• METAREP: JCVI Metagenomics Reports – an open source tool for high-performance comparative metagenomics. Hat tip to @kozo2 [Mary]
• PLoS now has a blogging network. Science blogging continues to diversify [Trey]
• USGS has a genomics site? I thought they just did earthquakes. From their page they refer to the sequencing of the fungus that’s affecting bat populations, and their team is involved with that. Neat. Plus lots of other interesting genetics of wildlife issues. And a microbiology component. Wowsa. H/T to @lakeganharris [Mary]
• Crowd-sourcing a genome project: to raise funds for sequencing the whale shark genome, you can buy hair bands. I’m not sure I get the linkage–but if I still had long hair I’d probably do it… Whale shark genome schwag has been unbridled! [Mary]
• Speaking of crowds (crowds of variations?.. something ), HapMap 3 paper just came out and Razib Khan has an excellent blog post about it. [Trey]
• Organizations that you might consider joining, or at least following: Scientists without Borders, UNESCO, and WAY: World Association of Young Scientists – Science, Remixed. [Jennifer]
• I might have to read the full article, based on this summary: “Sweet fruit from a poisonous kiss” by Delong Liu Full paper by Zhang et al at Science. 2010 Apr 9;328(5975):240-3. [Jennifer]
• ensembl wants your feedback. Check out their survey: http://bit.ly/cTSXyu [Mary]
• Check it out – the series of ScienceOnline interviews, or my specific interview – you decide which to read… [Jennifer]
• more than 2 weeks of science fun throughout North Carolina and including the MythBusters guys: NC Science Festival 2010 – Life is Your Lab! [Jennifer]

But impact factor measuring has it’s issues (Article-Level Metrics and the Evolution of Scientific Impact, Neylon and Wu. PLoS Biol 7: e1000242), and there is always a search to measure the impact of journals and individual articles better, or at least differently. Well, one of my favorite science sites and one of my favorite journal publishers ResearchBlogging.org and PLoS, have worked together to measure the impact of journal articles. PLoS has a lot of metrics to see what the ‘impact’ of an article might be, and now they’ve added a metric to see how many times it’s been written about on blogs using blog aggregators like Postgenomic, Blog Lines and Nature Blogs, and now ResearchBlogging.

I like the partnership with ResearchBlogging specifically because whereas the other blog aggregators are not necessarily picking up articles that discuss the science of the article (Postgenomic) or aggregate only a subset of science blogs out there (Nature Blogs), ResearchBlogging is specifically blogs posts discussing the research of  peer-reviewed articles.

Of course I don’t find this particularly useful to compare one article against another (the best articles aren’t always written about, and those that are might not be in the blog aggregators), but I do think this will be great way to carry on the conversation and dig deeper into the research topic.

You can view that metric at PLoS of any article, for example the one I link to above, click on the “metric” tab, scroll down a bit until you see the heading “Blog Coverage.” For that article, you’ll see two ResearchBlogging posts (as of this writing), a metric for this paper about metrics .

This method asks students to randomly pick and analyze unknown metagenomic DNA fragments from a real research sequence stockpile. The student’s mission, using Internet tools only, is to figure out from which organism the DNA comes from, and what biological function it might have. As well as gaining confidence and proficiency in bioinformatics, students experience the authentic research process of weighing the arguments, establishing prediction reliability, building hypotheses, and maintaining rigorous disourse.

The lecturers have put together  a teaching-annotation procedure in a publicly accessible “annotation environment” they call “Annotathon.” This web interface walks the student through the annotation process in a procedure as you see in the figure here. Since you can join and use this interface, I thought I’d give it a test drive.

You start by getting a user account. After that, a painless process, if you are a ‘student’ (as I am in this case), you are give a sequence. My sequence?

>GOS_748010 genomic DNA (North American East Coast: South of Charleston, SC)

