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”.
This post is an apology and an attempt to make amends for contributing to the decay of online bioinformatics resources. It’s also, I think, a nice example of why reproducible research can be difficult.
Come back in time with me 10 years, to 2004.
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.
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:
links <- entrez_link(dbfrom = "pccompound", db = "gds", id = "62857")
#  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")
#  0
That’s odd. In fact, this query does not even link pccompound to gds:
#  39
which(names(links) == "pccompound_gds")
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.
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.