The top 10 #PLOSBuzzFeed tweets that will put a smile on your face

It all started so innocently. Nick Loman (@pathogenomenick) expressed his dissatisfaction with yet another PLOS Computational Biology article that uses the 10 Simple Rules… template:


There were two immediate responses from Kai Blin (@kaiblin) and Phil Spear (@Duke_of_neural):


I immediately saw the possibility that this could become a meme-worthy hashtag, so I simply retweeted Phil’s tweet, added the hashtag #PLOSBuzzFeed, and waited to see what would happen (as well making some of my own contributions).

At the time of writing — about 10 hours later — there have been several hundred tweets using this hashtag. Presented in reverse order, here are the most ‘popular’ tweets from today (as judged by summing retweets and favorites):

How to cite bioinformatics resources

I saw a post on Biostars today that asked how specific versions of genome assemblies should be cited. This question also applies to the more general issue of citing any bioinformatics resource which may have multiple releases/versions which are not all formally published in papers. Here is how I replied:

Citing versions of any particular bioinformatics/genomics resources can get tricky because there is often no formal publication for every release of a given dataset. Further complicating the situation is the fact that you will often come across different dates (and even names) for the same resource. E.g. the latest cow genome assembly generated by the University of Maryland is known as 'UMD 3.1.1'. However, the UCSC genome browser uses their own internal IDs for all cow genome assemblies and refers to this as 'bosTau8'. Someone new to the field might see the UCSC version and not know about the original UMD name.

Sometimes you can use dates of files on FTP sites to approximately date sequence files, but these can sometimes change (sometimes files accidentally get removed and replaced from backups, which can change their date).

The key thing to aim for is to provide suitable information so that someone can reproduce your work. In my mind, this requires 2–3 pieces of information:

  1. The name or release number of the dataset you are downloading (provide alternate names when known)
  2. The specific URL for the website or FTP site that you used to download the data
  3. The date on which you downloaded the data

E.g. The UMD 3.1.1 version of the cow genome assembly (also known as bosTau8) was downloaded from the UCSC Genome FTP site (ftp://hgdownload.cse.ucsc.edu/bosTau8/bigZips/bosTau8.fa.gz).

When no version number is available — it is very unhelpful not to provide version numbers of sequence resources: they can, and will change — I always refer to the date that I downloaded it instead.

Get shorty: the decreasing usefulness of referring to 'short read' sequences

I came across a new paper today:

When I see such papers, I always want to know 'what do you consider short?'. This particular paper makes no attempt to define what 'short' refers to, with the only mention of the 'S' word in the paper being as follows:

Finally, qAlign stores metadata for all generated BAM files, including information about alignment parameters and checksums for genome and short read sequences

There are hundreds of papers that mention 'short read' in their title and many more which refer to 'long read' sequences.

But 'short' and 'long' are tremendously unhelpful terms to refer to sequences. They mean different things to different people, and they can even mean different things to the same person at different times. I think that most people would agree that Illumina's HiSeq and MiSeq platforms are considered 'short read' technologies. The HiSeq 2500 is currently capable of generating 250 bp reads (in Rapid Run Mode), yet this is an order of magnitude greater than when Illumina/Solexa started out generating ~25 bp reads. So should we refer to these as long-short reads?

The length of reads generated by the first wave of new sequencing technologies (Solexa/Illumina, ABI SOLiD, and Ion Torrent) were initially compared to the 'long' (~800 bp) reads generated by Sanger sequencing methods. But these technologies have evolved steadily. The latest reagent kits for the MiSeq platform offer the possibility of 300 bp reads. However, if you perform paired end sequencing of libraries with insert sizes of ~600 bp, then you may end up generating single consensus reads that approach this length. Thus we are already at the point where a 'short read' sequencing technology can generate some reads that are longer than some of the reads produced by the former gold-standard 'long read' technology.

But the read lengths of any of these technologies pales into comparison when we consider the output of instruments from Pacficic Biosciences and Oxford Nanopore. By their standards, even Sanger sequencing reads could be considered 'short'.

If someone currently has reads that are 500-600 bp in length, it is not clear whether any software tool that proclaims to work with 'short reads' is suitable or not. Just as the 'Short Read Archive' (SRA) became the more-meaningfully-named Sequence Read Archive, so we as a community should banish these unhelpful names. If you develop tools that are optimized to work with 'short' or 'long' read data, please provide explicit guidelines as to what you mean!

To conclude:

There are no 'short' or 'long' reads, there are only sequences that are shorter or longer than other sequences.

101 questions with a bioinformatician #23: Todd Harris

Todd Harris is a Bioinformatics Consultant and Project Manager at WormBase. I first came to know Todd when I was also working on the WormBase project. As part of the UK operation (based at the Sanger Institute), we would frequently refer to him as 'SuperTodd' for his amazing skills at single-handedly keeping the WormBase website updated and working smoothly.

Read More