TCTCATTATTTAGCCTTTGGACTATCAAAGAGTTATACATTAGGTAGTTATACTTTGGGTGTAGGATCAAATTTTCAATATGCACAGCTATTTACTGACA
ATAAATATGCTGTTTCCATTGATTTAGGACTTAAAAAACATATTTCTGATAAACTCAGGGCTGGTATTTTAATTGAAAACTTATCATCTAATAATATAGA
CTTACCTCTAAATAGTTCTTTAGGCTTTTCTATTTATAATAAAAAAATTAAAACTGAAATATTATTTGACTATAATTATTCATCAGTACATGATAATGGT
TTGCATCTAGGAGTCATTAACAAAAATAAATATCTGACATTAAATTTTGGCTATTCATTATATAAATCAAGAACGACCCTCTCATCTGGAGTTGACTTTA
TAATTAAAGAAAAATATAAGTTCATTTATTCAATTCTATCTTTAGAAAGTTCAAACTTAGGACTTTCACATTATTTTGGACTAGAGATATCAATTTAATC
CGTTGAGGAATTTGATTTAATTTCTTAATATATGGTGATAATTATTCCCATTATATAAAGGATGTCGATATGCTAGCTACAATTATAACCTTAGTTTCAT
CAATTTTATTTTCCCAGTCACTATTTTTTTCAGAGTATGCTGAGGGTACTTCTAATAATAAATATTTAGAGATATATAATCCGACTTCAGAATCTATTGA
TTTATCTGGGTATGCATTTCCTAGTACGGCAAATGAACCTTCTACTCCAGGTATGCATGAATATTGGAATGAATTTGATAGCGGTTCGATAATAGCCCCT
GGTGATGTATTTGTAATATGTCATGGCTCATCAGATGCGCTGATTCTAGCTGAATGCGATCAATTCCATACATATCTTAGTAATGGTGATGATGGATATT
GCTTAGTTTCACGACCTGAGAGCTCATATG

I’ve started the process using two of the three ORF finders they recommend, NCBI ORF Finder and EBI’s Transeq. Already I find there are advantages and disadvantages to both (or rediscover, I’ve used both before). I actually ended up using both for this because the displays were different. I liked that Transeq gives me all 6 frames easily in one place to copy and paste, and I liked the ability to immediately BLAST the sequence in NCBI’s tool. I found four possible ORFs in this sequence, the translation of the third frame from nt 570 to 929 one being the most likely a real coding sequence. The BLAST results of this translation show high similarity to a Flavobacteria bacterium hypothetical protein and a Hahella chejuensis extracellular nuclease protein, both species are marine bacteria (a larger ORF in the first frame had a low-similarity result to a rat sequence).

The next steps in the Annotathon, which I have not yet accomplished, are to find molecular weight, protein domains, sequence alignment and taxanomic placement through phylogenetic analysis. The tools they suggest for these are SMS, MWCALC (Molecular weight),  PROSITE, PFam, InterPro (protein domains), NCBI BLAST homolog search (or EBI’s BLAST interface), EBI’s Clustal interface, and the http://www.phylogeny.fr/ or Mobyle (phylogeny).

There are several things I think would improve the Annotathon interface. I’d like to see more integration of the tools into the annotation ‘cart’, if at least just a simple link to the different tools for each annotation section. I also, right off the bat, can think of several tools that might be nice to use or at least show a compare/contrast in analysis for teaching different phylogeny programs, SMART, etc). (oh, and of course a good introductory tutorial on all these tools would be nice for students But, these are minor quibbles to what looks like a great teaching tool. I know I’m having some fun with it and I’ll report as I go along over the next couple weeks. That brings me to their last conclusion of the paper…

These students have done some excellent annotations and the process allows their annotations to be submitted to the public databases.

“The 515 students that have taken part in the Annotathon over the past three years have analyzed a total of 2.3 Mb of ocean microbial DNA, representing 9,500 hours of cumulative annotation.”

And from the analysis, it appears their annotations are as good as those done by large automated annotation projects. Though I don’t see students taking over and solving the huge issue the scientific community has, and will increasingly have, with annotating the HUGE amounts of sequence data that we are generating, every little bit helps.. and if they are learning some valuable skills while contributing to our knowledge, it’s an excellent endevour

I suggest you go over and try your hand at it. Let me know what you are finding (here AND in their discussion boards).

The Gene Wiki, like the proposal for the ‘wikification of GenBank‘ and the now online Encyclopedia of Life, is an attempt to harness the power of the community to provide the community with a wealth of annotated information

But, even as the authors admit, the Gene Wiki’s success so far has been muted. Part of the solution to make this a more useful tool is reported in this paper. The authors seeded the gene wiki with entry data from Entrez Gene. This is based on the observation that editors are more likely to add or correct information on a pre-existing article than they are to create a new one. So far the success of this effort of seeding hasn’t been shown (but as the authors suggest, this is recent and as of this paper wasn’t announced).

