stay current with things that are relevant to me. Please leave comments with what you're reading and what you find useful that I omitted here.

RSS

I get the majority of my news from RSS feeds from blogs and journals in my field. I spend about 15 minutes per day going through headlines from the following sources:

Journals. Most journals have separate RSS feeds for their current table of contents as well as their advance online ahead-of-print articles.

• Bioinformatics - RSS feed of current and advance online publications
• Genome Research - current & advance
• Genome Biology - editors picks, latest, most viewed, most forwarded. (Hit the RSS icon under each tab).
• PLoS Genetics - new articles
• PLoS Computational Biology - new articles
• Nature Genetics - current TOC and AOP
• Nature Reviews Genetics - current TOC and AOP

Blogs. Some of these blogs are very relevant to what I do on the job. Others are more personal interest.

• The OpenHelix Blog
• Ensembl blog
• Galaxy News
• Blue Collar Bioinformatics
• Homologus
• Golden Helix - our 2 SNPs
• Genomics Law Report
• R-bloggers (aggregates feeds from >350 blogs about R)
• Genomes Unzipped
• Jason Moore's Epistasis Blog
• 23andMe - the Spitoon

Forums.

• Seqanswers - bioinformatics forum
• Seqanswers - RNA-Seq forum
• BioStar

Mailing lists


I prefer to keep work and personal email separate, but I have all my mailing list email sent to my Gmail because Gmail's search is better than any alternative. I have a filter set up to automatically filter and tag mailing list digests under a "Work" label so I can get to them (or filter them from my inbox) easily.

• Bioconductor (daily digest)
• Galaxy mailing lists. I subscribe to the -announce, -user, and -dev mailing lists, but I have a Gmail filter set up to automatically skip the inbox and mark read messages from the -user and -dev lists. I don't care to look at these every day, but again, it's handy to be able to use Gmail's search functionality to look through old mailing list responses.

Email Alerts & Subscriptions

Again, email can get out of hand sometimes, so I prefer to only have things that I really don't want to miss sent to my email. The rest I use RSS.

• SeqAnswers subscriptions. When I ask a question or find a question that's relevant to something I'm working on, I subscribe to that thread for email alerts whenever a new response is posted. 
• Google Scholar alerts. I have alerts set up to send me emails based on certain topics (e.g. [ rna-seq | transcriptome sequencing | RNA-sequencing ] or [ intitle:"chip-seq" ]), or when certain people publish (e.g. ["ritchie md" & vanderbilt]). I also use this to alert me when certain consortia publish (e.g. ["Population Architecture using Genomics and Epidemiology"]).
• PubMed Saved Searches using MyNCBI, because Google Scholar doesn't catch everything. I have alerts set up for RNA-seq, ChIP-Seq, bioinformatics methods, etc.
• GenomeWeb subscriptions. Most of these are once per week, except Daily Scan. I subscribe to Daily Scan, Genome Technology, BioInform, Clinical Sequencing News, In Sequence, and Pharmacogenomics Reporter. BioInform has a "Bioinformatics Papers of Note", and In Sequence has a "Sequencing papers of note" column in every issue. These are good for catching things I might have missed with the Scholar and Pubmed alerts.

Twitter

99.9% of Twitter users have way too much time on their hands, but when used effectively, Twitter can be incredibly powerful for both consuming and contributing to the dialogue in your field. Twitter can be an excellent real-time source of new publications, fresh developments, and current opinion, but it can also quickly become a time sink. I can tolerate an occasional Friday afternoon humorous digression, but as soon as off-topic tweets become regular it's time to unfollow. The same is true with groups/companies - some deliver interesting and broadly applicable content (e.g. 23andMe), while others are purely a failed attempt at marketing while not offering any substantive value to their followers. A good place to start is by (shameless plug) following me or the people I follow (note: this isn't an endorsement of anyone on this list, and there are a few off-topic people I follow for my non-work interests). I can't possibly list everyone, but a few folks who tweet consistently on-topic and interesting content are: Daniel MacArthur, Jason Moore, Dan Vorhaus, 23andMe, OpenHelix, Larry Parnell, Francis Ouellette, Leonid Kruglyak, Sean Davis, Joe Pickrell, The Galaxy Project, J. Chris Pires, Nick Loman, and Andrew Severin. Also, a hashtag in twitter (prefixed by the #), is used to mark keywords or topics in Twitter. I occasionally browse through the #bioinformatics and #Rstats hashtag.

