When your tools are broken, just change the data

It’s been 3 years since we last visited that old favourite recurring topic, data corruption by Excel. Specifically, the unwanted auto-conversion of identifiers that look like dates, e.g. SEPT1, to – well, dates.

Here’s a new twist – well, a two year-old twist in fact, as I don’t keep up to date with this field any longer:

Yes, in 2017 the HGNC decided that the solution to this long-standing issue is to rename the offending genes to prevent the auto-conversion. I’m yet to determine whether anything more came of the proposal.

It is I suppose a practical suggestion that will work. The newsletter states that:

Our initial consultation with the research community publishing on these genes had very mixed results

I bet it did. However, given that ongoing consultation with the research community about the inappropriate use of software has had essentially no results in 15+ years, perhaps it is the most effective solution to the problem.

50% bananas

factoid

Today in “blog posts that have spent two years in the draft folder” – “Humans are 50% banana.”

“Humans are 50% banana.”

Perhaps you have heard this statement, or one like it. It seems to be widely-quoted. As an example it’s hard to go past this article from UK tabloid The Mirror which, in addition to the banana, also informs us that “the entire internet weighs about the same as one large strawberry”. I don’t even know where to begin with that one.

A couple of years ago, between jobs and with time on my hands, I thought I’d go in search of the source for this factoid.

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Twitter Coverage of the Lorne Genome Conference 2017

Things to know about Lorne in the state of Victoria, Australia.

  • It’s situated on the Great Ocean Road, a major visitor attraction and a great way to see the scenic coastline of the region
  • It’s home to a number of life science conferences including Lorne Genome 2017

tweets-by-day-hour-1This week’s project then: use R to analyse coverage of the 2017 meeting on Twitter. I last did something similar for the ISMB meeting in 2012. How things have changed. Back then I prepared PDF reports using Sweave, retrieved tweets using the twitteR package and struggled with dates and time when plotting timelines. This time around I wrote RMarkdown in RStudio, tried out the newer rtweet package and, thanks to packages such as dplyr and lubridate, the data munging is all so much cleaner and simpler.

So without further ado here is the Github repository.

The report examines several aspects of the conference coverage under the broad headings of timeline, users, networks, retweets, favourites, quotes, media and text.

Better living through informatics: in search of koalas

In 2015, I’d like to write, think and do more about things that I care about. One of those things happens to be the koala. Now, this being a blog about bioinformatics and computational biology, I can’t just start writing about any old thing that takes my fancy…I guess. So in this post I’m going to stretch the definition to include ecological informatics and tell you the story of how I achieved a long-held ambition using one of my favourite online resources, The Atlas of Living Australia. And then we’ll wrap up with a quick survey of the (sorry) state of marsupial genomics.
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Finally, NCBI Genomes recognises Archaea*

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.

GenBank: ftp://ftp.ncbi.nlm.nih.gov/genomes/genbank/archaea/
RefSeq:  ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/archaea/

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

Venn figures go wrong

6-way Venn banana

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

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.

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?

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Using the Ensembl Variant Effect Predictor with your 23andme data

I subscribe to the Ensembl blog and found, in my feed reader this morning, a post which linked to the Variant Effect Predictor (VEP). The original blog post, strangely, has disappeared.

Not to worry: so, the VEP takes genotyping data in one of several formats, compares it with the Ensembl variation + core databases and returns a summary of how the variants affect transcripts and regulatory regions. My first thought – can I apply this to my own 23andme data?

Read the rest…