The data isn’t in the papers anymore, you know.

This week I was working on finishing up some training materials on the ENCODE data. We’ve talked about this before, and we’ve had some materials out already to support the ENCODE project, since we have a contract with the folks at UCSC to do some training on it. (The new materials should be out later this month.) But we were out doing a workshop on this data/software recently and we had a really great thing happen.

In the workshop we got to the exercise where I showed the attendees how to add the data for the GATA1 transcription factor binding sites to the visualization. This data is part of the Yale Transcription Factor Binding Site track.

In the front row of the training room, a researcher actually started to giggle.  Sometimes you can have fun in software training, but this was different. This woman was so happy to have discovered something she didn’t know before about GATA1 binding near her gene of interest that she was beside herself with delight.

Maybe this happens when she reads papers, too. But it struck me that what she had just done was come across something that isn’t in the papers. And that specific item may not be in the papers for a long time. But because she knew how to use the UCSC Genome Browser, and because she is now aware of the ENCODE data in the browser, she discovered something important for her research.

And that’s not in the literature. It’s in the databases.

I was also recently using the International Cancer Genome Consortium site’s new BioMart interface at their Data Coordination Center.  With their recent update they added some new features, I was using the new view of “Affected Genes” on that page. I picked a cancer type, I loaded up the Protein Coding genes, and there I was looking at the genes that had been repeatedly found to be affected in patient after patient. Some of the genes were not a surprise, certainly. But I sat there looking at data that a lot of people don’t know about–because it’s not in the papers yet. And it may not be for a long time.

Now, both of these “big data” projects have caveats: this data is pre-publication. Although there are some levels of QC, it should be considered as preliminary and you need to do due diligence before running off with conclusions about it. And both projects have data usage policies about how far you can take it before the embargo or moratorium is considered lifted. But still: you could make discoveries that no one else has made yet if you 1) are aware that this data is there, and 2) know how to use the software to get at it. There’s really no other way to know it.

That said, I know there are issues with the information in databases. A paper spoke to some issues of mis-annotation of data (Schnoes et al below):

Due to the rapid release of new data from genome sequencing projects, the majority of protein sequences in public databases have not been experimentally characterized; rather, sequences are annotated using computational analysis. The level of misannotation and the types of misannotation in large public databases are currently unknown and have not been analyzed in depth…..

So you need to be aware of that. And you need to confirm what you are seeing. But again–you need: 1) awareness of the tools used to do this, and 2) training on how to use tools to be sure you are getting appropriate information.

That’s also not in the papers anymore. It’s up to you.

There are so many projects of this nature out there now. We know of many species, data types, and topics that are just tossing great stuff into the ethers….and so many people don’t realize it. I just wish I had time to mine it all myself. There’s some real gems of discovery out there.  But you need a map, and you need some tools. And I want to hear more giggling, people. Get on it, please.

Schnoes, A., Brown, S., Dodevski, I., & Babbitt, P. (2009). Annotation Error in Public Databases: Misannotation of Molecular Function in Enzyme Superfamilies PLoS Computational Biology, 5 (12) DOI: 10.1371/journal.pcbi.1000605

Rosenbloom, K., Dreszer, T., Pheasant, M., Barber, G., Meyer, L., Pohl, A., Raney, B., Wang, T., Hinrichs, A., Zweig, A., Fujita, P., Learned, K., Rhead, B., Smith, K., Kuhn, R., Karolchik, D., Haussler, D., & Kent, W. (2009). ENCODE whole-genome data in the UCSC Genome Browser Nucleic Acids Research, 38 (Database) DOI: 10.1093/nar/gkp961

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