Stepping Outside My Open-Source Comfort Zone: A First Look at Golden Helix SVS

I'm a huge supporter of the Free and Open Source Software movement. I've written more about R than anything else on this blog, all the code I post here is free and open-source, and a while back I invited you to steal this blog under a cc-by-sa license.

Every now and then, however, something comes along that just might be worth paying for. As a director of a bioinformatics core with a very small staff, I spend a lot of time balancing costs like software licensing versus personnel/development time, so that I can continue to provide a fiscally sustainable high-quality service.

As you've likely noticed from my more recent blog/twitter posts, the core has been doing a lot of gene expression and RNA-seq work. But recently had a client who wanted to run a fairly standard case-control GWAS analysis on a dataset from dbGaP. Since this isn't the focus of my core's service, I didn't want to invest the personnel time in deploying a GWAS analysis pipeline, downloading and compiling all the tools I would normally use if I were doing this routinely, and spending hours on forums trying to remember what to do with procedural issues such as which options to specify when running EIGENSTRAT or KING, or trying to remember how to subset and LD-prune a binary PED file, or scientific issues, such as whether GWAS data should be LD-pruned at all before doing PCA.

Golden Helix

A year ago I wrote a post about the "Hitchhiker's Guide to Next-Gen Sequencing" by Gabe Rudy, a scientist at Golden Helix. After reading this and looking through other posts on their blog, I'm confident that these guys know what they're doing and it would be worth giving their product a try. Luckily, I had the opportunity to try out their SNP & Variation Suite (SVS) software (I believe you can also get a free trial on their website).

I'm not going to talk about the software - that's for a future post if the core continues to get any more GWAS analysis projects. In summary - it was fairly painless to learn a new interface, import the data, do some basic QA/QC, run a PCA-adjusted logistic regression, and produce some nice visualization. What I want to highlight here is the level of support and documentation you get with SVS.

Documentation

First, the documentation. At each step from data import through analysis and visualization there's a help button that opens up the manual at the page you need. This contextual manual not only gives operational details about where you click or which options to check, but also gives scientific explanations of why you might use certain procedures in certain scenarios. Here's a small excerpt of the context-specific help menu that appeared when I asked for help doing PCA.

What I really want to draw your attention to here is that even if you don't use SVS you can still view their manual online without registering, giving them your e-mail, or downloading any trialware. Think of this manual as an always up-to-date mega-review of GWAS - with it you can learn quite a bit about GWAS analysis, quality control, and statistics. For example, see this section on haplotype frequency estimation and the EM algorithm. The section on the mathematical motivation and implementation of the Eigenstrat PCA method explains the method perhaps better than the Eigenstrat paper and documentation itself. There are also lots of video tutorials that are helpful, even if you're not using SVS. This is a great resource, whether you're just trying to get a better grip on what PLINK is doing, or perhaps implementing some of these methods in your own software.

Support

Next, the support. After installing SVS on both my Mac laptop and the Linux box where I do my heavy lifting, one of the product specialists at Golden Helix called me and walked me through every step of a GWAS analysis, from QC to analysis to visualization. While analyzing the dbGaP data for my client I ran into both software-specific procedural issues as well as general scientific questions. If you've ever asked a basic question on the R-help mailing list, you know need some patience and a thick skin for all the RTFM responses you'll get. I was able to call the fine folks at Golden Helix and get both my technical and scientific questions answered in the same day. There are lots of resources for getting your questions answered, such as SEQanswers, Biostar, Cross Validated, and StackOverflow to name a few, but getting a forum response two days later from "SeqGeek96815" doesn't compare to having a team of scientists, statisticians, programmers, and product specialists on the other end of a telephone whose job it is to answer your questions.

