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.
File this one under “has troubled me (and others) for some years now, let’s try to resolve it.”
Let’s use the excellent R/rentrez package to search PubMed for articles that were retracted in 2013.
es <- entrez_search("pubmed", "\"Retracted Publication\"[PTYP] 2013[PDAT]", usehistory = "y")
#  117
117 articles. Now let’s fetch the records in XML format.
xml <- entrez_fetch("pubmed", WebEnv = es$WebEnv, query_key = es$QueryKey,
rettype = "xml", retmax = es$count)
Next question: which XML element specifies the “Date of publication” (PDAT)?
I’ve been complaining about this for years. They fixed it. The NCBI have reorganised their genomes FTP site and finally, Archaea are not lumped in with Bacteria.
Archaea are still included in the ASSEMBLY_BACTERIA directory; hopefully that’s next on the list.
[*] to be fair, they’ve always recognised Archaea – just not in a form that makes downloads convenient
Ever wondered whether the “>” symbol can, or does, appear in FASTA sequence headers at positions other than the start of the line?
I have a recent copy of the NCBI non-redundant nucleotide (nt) BLAST database on my server, so let’s take a look. The files are in a directory which is also named nt:
# %i = sequence ID; %t = sequence title
blastdbcmd -db /data/db/nt/nt -entry all -outfmt '%i %t' | grep ">" > ~/Documents/nt.txt
wc -l ~/Documents/nt.txt
# => 54451 /home/sau103/Documents/nt.txt
# and how many sequences total in nt?
blastdbcmd -list /data/db/nt/ -list_outfmt '%n' | head -1
# => 23745273
Short answer – yes, about 0.23% of nt sequence descriptions contain the “>” character. Inspection of the output shows that it’s used in a number of ways. A few examples:
# as "brackets" (very common)
emb|V01351.1| Sea urchin fragment, 3' to the actin gene in <SPAC01>
gb|GU086899.1| Cotesia sp. jft03 voucher BIOUG<CAN>:CPWH-0042 cytochrome oxidase subunit 1 (COI) gene, partial cds; mitochondrial
# to indicate mutated bases or amino acids
gb|M21581.1|SYNHUMUBA Synthetic human ubiquitin gene (Thr14->Cys), complete cds
dbj|AB047520.1| Homo sapiens gene for PER3, exon 1, 128+22(G->A) polymorphism
# in chemical nomenclature
gb|AF134414.1|AF134414 Homo sapiens B-specific alpha 1->3 galactosyltransferase (ABO) mRNA, ABO-*B101 allele, complete cds
# as "arrows"
gb|EU303182.1| Apoi virus note kitaoka-> canals ->NIMR nonstructural protein 5 (NS5) gene, partial cds
ref|XM_001734501.1| Entamoeba dispar SAW760 5'->3' exoribonuclease, putative EDI_265520 mRNA, complete cds
Something to keep in mind when writing code to process FASTA format.
6-way Venn banana
I thought nothing could top the classic “6-way Venn banana
“, featured in The banana (Musa acuminata) genome and the evolution of monocotyledonous plants
That is until I saw Figure 3 from Compact genome of the Antarctic midge is likely an adaptation to an extreme environment.
5-way Venn roadkill
What’s odd is that Figure 2 in the latter paper is a nice, clear R/ggplot2 creation, using facet_grid(), so someone knew what they were doing.
That aside, the Antarctic midge paper is an interesting read; go check it out.
This led to some amusing Twitter discussion which pointed me to *A New Rose : The First Simple Symmetric 11-Venn Diagram.
[*] +1 for referencing The Damned, if indeed that was the intention.
Let’s start by making one thing clear. Using coloured cells in Excel to encode different categories of data is wrong. Next time colleagues explain excitedly how “green equals normal and red = tumour”, you must explain that (1) they have sinned and (2) what they meant to do was add a column containing the words “normal” and “tumour”.
I almost hesitate to write this post but…we have to deal with the world as it is, not as we would like it to be. So in the interests of just getting the job done: here’s one way to deal with coloured cells in Excel, should someone send them your way.
“Take a look at the TP53 mutation database“, my colleague suggested. “OK then, I will”, I replied.
I present what follows as “a typical day in the life of a bioinformatician”.