Lessons Learned at the JGI Users Meeting

Well, the Joint Genome Institute (JGI) Users Meeting is Over. For some rapid fire notes on the meeting see the FriendFeed room here. where Jason Stajich, Tom Sharpton and I (and occasionally a few others) took notes on the whole thing as it was happening.

So – what were the lessons learned? What were the main points? So – if you want to know what we were writing about – and don’t want to read the notes (they are quite fun actually) how about a word cloud? Well, here is one, which I made by taking the notes, editing out some of the names and other non notes text, and then pasting them into TagCrowd.Com and I got this to the left here:

Not actually a bad representation of many of the topics.  
But you know, this does not per se capture the big points, just the common points.  So I guess if you want the key points, you have to think a bit. First, you have to realize this is a JGI focused meeting (which of course is the whole point of the meeting – it is the JGI User Meeting after all) and most of the people work with JGI in some way so it is a bit hard to see the forrest of genomics for the JGI trees.    And here are my top lessons I got out of the meeting after trying to not get too biased by the JGI focus (note – I have an Adjunct Appt. at JGI and do lots of things through there so I am sure I cannot remove the JGI focus completely)
  1. NextGen sequencing continues to open up new windows into biology.
  2. Ecological and population genomics are truly the next big thing.
  3. Related to the above point, one of the next revolutions is going to be in high throughput phenotyping — after all, we cannot solve the genotype-phenotype problem when we only know the genotype.
  4. Model / reference organisms are still in, but every single organism on the planet is now in play.
  5. NextGen sequencing has completely outrun the ability of even good bioinformatics people to keep up with the data and to use it well.
  6. Related to the above point, the NextNextGen (e.g., Pacific Biosciences) seems to be barreling along and almost ready for prime time.  WTF are we going to do in terms of informatics then?
  7. Following up on the above point- we desperately need a MASSIVE effort in the development of tools for “normal” biologists to make better use of massive sequence databases.
  8. I am happy to report that just about everyone seems to be trying to use an evolutionary perspective as part of their work – especially in the selection of organisms for sequencing
  9. I am sorry to report that many of the evolutionary “perspectives” are a bit off kilter.
  10. Sequencing is definitely not over – it is just getting started.
  11. People pushing the technology (e.g., George Church, Craig Venter) into new arenas definitely inspire the crowds.
  12. If you study a plant or an animal and are not studying the microbes that live with them, you are missing something.
  13. If you study ANY organism and are ignoring epigenetics you are behind the curve
  14. Open Access journals like PLoS Biology and PLoS One and Open Science got some huge props at this meeting, for example, with Venter showing many PLoS related images, many others showing stuff from OA journals, George Church talking about Open Source sequencers and Open personal genomics, and many referring to Open genomics databases.  Still some areas in need of improvement (e.g., not enough publishing in open access journals still) but the move in the direction of openness is great.
  15. Genome Centers definitely each have their own flavors with JGI positioning itself well in the niches of Ecology, Evolution, and Energy.
  16. Genome Centers are definitely going to have to reinvent themselves as the sequencing capacity for individual labs goes up and up with Illumina/Roche/ABI Solid continuing to spread.  Bigger, better, faster, more is one way they can stay ahead of the curve.
  17. Education and training did not get as much play during the talks as I would have hoped.  I mentioned it a bit but I do not recall too many other mentions.  Too bad as the real potential for the democritization of sequencing comes from people getting trained in how to generate and handle the data and how to at least think about it even if they do not use it directly.
  18. Organismal biology is still desperately important in all of this work – if you know a lot about the organism as a whole then you already are a systems biologist.
  19. Genomic characterization of entire multi-organism systems is on the rise and this is not just microbiota stuff but also things like host-pathogen interactions and symbioses and so on.
  20. Reading DNA is being used in every which way imaginable.  Next up – writing DNA.  
This entry was posted in Genomics and tagged , , by Jonathan Eisen. Bookmark the permalink.

About Jonathan Eisen

I am an evolutionary biologist and a Professor at U. C. Davis. (see my lab site here). My research focuses on the origin of novelty (how new processes and functions originate). To study this I focus on sequencing and analyzing genomes of organisms, especially microbes and using phylogenomic analysis

8 thoughts on “Lessons Learned at the JGI Users Meeting

  1. Thanks very much for taking the time to distill the important points of the meeting. I skimmed the live blogging thread on FriendFeed from time to time; this is precisely what I had hoped would come about to summarize that thread. Much appreciated.

    Like

  2. the live feed was fun and kept all of us following the talks … and I hope it is useful in some ways … but even I had a hard time going back over it … so I wanted to get some thoughts down about the big picture

    Like

  3. Good stuff. It seems like a major impediment to doing population and ecological genomics has been that most sequencing technologies are designed to sequence lots of material from a single organism (or a pooled sample), did anybody talk about improved methods for obtaining homologous sequence data across a suite of related individuals or populations? Wouldn’t it be nice if next generation sequencing could be used to build phylogenetic datasets (in a more efficient manner than just independently generating EST data for numerous organisms)?

    Like

  4. I have been asking about this (sequencing homologous regions in a high throughput manner) for years and years. I think there is no simple way to do it with next gen sequencing unless you do many many many tagged PCR primers and then you use different pairs for each organism — then you pool together all the PCR products and sequence in a run and deconvolute the tags. Maybe we should try it?

    Like

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s