Final Thoughts

This isn't meant to be a wholesale endorsement of Golden Helix or any other particular software company - I only wanted to share my experience stepping outside my comfortable open-source world into the walled garden of a commercially-licensed software from a for-profit company (the walls on the SVS garden aren't that high in reality - you can import and export data in any format imaginable). One of the nice things about command-line based tools is that it's relatively easy to automate a simple (or at least well-documented) process with tools like Galaxy, Taverna, or even by wrapping them with perl or bash scripts. However, the types of data my clients are collecting and the kinds of questions they're asking are always a little new and different, which means I'm rarely doing the same exact analysis twice. Because of the level of documentation and support provided to me, I was able to learn a new interface to a set of familiar procedures and run an analysis very quickly and without spending hours on forums figuring out why a particular program is seg-faulting. Will I abandon open-source tools like PLINK for SVS, Tophat-Cufflinks for CLC Workbench, BWA for NovoAlign, or R for Stata? Not in a million years. I haven't talked to Golden Helix or some of the above-mentioned companies about pricing for their products, but if I can spend a few bucks and save the time it would taken a full time technician at $50k+/year to write a new short read aligner or build a new SNP annotation database server, then I'll be able to provide a faster, high-quality, fiscally sustainable service at a much lower price for the core's clients, which is all-important in a time when federal funding is increasingly harder to come by.

Video Tip: Use Ensembl BioMart to Quickly Get Ortholog Information

A few weeks ago I showed you how to convert gene IDs with BioMart. Yesterday I hosted a workshop on the Ensembl Genome Browser, given by Dr. Bert Overduin from EBI-EMBL. He gave several examples of very useful tasks that you can do very quickly and easily using BioMart. One, in particular, is something that I'm doing for a client in the core right now.

Let's say you have a set of genes in one species and you want to know the orthologs in another species and gene expression probes in that species you can use to assay those orthologs. For example, Table 1 in this paper reports 25 gene expression probes that are dysregulated in humans when exposed to benzene. What if you only had the U133A/B Affymetrix probe IDs and wanted to know the gene names? What if you also wanted all the Ensembl gene IDs, names, and descriptions of the mouse orthologs for these human genes? Further, what are the mouse Affymetrix 430Av2 probe IDs that you can use to assay these genes' expression in mouse? All this can be accomplished for a list of genes in about 60 seconds using BioMart. See the video below. Watch it on Youtube in 1080p if you're having a hard time reading the text.



If you want to try this yourself, head to BioMart and copy the list of Affy probe IDs below:

207630_s_at
221840_at
219228_at
204924_at
227613_at
223454_at
228962_at
214696_at
210732_s_at
212371_at
225390_s_at
227645_at
226652_at
221641_s_at
202055_at
226743_at
228393_s_at
225120_at
218515_at
202224_at
200614_at
212014_x_at
223461_at
209835_x_at
213315_x_at

Youtube - Get Orthologs via BioMart

NSF BIGDATA webinar

If you're doing any kind of big data analysis - genomics, transcriptomics, proteomics, bioinformatics - then unless you've been on vacation the last few weeks you've no doubt heard about the NSF/NIH BIGDATA  Initiative (here's the NSF solicitation and here's the New York Times article about the funding opportunity). The solicitation "aims to advance core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large, diverse, distributed and heterogeneous data sets so as to: accelerate the progress of scientific discovery and innovation; lead to new fields of inquiry that would not otherwise be possible; encourage the development of new data analytic tools and algorithms; facilitate scalable, accessible, and sustainable data infrastructure; increase understanding of human and social processes and interactions; and promote economic growth and improved health and quality of life."

NSF is holding a webinar to describe the goals and focus of the BIGDATA solicitation, help investigators understand its scope, and answer any questions potential PIs might have.

The Webinar will be held from 11am-noon EST on May 8, 2012. Register here. The webinar will also be archived here a few days later.

NSF BIGDATA Webinar - May 8 2012