Let me state first, I completely agree with their premise, that databases and resources have become imperative. The earlier model of “publication of experimental results and sharing of the reated research materials” needs to be extended. As they state:

It is however no longer adequate to share data through traditional modes of publication, and, particularly with high throughput (‘-omics) technologies, sharing of datasets requires submission to public databases as has long been the case with nucleic acid and protein sequence data.

The authors state, factually, that the financial model for most biological databases (we are talking the thousands that exist), has often been a 3-5 year development funding, that once runs out, the infrastructure needs to be supported by another source. In fact, this has lead to the defunding of databases such as TAIR and VBRC (and many others), excellent resources with irreplaceable data and tools, that then must struggle to find funding to maintain the considerable costs of funding infrastructure and continued development.

The demands of scientific research, open, shared data, require a funding model that maintains the publicly available nature of these databases. And thus the problem as they state:

If, for financial reasons, BRCs are unable to perform their tasks under conditions that meet the requirements of sceintfic research and the deamnds of industry, scientists will either see valuable information lost or being transferred into strictly commercial environment with at east two consequences: (i) blockade of access to this information and/or high costs and (ii) loss of data and potentioal for technology transfer for the foreseeable future. In either case the effect on both the scientific and broader community will be detrimental.

Again, I agree.

They discuss several possible solutions to maintaining the viability of publicly available databases including a private-public dual tier system where for-profits paid an annual fee and academic researchers have free access. They mention Uniprot, which underwent a funding crisis over a decade ago, as an example. Uniprot (then Swissprot) went back to complete public funding in 2002. There are still several other databases that are attempting to fund themselves by such a model. BioBase is one where several databases have been folded. TransFac is one. There is a free, reduced functionality, version that is available to academics through gene-regulation.com and the fuller version for a subscription at BioBase. This former version allows some data to be shared, as one could see at VISTA or UCSC. I am not privy to the financials of BioBase and other similar models, and I assume that will work for some, but I agree with the authors that many useful databases and resources would be hard-pressed to be maintained this way.

Other possibilities include fully  including databases under a single public institution funding mechanism. The many databases of NCBI and EBI fit this model. In fact, there is even a recent case of a resource being folded into this model at NCBI. Again, this works for some, but not all useful resources.

Most will have to find variable methods for funding their databases. Considering the importance of doing so, it is imperative that viable models are found. The authors reject, out of hand, advertising. As they mention, most advertisers will not be drawn to website advertising without a visibility of at least 10,000 visitors per month. There might be some truth to this (and I need to read the reference they cite that use to back that up).

But the next model they suggest seems to me to have the same drawback. In this model, the database or resource would have a ‘partnership of core competencies.’ An example they cite is MMdb (not to be confused with MMDB). This virtual mutant mouse repository provides direct trial links to Invitrogen from it’s gene information to the product page. They mention that though 6 companies were approached, only one responded. It would seem that this model has the same issues as directly selling advertising.

They also mention that, at least for their research community of mouse functional genomics, “Institutional Funding” seems the best solution for long-term viability and open access. Unfortunately, until institutions like NIH and EMBL are willing or able to fund these databases, I’m not sure that’s thats a solution.

As they mention in the paper, the rate of growth of the amounts and types of data that is being generated is exponential. I am not sure that government or institutional funding can financially keep up with housing the infrastructure needed to maintain and further develop these databases so that all the data generated can remain publicly and freely accessible.

Information is should be free, but unfortunately it is not without cost. It will be interesting to see how funding of databases and resources evolves in this fast growing genomics world (and imperative we figure out solutions).

PS: On a personal note, the authors use their resource, EMMA (European Mouse Mutant Archive), as an example in the paper. I like the name since it’s the name of my daughter, but it just goes to prove that names come in waves. We named our daughter thinking few would name their daughter the same. When even databases name the same name, you know that’s not the case.

Chandras, C., Weaver, T., Zouberakis, M., Smedley, D., Schughart, K., Rosenthal, N., Hancock, J., Kollias, G., Schofield, P., & Aidinis, V. (2009). Models for financial sustainability of biological databases and resources Database, 2009 DOI: 10.1093/database/bap017

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).

Seattle, WA September 24, 2008 — OpenHelix today announced the availability of new tutorial suites on the Alternative Splicing and Transcript Diversity (ASTD) database, Entrez Protein and the Molecular Modeling Database (MMDB). ASTD is an European Bioinformatics Institute (EBI) resource for alternative splice events and transcripts for the human, mouse, and rat systems. Entrez protein is a comprehensive database of protein information brought to you by the National Center for Biotechnology Information (NCBI). MMDB is another NCBI resource which contains an extensive collection of three-dimensional protein structures with detailed annotation that can be used to learn about the structure and function of many proteins. Together these three tutorials give the researcher an excellent set of resources to carry their research from transcript to 3d protein structure.

The tutorial suites, available for single purchase or through a low-priced yearly subscription to all OpenHelix tutorials, contain a narrated, self-run, online tutorial, slides with full script, handouts and exercises. With the tutorials, researchers can quickly learn to effectively and efficiently use these resources. These tutorials will teach users:

ASTD

• to perform Quick and Advanced searches
• to navigate gene and transcript report pages
• to predict intron/exon boundaries and likely regulatory protein binding site
• to search manually curated data regarding alternate splicing

Entrez Protein

• to perform basic and advanced searches utilizing the many available tools and options
• to understand the protein records and exploit the many internal and external links you are provided with
• to explore some of the resources provided by the NCBI network of databases, such as “My NCBI”

MMDB

• to search MMDB using both basic and advanced query techniques
• to understand the detailed results you obtain
• to visualize and manipulate structures using NCBI’s Cn3D structural viewer
• to locate and view structurally aligned homologs

To find out more about these and other tutorial suites visit the OpenHelix Tutorial Catalog and OpenHelix or visit the OpenHelix Blog for up-to-date information on genomics.

About OpenHelix
OpenHelix, LLC, provides the genomics knowledge you need when you need it. OpenHelix currently provides online self-run tutorials and on-site training for institutions and companies on the most powerful and popular free, web based, publicly accessible bioinformatics resources. In addition, OpenHelix is contracted by resource providers to provide comprehensive, long-term training and outreach programs.