Understanding MAPQ scores in SAM files: does 37 = 42?

The official specification for the Sequence Alignment Map (SAM) format outlines what is stored in each column of this tab-separated value file format. The fifth column of a SAM file stores MAPping Quality (MAPQ) values. From the SAM specification:

MAPQ: MAPping Quality. It equals −10 log10 Pr{mapping position is wrong}, rounded to the nearest integer. A value 255 indicates that the mapping quality is not available.

So if you happened to know that the probability of correctly mapping some random read was 0.99, then the MAPQ score should be 20 (i.e. log10 of 0.01 * -10). If the probability of a correct match increased to 0.999, the MAPQ score would increase to 30. So the upper bounds of a MAPQ score depends on the level of precision of your probability (though elswhere in the SAM spec, it defines an upper limit of 255 for this value). Conversely, as the probability of a correct match tends towards zero, so does the MAPQ score.

So I'm sure that the first thing that everyone does after generating a SAM file is to assess the spread of MAPQ scores in your dataset. Right? Anyone?

< sound of crickets >

Okay, so maybe you don't do this. Maybe you don't really care, and you are happy to trust the default output of whatever short read alignment program that you used to generate your SAM file. Why should it matter? Will these scores really vary all that much?

Here is a frequency distribution of MAPQ scores from two mapping experiments. The bottom panel zooms in to more clearly show the distribution of low frequency MAPQ scores:

Distribution of MAPQ scores from two experiments: bottom panel shows zoomed in view of MAPQ scores with frequencies < 1%. Click to enlarge.

What might we conclude from this? There seems to be some clear differences between both experiments. The most frequent MAPQ scores in the first experiment are 42 followed by 1. In the second experiment, scores only reach a maximum value of 37, and scores of 0 are the second most frequent value.

These two experiments reflect some real world data. Experiment 1 is based on data from mouse, and experiment 2 uses data from Arabidopsis thaliana. However, that is probably not why the distributions are different. The mouse data is based on unpaired Illumina reads from a DNase-Seq experiment, wheras the A. thaliana data is from paired Illumina reads from whole genome sequencing. However, that still probably isn't the reason for the differences.

The reason for the different distributions is that experiment 1 used Bowtie 2 to map the reads whereas experiment 2 used BWA. It turns out that different mapping programs calculate MAPQ scores in different ways and you shouldn't really compare these values unless they came from the same program.

The maximum MAPQ value that Bowtie 2 generates is 42 (though it doesn't say this anywhere in the documentation). In contrast, the maximum MAPQ value that BWA will generate is 37 (though once again, you — frustratingly — won't find this information in the manual).

The data for Experiment 1 is based on a sample of over 25 million mapped reads. However, you never see MAPQ scores of 9, 10, or 20, something that presumably reflects some aspect of how Bowtie 2 calculates these scores.

In the absence of any helpful information in the manuals of these two popular aligners, others have tried doing their own experimentation to work out what the values correspond to. Dave Tang has a useful post on Mappinq Qualities on his Musings from a PhD Candidate blog. There are also lots of posts about mapping quality on the SEQanswers site (e.g. see here, here or here). However, the prize for the most detailed investigation of MAPQ scores — from Bowtie 2 at least — goes to John Urban who has written a fantastic post on his Biofinysics blog:

So in conclusion, there are 3 important take home messages:

  1. MAPQ scores vary between different programs and you should not directly compare results from, say, Bowtie 2 and BWA.
  2. You should look at your MAPQ scores and potentially filter out the really bad alignments.
  3. Bioinformatics software documentation can often omit some really important details (see also my last blog post on this subject).