A while back I wrote this post about how I stay current in bioinformatics & genomics. That was nearly five years ago. A lot has changed since then. A few links are dead. Some of the blogs or Twitter accounts I mentioned have shifted focus or haven’t been updated in years (guilty as charged). The way we consume media has evolved – Google thought they could kill off RSS (long live RSS!), there are many new literature alert services, preprints have really taken off in this field, and many more scientists are engaging via social media than before.

People still frequently ask me how I stay current and keep a finger on the pulse of the field. I’m not claiming to be able to do this well – that’s a near-impossible task for anyone. Five years later and I still run our bioinformatics core, and I’m still mostly focused on applied methodology and study design rather than any particular phenotype, model system, disease, or specific method. It helps me to know that transcript-level estimates improve gene-level inferences from RNA-seq data, and that there’s software to help me do this, but the details underlying kmer shredding vs pseudoalignment to a transcriptome de Bruijn graph aren’t as important to me as knowing that there’s a software implementation that’s well documented, actively supported, and performs well in fair benchmarks. As such, most of what I pay attention to is applied/methods-focused.

What follows is a scattershot, noncomprensive guide to the people, blogs, news outlets, journals, and aggregators that I lean on in an attempt to stay on top of things. I’ve inevitably omitted some key resources, so please don’t be offended if you don’t see your name/blog/Twitter/etc. listed here (drop a link in the comments!). Whatever I write here now will be out of date in no time, so I’ll try to write an update post every year instead of every five.

Twitter

In the 2012 post I ended with Twitter, but I have to lead with it this time. Twitter is probably my most valuable resource for learning about the bleeding-edge developments in genomics & bioinformatics. It’s great for learning what’s new and contributing to the dialogue in your field, but only when used effectively.

I aggressively prune the list of people I follow to keep what I see relevant and engaging. I can tolerate an occasional digression into politics, posting pictures of you drinking with colleagues at a conference, or self-congratulatory announcements. But once these off-topic Tweets become the norm, I unfollow. I also rely on the built-in list feature. I follow a few hundred people, but I only add a select few dozen to a “notjunk” list that I look at when I’m short on time. Folks in this list don’t Tweet too often and have a high signal-to-noise ratio (as far as what I’m interested in reading). If I don’t get a chance to catch up on my entire timeline, I can at least breeze through recent Tweets from folks on this list.

I’m also wary of following extremely prolific users. For example – if someone’s been on Twitter less than a year, already has 20,000 Tweets, but only 100 followers, it tells me they’ve got a lot to say but nobody cares. I let the hive mind work for me in this case, using this Tweet-to-follower ratio as sort of a proxy for signal-to-noise.

I mostly follow individuals and aggregators, but I also follow a few organization accounts. These can be a mixed bag. Only a few organization accounts do this well, delivering interesting and applicable content to a targeted audience, while many more are poor attempts at marketing and self-promotion while not offering any substantive value or interesting content.

Individuals: In no particular order, here’s an incomplete list of people who Tweet content that I find consistently on-topic and interesting.

Others: Besides individual accounts, there are also a number of aggregators and organizations that I keep on a high signal-to-noise list.

Blogs

I follow these and other blogs using RSS. I’ve been happy with the free version of Feedly ever since Google Reader was killed. The web interface and iOS app have everything I need, and they both integrate nicely with other services like Evernote, Instapaper, Buffer, Twitter, etc. If you can’t find a direct link to the blog’s RSS feed, you can usually type the name of the blog into Feedly’s search bar and it’ll find it for you. Similar to my “notjunk” list in Twitter, I have a Favorites category in Feedly where I include only the feeds I absolutely wouldn’t want to miss.

These are some of the few that I try to read whenever something new is posted, and Feedly helps me keep those organized, either by “starring” something I want to come back to, or saving it for later with Instapaper. They’re in no particular order, and I’m sure I’ve forgotten something.

Others

I’m unsure how to categorize the rest. These are things like aggregators, Q&A sites/forums, and others.

Preprints!

Preprints in life sciences were nearly unheard of when I wrote the 2012 post. Now everybody’s doing it. There are still a few people using the arXiv Quantitative biology channel, and I’ll occasionally find something in PeerJ Preprints that grabs my attention.

bioRxiv is the biggest player here, hands down. The Alerts/RSS page lets you sign up for email alerts on particular topics, or subscribe to RSS feeds coming from particular categories that interest you. I subscribe to the Genomics and Bioinformatics feeds. I also follow several of the bioRxiv’s top-level and category Twitter feeds @biorxivpreprint, @biorxiv_bioinfo, and @biorxiv_genomic).

F1000 Research deserves some special attention here. It’s somewhere in-between a preprint server and a peer-reviewed publication. You can upload manuscripts (or other research outputs like posters or slides), and they’re immediately and permanently published, and given a DOI. Then one or more rounds of open peer review as well as public comment take place, and authors can update the published paper for further review. Check out the transcript estimates / gene inference paper I mentioned earlier. You’ll see it’s “version 2,” and was approved by two referees. If you look at the right-hand panel, you can actually go back and see the prior to revision, as well as see who reviewed it, what the reviewer wrote, and how the authors responded to those reviews. It’s an innovative platform where peer review is open and transparent, and is independent of publication, since papers are published before they are reviewed, and remain regardless of the outcome of the review. F1000 Research has a number of channels that are externally curated by different organizations, societies, conferences, etc. I subscribe to and get alerts about the R package and Bioconductor channels. Whenever a new preprint is dropped into one of these channels, I’ll get an email and an RSS item.

I only recently discovered PrePubMed, which looks very useful. PrePubMed indexes preprints from arXiv q-bio, PeerJ Preprints, bioRxiv, F1000Research, preprints.org, The Winnower, Nature Precedings, and Wellcome Open Research. In the tools box on the homepage, you can enter a search string and get back an RSS feed with results from that search. It looks like PrePubMed is maintained by a single person, but he’s made the entire thing open source, so you could presumably set this up and mirror it on your own, should you check back in 2021 and the link be dead.

Journals

I started with Journals in my 2012 post, but they’re last (and probably least) here. I still subscribe to a few journals’ RSS feeds, but in most cases, by the time I see a new Table of Contents hit my RSS reader, I probably saw the publications making the rounds on Twitter, blogs, or other channels mentioned above. It’s also no longer unusual to see a “publication” land where I read the preprint on biorXiv months ago, and perhaps even a blog post before that! What “publication” means is changing rapidly, and I’m sure the lines between a blog post, preprint, and journal article will be even blurrier in the year 2022 post.

How do you have the time to do this?

How do you not? It’s not as bad as it seems. I probably spend an hour each weekday scanning all the resources mentioned here, and I find the time well spent. I can breeze through my Twitter and RSS feeds on my bus ride into work, and saving things I actually want to look at later with a bookmark, star, favorite, Instapaper, etc.

I should have prefaced this whole article with the note that I hardly ever actually fully read any of the papers or blog posts I see here. If I see, for example, a new WGS variant caller published, I’ll glance at the figures benchmarking it against GATK and FreeBayes, and skim through the documentation on the GitHub README or BioConductor vignette. If either of these is missing or falls short, that’s usually enough for me to ignore the publication completely (don’t underestimate the importance of good documentation!).

It’s taken me a decade to compile and continually hone this list of resources to the things that I find useful and relevant. This is what works for me, now, in 2017. It’s not a one-size-fits-all, and the 2018-me will probably have a somewhat different list, but I hope you’ll find it useful. If your interests are similar to what I’ve discussed here, how do you stay current? What have I left out? Let me know in the comments!