Video Tip of the Week: Immune Epitope DB (IEDB)

This week’s tip was inspired by the recent NHGRI workshop of the future directions for funding and resourcing of genomics-related projects. Titled “Future Opportunities for Genome Sequencing and Beyond: A Planning Workshop for the National Human Genome Research Institute” brought together a lot of influential folks on this topic, and had them noodle on the priorities and major gaps in this arena that should get more attention going forward.

Much of the meeting was live-streamed, which was really great. You can see the video segments and sometimes the slides are available on the workshop page. One of the great things about this meeting was that there’s so much excitement about what scientists want to do, and all the terrific ideas that are out there. One of my personal favorites was the Human Cell Atlas presented by Aviv Regev. I’d love to work on that. I loved working on the Adult Mouse Anatomical Dictionary and Gene Expression Database at Jax.

But for today’s focus, I’ll turn to a totally different aspect of genomics research that intrigues me–the immune system. As an undergraduate in microbiology and immunology, the fact that microbes and their teeny genomes could wreak havoc on large mammals fascinated me (Ebola–I mean, seriously, it’s not that big). And that the hosts have developed the mix-and-match adaptable response and antibody system to do battle–clever stuff, as long as it doesn’t turn into an autoimmune situation…. But this could also be turned to good use if you want to battle cancer cells with immunotherapies. So when David Haussler’s talk brought that back around–the idea of the complexity of the immune response genomics which is not well characterized yet–I connected with that idea as well. And it struck me that I had not ever featured the Immune Epitope Database before, which Haussler had mentioned in his talk. It was also noted that this is an interesting system because it is also a hybrid of proteomics and genomics information that’s required to be wrangled. And if this is a direction that NHGRI will emphasize, it’s important to know what’s out there, and think about the ways to go forward.

So here’s Haussler’s talk to set the foundation, but there’s another video about the database I’ll point to below.

In this talk he mentioned NetMHC for peptide binding prediction as well, and ImmPort at NIAID. There was a quick mention of an unfunded prototype UCSC immunobrowser to keep an eye out for. And for the most part these resources aren’t new–you can find a number of publications that go back and describe the foundations and development over the years. And it seems to be a good solid foundation, and with appropriate support can continue to keep this important information coming.

To learn more about IEDB, you can access their documentation, which includes a whole list of video tutorials. Here I’ll highlight the intro/overview one–but there are others that offer specific guidance on other tasks. I can’t embed this one, so the link will take you over to the video at their site.

Click the image to visit the video page.

Click the image to visit the video page.

So have a look at the IEDB resources, and think about the future directions of this important aspect of genomics.

Quick links:

NHGRI workshop: http://www.genome.gov/27558042

IEDB: http://www.iedb.org/

Intro IEDB video: http://www2.immuneepitope.org/videos/site_overview.cfm

NetMHC: http://www.cbs.dtu.dk/services/NetMHC/

ImmPort: http://immport.niaid.nih.gov/

References:

Vita R., J. A. Greenbaum, H. Emami, I. Hoof, N. Salimi, R. Damle, A. Sette & B. Peters (2010). The Immune Epitope Database 2.0, Nucleic Acids Research, 38 (Database) D854-D862. DOI: http://dx.doi.org/10.1093/nar/gkp1004

Kim Y., Z. Zhu, D. Tamang, P. Wang, J. Greenbaum, C. Lundegaard, A. Sette, O. Lund, P. E. Bourne & M. Nielsen & (2012). Immune epitope database analysis resource, Nucleic Acids Research, 40 (W1) W525-W530. DOI: http://dx.doi.org/10.1093/nar/gks438

Lundegaard C. & M. Nielsen (2008). Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers, Bioinformatics, 24 (11) 1397-1398. DOI: http://dx.doi.org/10.1093/bioinformatics/btn128

Bhattacharya S., Linda Gomes, Patrick Dunn, Henry Schaefer, Joan Pontius, Patty Berger, Vince Desborough, Tom Smith, John Campbell & Elizabeth Thomson & (2014). ImmPort: disseminating data to the public for the future of immunology, Immunologic Research, 58 (2-3) 234-239. DOI: http://dx.doi.org/10.1007/s12026-014-8516-1