Regarding the current state of bioinformatics training →

Todd Harris (@tharris) is on a bit of a roll at the moment. Last month I linked to his excellent blog post regarding community annotation, and today I find myself linking to his latest blog post:

Todd makes a convincing argument that bioinformatics education has largely failed, and he lists three reasons for this, the last of which is as follows:

Finally, the nature of much bioinformatics training is too rarefied. It doesn’t spend enough time on core skills like basic scripting and data processing. For example, algorithm development has no place in a bioinformatics overview course, more so if that is the only exposure to the field the student will have.

I particularly empathize with this point. There should be a much greater emphasis on core data processing skills in bioinformatics training, but ideally students should be getting access to some of these skills at an even earlier age. Efforts such as the Hour of Code initiative are helping raise awareness regarding the need to teach coding skills — and it's good to see the President join in with this — but it would be so much better if coding was part of the curriculum everywhere. As Steve Jobs once said:

"I think everybody in this country should learn … a computer language because it teaches you how to think … I view computer science as a liberal art. It should be something that everybody learns, takes a year in their life, one of the courses they take is learn how to program" — Steve Jobs, 1995.

Taken from 'Steve Jobs: The Lost Interview

Maybe this is still a pipe dream, but if we can't teach useful coding skills for everyone, we should at least be doing this for everyone who is considering any sort of career in the biological sciences. During my time at UC Davis, I've helped teach some basic Unix and Perl skills to many graduate students, but frustratingly this teaching has often come at the end of their first year in Grad School. By this point in their graduate training, they have often already encountered many data management problems and have not been equipped with the necessary skills to help them deal with those problems.

I think that part of the problem is that we still use the label 'bioinformatics training' and this reinforces the distinction from a more generic 'biological training'. It may once have been the case that bioinformatics was its own specialized field, but today I find that bioinformatics mostly just describes a useful set of data processing skills…skills which will be needed by anybody working in the life sciences.

Maybe we need to rebrand 'bioinformatics training', and use a name which better describes the general importance of these skills ('Essential data training for biologists?'). Whatever we decide to call it, it is clear that we need it more than ever. Todd ends his post with a great piece of advice for any current graduate students in the biosciences:

You should be receiving bioinformatics training as part of your core curriculum. If you aren’t, your program is failing you and you should seek out this training independently. You should also ask your program leaders and department chairs why training in this field isn’t being made available to you.


Thinking of naming your bioinformatics software? Be afraid (of me), be very afraid (of me)

Saw this tweet today by Jessica Chong (@jxchong):

I like the idea that people might be fearful of choosing a name that could provoke me into writing a JABBA post. If my never-ending tirade about bogus bioinformatics acronyms causes some people to at least think twice about their intended name, then I will take that as a minor victory for this website!

More madness with MAPQ scores (a.k.a. why bioinformaticians hate poor and incomplete software documentation)

I have previously written about the range of mapping quality scores (MAPQ) that you might see in BAM/SAM files, as produced by popular read mapping programs. A very quick recap:

  1. Bowtie 2 generates MAPQ scores between 0–42
  2. BWA generates MAPQ scores between 0–37
  3. Neither piece of software describes the range of possible scores in their documentation
  4. The SAM specification defines the possible ranges of the MAPQ score as 0–255 (though 255 should indicate that mapping quality was not available)
  5. I advocated that you should always take a look at your mapped sequence data to see what ranges of scores are present before doing anything else with your BAM/SAM files

So what is my latest gripe? Well, I've recently been running TopHat (version 2.0.13) to map some RNA-Seq reads to a genome sequence. TopHat uses Bowtie (or Bowtie 2) as the tool to do the intial mapping of reads to the genome, so you might expect it to generate the same range of MAPQ scores as the standalone version of Bowtie.

But it doesn't.

From my initial testing, it seems that the BAM/SAM output file from TopHat only contains MAPQ scores of 0, 1, 3, or 50. I find this puzzling and incongruous. Why produce only four MAPQ scores (compared to >30 different values that Bowtie 2 can produce), and why change the maximum possible value to 50? I turned to the TopHat manual, but found no explanation regarding MAPQ scores.

Turning to Google, I found this useful Biostars post which suggests that five MAPQ values are possible with TopHat (you can also have a value of 2 which I didn't see in my data), and that these values correspond to the following:

  • 0 = maps to 10 or more locations
  • 1 = maps to 4-9 locations
  • 2 = maps to 3 locations
  • 3 = maps to 2 locations
  • 50 = unique mapping

The post also reveals that, confusingly, TopHat previously used a value of 255 to indicate uniquely mapped reads. However, I then found another Biostars post which says that a MAPQ score of 2 isn't possible with TopHat, and that the meaning of the scores are as follows:

  • 0 = maps to 5 or more locations
  • 1 = maps to 3-4 locations
  • 3 = maps to 2 locations
  • 255 = unique mapping

This post was in reference to an older version of TopHat (1.4.1) which probably explains the use of the 255 score rather than 50. The comments on this post reflect some of the confusion over this topic. Going back to the original Biostars post, I then noticed a recent comment suggesting that MAPQ scores of 24, 28, 41, 42, and 44 are also possible with TopHat (version 2.0.13).

As this situation shows, when there is no official explanation that fully describes how a piece of software should work, it can lead to mass speculation by others. Such speculation can sometimes be inconsistant which can end up making things even more confusing. This is what drives bioinformaticians crazy.

I find it deeply frustrating when so much of this confusion could be removed with better documentation by the people that developed the original software. In this case the documentation needs just one paragraph added; something along the lines of…

Mapping Quality scores (MAPQ)
TopHat outputs MAPQ scores in the BAM/SAM files with possible values 0, 1, 2, or 50. The first three values indicate mappings to 5, 3–4, or 2 locations, whereas a value of 50 represents a unique match. Please note that older versions of TopHat used a value of 255 for unique matches. Further note that standalone versions of Bowtie and Bowie 2 (used by TopHat) produce a different range of MAPQ scores (0–42).

Would that be so hard?

New paper provides a great overview of the current state of genome assembly →

The following paper by Stephen Richards and Shwetha Murali has just appeared in the journal Current Opinion in Insect Science:

Best practices in insect genome sequencing: what works and what doesn’t

In some ways I wish they had chosen a different title as the focus of this paper is much more about genome assembly than genome sequencing. Furthermore, it provides a great overview of all of the current strategies in genome assembly. This should be of interest to any non-insect researchers interested in the best way of putting a genome together. Here is part of the legend from a very informative table in the paper:

Table 1 — De novo genome assembly strategies:
Assembly software is designed for a specific sequencing and assembly strategy. Thus sequence must be generated with the assembly software and algorithm in mind, choosing a sequence strategy designed for a different assembly algorithm, or sequencing without thinking about assembly is usually a recipe for poor un-publishable assemblies. Here we survey different assembly strategies, with different sequence and library construction requirements.