Next week I’ll be in Melbourne for one of my favourite meetings, the annual Computational and Simulation Sciences and eResearch Conference.
The main reason for my visit is the Bioinformatics FOAM workshop. Day 1 (March 27) is not advertised since it is an internal CSIRO day, but I’ll be presenting a talk titled “SQL, noSQL or no database at all? Are databases still a core skill?“. Day 2 (March 28) is open to all and I’ll be talking about “Learning from complete strangers: social networking for bioinformaticians“.
I imagine these and other talks will appear on Slideshare soon, at both my account and that of the Australian Bioinformatics Network.
I’m also excited to see that Victoria Stodden is presenting a keynote at the main CSS meeting (PDF) on “Reproducibility in Computational Science: Opportunities and Challenges”.
Hope to see some of you there.
I’m pleased to announce an open-access publication with my name on it:
Mitchell, S.M., Ross, J.P., Drew, H.R., Ho, T., Brown, G.S., Saunders, N.F.W., Duesing, K.R., Buckley, M.J., Dunne, R., Beetson, I., Rand, K.N., McEvoy, A., Thomas, M.L., Baker, R.T., Wattchow, D.A., Young, G.P., Lockett, T.J., Pedersen, S.K., LaPointe L.C. and Molloy, P.L. (2014). A panel of genes methylated with high frequency in colorectal cancer. BMC Cancer 14:54.
So, I read the title:
Mining locus tags in PubMed Central to improve microbial gene annotation
and skimmed the abstract:
The scientific literature contains millions of microbial gene identifiers within the full text and tables, but these annotations rarely get incorporated into public sequence databases.
and thought, well OK, but wouldn’t it be better to incorporate annotations in the first place – when submitting to the public databases – rather than by this indirect method?
The point, of course, is to incorporate new findings from the literature into existing records, rather than to use the tool as a primary method of annotation. I do believe that public databases could do more to enforce data quality standards at deposition time, but that’s an entirely separate issue.
Big thanks to Michael Hoffman for a spirited Twitter discussion that put me straight.
I enjoyed this story from the OpenHelix blog today, describing a Microsoft Research project to mine DNA sequences from web pages and map them to UCSC genome builds.
Laura DeMare asks: what was the most-hit gene?
A “quilt plot”
Quilt plots. Sounds interesting. The link points to a short article in PLoS ONE
, containing a table and a figure. Here is Figure 1.
If you looked at that and thought “Hey, that’s a heat map!”, you are correct. That is a heat map. Let’s be quite clear about that. It’s a heat map.
So, how do the authors justify publishing a method for drawing heat maps and then calling them “quilt plots”?
Read the rest…
Laboratory work, of the “wet” kind, not working out for you? Or perhaps you just need new challenges. Think you have some aptitude with data analysis, computers, mathematics, statistics? Maybe a switch to computational biology is what you need.
That’s the topic of the Nature Careers feature “Computing: Out of the hood“. With thoughts and advice from (on Twitter) @caseybergman, @sarahmhird, @kcranstn, @PavelTomancak, @ctitusbrown and myself.
I enjoyed talking with Roberta and she did a good job of capturing our thoughts for the article. One of these days, I might even write here about my own journey in more detail.
This bioinformatician, at least. Hate is a strong word. Perhaps “dislike” is better.
Short answer: because you can’t get data out of them easily, if at all. Longer answer:
Read the rest…
When dealing with data from high-throughput experimental platforms such as microarrays, it’s important to account for potential batch effects. A simple example: if you process all your normal tissue samples this week and your cancerous tissue samples next week, you’re in big trouble. Differences between cancer and normal are now confounded with processing time and you may as well start over with new microarrays.
Processing date is often a good surrogate for batch and it was once easy to extract dates from Affymetrix CEL files using Bioconductor. It seems that this is no longer the case.
Read the rest…
Just how many (bad) -omics are there anyway? Let’s find out.
Update: code and data now at Github
Read the rest…
Here’s a tip. When you write an article about your software, the title of which indicates that open-source is important:
A universal open-source Electronic Laboratory Notebook
but you then:
- provide almost no details in the abstract
- do not provide a link to a website or repository from which your “free” software can be obtained
- choose not to make the article open access
- and put the installation instructions in a supplementary data file which is also not open access
Don’t be surprised when no-one uses your software.
Or is the publication more important to you than the product?