One of the commonest bioinformatics questions, at Biostars and elsewhere, takes the form: “I have a list of identifiers (X); I want to relate them to a second set of identifiers (Y)”. HGNC gene symbols to Ensembl Gene IDs, for example.
When this occurs I have been known to tweet “the answer is BioMart” (there are often other solutions too) and I’ve written a couple of blog posts about the R package biomaRt in the past. However, I’ve realised that we need to take a step back and ask some basic questions that new users might have. How do I find what marts and datasets are available? How do I know what attributes and filters to use? How do I specify different genome build versions?
I’ve been meaning to write about Entrez Direct, henceforth called edirect, for some time. This tweet provided me with an excuse:
This post is not strictly the answer to that question. Instead we’ll ask: which parent IDs of records for insects in the NCBI Taxonomy database have the most species IDs?
I guess I’ve been around bioinformatics for the best part of 15 years. In that time, I’ve seen almost no improvement in the way biologists handle and use data. If anything I’ve seen a decline, perhaps because the data have become larger and more complex with no improvement in the skills base.
It strikes me when I read questions at Biostars that the problem faced by many students and researchers is deeper than “not knowing what to do.” It’s having no idea how to figure out what they need to know in order to do what they want to do. In essence, this is about how to get people into a problem-solving mindset so as they’re aware, for example that:
- it’s extremely unlikely that you are the first person to encounter this problem
- it’s likely that the solution is documented somewhere
- effective search will lead you to a solution even if you don’t fully understand it at first
- the tool(s) that you know are not necessarily the right ones for the job (and Excel is never the right tool for the job)
- implementing the solution may require that you (shudder) learn new skills
- time spent on those skills now is almost certainly time saved later because…
- …with a very little self-education in programming, tasks that took hours or days can be automated and take seconds or minutes
It’s good (and bad) to know that these issues are not confined to Australian researchers: here is It’s time to reboot bioinformatics education by Todd Harris. It is excellent and you should go and read it as soon as possible.
I am forever returning to PubMed data, downloaded as XML, trying to extract information from it and becoming deeply confused in the process.
Take the seemingly-simple question “how many retracted articles are there in PubMed?”
The blog post in question concerns conversion of PubMed PMIDs to BibTeX citations. However, a few things have changed since 2010.
Here’s what currently works.
Off to Melbourne tomorrow for perhaps my favourite annual work event: the Bioinformatics FOAM (Focus on Analytical Methods) meeting, organised by CSIRO.
Unfortunately, but for good reasons, it’s an internal event this year, but I’m putting my presentations online. I’ll be speaking twice; the first for Thursday is called “Online bioinformatics forums: why do we keep asking the same questions?” It’s an informal, subjective survey of the questions that come up again and again at bioinformatics Q&A forums such as Biostars and my attempt to understand why this is the case. Of course one simple answer might be selection bias – we don’t observe the users who came, found that their question already had an answer and so did not ask it again. I’ll also try to articulate my concern that many people view bioinformatics as a collection of recipe-style solutions to specific tasks, rather than a philosophy of how to do biological data analysis.
My second talk on Friday is called “Should I be dead? a very personal genomics.” It’s a more practical talk, outlining how I converted my own 23andMe raw data to VCF format, for use with the Ensembl Variant Effect Predictor. The question for the end – which I’ve left open – is this: as personal genomics becomes commonplace, we’re going to need simple but effective reporting tools that patients and their clinicians can use. What are those tools going to look like?
Looking forward to spending some time in Melbourne and hopefully catching up with this awesome lady.
Back in July, I was complaining about the latest abuse of the word “database” by biologists: the “PDF as database.”
This led to some very productive discussion using PubMed Commons and I’m happy to report that misidentified and contaminated cell lines are now included in the NCBI BioSample database.
As the news release notes, rather alarmingly:
This problem is so common it is thought that thousands of misleading and potentially erroneous papers have been published using cell lines that are incorrectly identified
So it would be useful if there were a direct link between the BioSample record for a cell line and PubMed records in which it was used…
Here’s a new way to abuse biological information: take a list of gene IDs and use them to create a completely fictitious, but very convincing set of microarray probeset IDs.
This one begins with a question at BioStars, concerning the conversion of Affymetrix probeset IDs to gene names. Being a “convert ID X to ID Y” question, the obvious answer is “try BioMart” and indeed the microarray platform ([MoGene-1_0-st] Affymetrix Mouse Gene 1.0 ST) is available in the Ensembl database.
However, things get weird when we examine some example probeset IDs: 73649_at, 17921_at, 18174_at. One of the answers to the question notes that these do not map to mouse.
The data are from GEO series GSE56257. The microarray platform is GPL17777. Description: “This is identical to GPL6246 but a custom cdf environment was used to extract data. The cdf can be found at the link below.”
Uh-oh. Alarm bells.
I’ve never attended a hackathon (hack day, hackfest or codefest). My impression of them is that there is, generally, a strong element of “working for the public good”: seeking to use code and data in new ways that maximise benefit and build communities.
Which is why I’m somewhat mystified by the projects on offer at the Sydney HealthHack. They read like tenders for consultants. Unpaid consultants.
The projects – a pedigree drawing tool, a workflow to process microscopy images, a statistical calculator and a mutation discovery pipeline – all describe problems that competent bioinformaticians could solve using existing tools in a relatively short time. For example, off the top of my head, ImageJ or CSIRO’s Workspace might be worth looking at for problem (2). The steps described in problem (4) – copy and paste between spreadsheets, manual inspection and manipulation of sequence data – should be depressingly familiar examples to many bioinformaticians. This project can be summarised simply as “you’re doing it wrong because you don’t know any better.”
The overall tone is “my research group requires this tool, but we’re unable to employ anyone to do it.” There is no sense of anything wider than the immediate needs of individual researchers. This does not seem, to me, what hackfest philosophy is all about.
This raises an issue that I think about a lot: how do we (the science community) best get the people with the expertise (in this case, bioinformaticians) to the people with the problems? In an ideal world the answer would be “everyone should employ at least one.” I wonder about the market (Australian or more generally) for paid consulting “biological data scientists”? We complain that we’re under-valued; well, perhaps it is we who are doing the valuation when we offer our skills for free.
Before we start: yes, we’ve been here before. There was the Biostars question “Calculating Time From Submission To Publication / Degree Of Burden In Submitting A Paper.” That gave rise to Pierre’s excellent blog post and code + data on Figshare.
So why are we here again? 1. It’s been a couple of years. 2. This is the R (+ Ruby) version. 3. It’s always worth highlighting how the poor state of publicly-available data prevents us from doing what we’d like to do. In this case the interesting question “which bioinformatics journal should I submit to for rapid publication?” becomes “here’s an incomplete analysis using questionable data regarding publication dates.”
Let’s get it out of the way then.