The authors reference the  Nature article comparing Wikipedia vs. Encyclopedia Britannica showing that Wikipedia stacked up well. I’d caveat that, these were for the most ‘basic’ science comparison, comparisons of scientific concepts that would be expected a college student might get correct. But the authors are suggesting a wiki for something much more complex and deep, the annotation of a gene. Even with the basic comparison, Wikipedia only compared favorably in the sense that it didn’t do nearly as badly as many would expect and only almost as good as Encyclopedia Britannica. Research needs something more than this.

And this would bring me to what I consider the biggest hurdle. The Gene Wiki project is deep and complex science. Wikipedia is doing reasonably well in ‘general science’, but I’m not so sure the model will work for this kind of science. I’m not sure Wikipedia is the venue for this (a similar concern voiced by BBGM) or will be able to bring the level of completeness and accuracy required by scientific research. Wikis have found uses and success, but for every successful wiki, there are a few hundred (thousand) failures littering the internet landscape. For success, a wiki needs a readership knowledgable enough and large enough not only to contribute but to keep data accurate. Too small or too many unknowledgable people adding to an article allows too many errors to creep into and, more importantly in a wiki’s success, to remain in the wiki. I’m not convinced the knowlegable readership of a gene wiki will be large enough.

Perhaps I’d use it like I use Wikipedia now for general use, a starting off point for information and research, but one I never use as a definitive source or reference.

And that is what the authors state in the end:

Importantly, this gene wiki effort  is not meant to be a substitute for existing resources. Gene portals and model organism databases will continue to serve as authoritative references with a specific role for data curation and enforcement of data standards. Moreover, the structured and typed data in gene portals is amenable to incorporation into pipelines and systematic analyses in  a way the information in a gene wiki cannot [22]. Most importantly, because articles are dynamic and not subject to rigorous peer review, the gene wiki is not intended to be a reference that is cited in a traditional peer-reviewed article or used exclusively as a source of gene annotation. Nevertheless, we believe that this gene wiki will be a valuable launch pad for collaboratively summarizing knowledge, and we expect that scientists will synergistically use the gene wiki with traditional gene portals.

Huss, J.W., Orozco, C., Goodale, J., Wu, C., Batalov, S., Vickers, T.J., Valafar, F., Su, A.I. (2008). A Gene Wiki for Community Annotation of Gene Function. PLoS Biology, 6(7), e175. DOI: 10.1371/journal.pbio.0060175

There has been resistance from scientific publishers to the open access movement. Some fully opposing it, others like Nature Publishing Group’s official opinion is that it has no position, but that the business model is unproven and peer review an expensive endeavor. Nature has been experimenting with open source models though through the Molecular Systems Biology (an open access Nature journal) supporting self-archiving through public repositories, making an exception for papers reporting genomes and their open text mining initiative.

And now a new development: Wednesday, President Bush signed the Consolidated Appropriations Act of 2007 (H.R. 2764), which requires all NIH-funded published research to be deposited into PubMed Central (and thus open access) within 12 months of publication. The open access movement has just made a large step forward considering the annual NIH budget is over 29 billion and a lot of research articles are produced off that budget. A Blog Around the Clock has a lot of links and discussion about this new law.

Some of the chatter:

Gordon’s Notes feels the Alliance for Taxpayer Access (a group that pushed for this) is cleverly named, giving a more fiscal conservative feel to the whole endevour, and…:

…wonder[s] if Bush knew what he signed. The open access provision would have been buried deeply in the bill.

Petermr’s feels:

The hard work continues. But now all fulltext derived from NIH work will be available on PubMed. Other funders will follow suit (if they are not ahead). So our journal-eating-robot OSCAR will have huge amounts of text to mine.The good news is that we believe that this text-mining will, in itself, uncover new science. How much we don’t know, but we hope it’s significant. And if so, that will be a further argument for freeing the fulltext of every science publication.

And one commenter there points to this document (warning: downloads pdf file) in response to a enquiry about the last provision of the law that says “Provided, That the NIH shall implement the public access policy in a manner consistent with copyright law.”

Martin Fenner states in his Nature Network blog “Gobbleygook,”:

…this change will probably have a big impact on how most biomedical journals do their business. A wonderful christmas present for all scientists.

You can read more about the new law and the discussion from the link to Bora Zivkovic’s “Blog Around the Clock” or the Open Access News Blog. Additionally, Nature held a “web focus” on open access with debate, information and more.

One thing is for certain, this will change science publishing.