Category Archives: bioinformatics

utils4bioinformatics: all those “little snippets” in one place

Over the years, I’ve written a lot of small “utility scripts”. You know the kind of thing. Little code snippets that facilitate research, rather than generate research results. For example: just what are the fields that you can use to qualify Entrez database searches?

Typically, they end up languishing in long-forgotten Dropbox directories. Sometimes, the output gets shared as a public link. No longer! As of today, “little code snippets that do (hopefully) useful things” have a new home at Github.

Also as of today: there’s not much there right now, just the aforementioned Entrez database code and output. I’m not out to change the world here, just to do a little better.

When is db=all not db=all? When you use Entrez ELink.

Just a brief technical note.

I figured that for a given compound in PubChem, it would be interesting to know whether that compound had been used in a high-throughput experiment, which you might find in GEO. Very easy using the E-utilities, as implemented in the R package rentrez:

library(rentrez)
links <- entrez_link(dbfrom = "pccompound", db = "gds", id = "62857")
length(links$pccompound_gds)
# [1] 741

Browsing the rentrez documentation, I note that db can take the value “all”. Sounds useful!

links <- entrez_link(dbfrom = "pccompound", db = "all", id = "62857")
length(links$pccompound_gds)
# [1] 0

That’s odd. In fact, this query does not even link pccompound to gds:

length(names(links))
# [1] 39
which(names(links) == "pccompound_gds")
# integer(0)

It’s not a rentrez issue, since the same result occurs using the E-utilities URL.

The good people at ropensci have opened an issue, contacting NCBI for clarification. We’ll keep you posted.

On the road: CSS and eResearch Conference 2014

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.

New publication: A panel of genes methylated with high frequency in colorectal cancer

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.

Continue reading

A lesson in “reading before you tweet”

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.

BLATting the internet: the most frequent gene?

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?

Continue reading

Quilt plots. Like heat maps, only…heat maps

Stephen tweets:

A "quilt plot"

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…

Career advice: switching to computational research

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.