Extracting shotgun reads based on coverage in the data set (assembly-free)

Written by: C. Titus Brown

Primary Source: Living in an Ivory Basement

In recent days, we’ve gotten several requests, including two or three on the khmer mailing list, for ways to extract shotgun reads based on their coverage with respect to the reference. This is fairly easy if you have an assembled genome, but what if you want to avoid doing an assembly? khmer can do this fairly easily using techniques taken from the digital normalization work, which allows you to estimate read coverage directly from the data.

The below is a recipe for computing coverage spectra and slicing reads out of a data set based on their coverage, with no assembly required. It’s the first of what I hope to be many practical and useful recipes for working with shotgun data. Let me know what you think!

Uses for extracting reads by coverage include isolating repeats or pulling out mitochondrial DNA. This approach won’t work on digitally normalized reads, and is primarily intended for genomes and low-complexity metagenomes. For high-complexity metagenomes we recommend partitioning.

Note: at the moment, the khmer script slice-reads-by-coverage is in the khmer repository under branch feature/collect_reads. Once we’ve merged it into the master branch and cut a release, we’ll remove this note and simply specify the khmer release required.

This recipe uses code from khmer-recipes and dbg-graph-null.

Let’s assume you have a simple genome with some 5x repeats, and you’ve done some shotgun sequencing to a coverage of 150. If your reads are in reads.fa, you can generate a k-mer spectrum from your genome with k=20

load-into-counting.py -x 1e8 -k 20 reads.kh reads.fa
abundance-dist.py -s reads.kh reads.fa reads.dist
./plot-abundance-dist.py reads.dist reads-dist.png --ymax=300

and it would look something like this:


For this (simulated) data set, you can see three peaks: one on the far right, which contains the high-abundance k-mers from your repeats; one in the middle, which contains the k-mers from the single-copy genome; and one all the way on the left at ~1, which contains all of the erroneous k-mers.

This is a useful diagnostic tool, but if you wanted to extract one peak or another, you’d have to compute a summary statistic of some sort on the reads. The khmer package includes just such a ‘read coverage’ estimator. On this data set, the read coverage spectrum can be generated like so::

~/dev/khmer/sandbox/calc-median-distribution.py reads.kh reads.fa reads-cov.dist
./plot-coverage-dist.py reads-cov.dist reads-cov.png --xmax=600 --ymax=500

and looks like this:


You see the same peaks at roughly the same places. While superficially similar to the k-mer spectrum, this is actually more useful in its own right — because now you can grab the reads and do things with them.

We provide a script in khmer to extract the reads; slice-reads-by-coverage will take either a min coverage, or a max coverage, or both, and extract the reads that fall in the given interval.

First, let’s grab the reads between 50 and 200 coverage — these are the single-copy genome components. We’ll put them in reads-genome.fa.

~/dev/khmer/sandbox/slice-reads-by-coverage.py reads.kh reads.fa reads-genome.fa -m 50 -M 200

Next, grab the reads greater in abundance than 200; these are the repeats. We’ll put them in reads-repeats.fa.

~/dev/khmer/sandbox/slice-reads-by-coverage.py reads.kh reads.fa reads-repeats.fa -m 200

Now let’s replot the read coverage spectra, first for the genome:

load-into-counting.py -x 1e8 -k 20 reads-genome.kh reads-genome.fa
~/dev/khmer/sandbox/calc-median-distribution.py reads-genome.kh reads-genome.fa reads-genome.dist
./plot-coverage-dist.py reads-genome.dist reads-genome.png --xmax=600 --ymax=500

and then for the repeats:

load-into-counting.py -x 1e8 -k 20 reads-repeats.kh reads-repeats.fa
~/dev/khmer/sandbox/calc-median-distribution.py reads-repeats.kh reads-repeats.fa reads-repeats.dist
./plot-coverage-dist.py reads-repeats.dist reads-repeats.png --xmax=600 --ymax=500

images/slice/reads-genome.png images/slice/reads-repeats.png

and voila! As you can see we have the reads of high coverage in reads-repeats.fa, and the reads of intermediate coverage in reads-genome.fa.

If you look closely, you might note that some reads seem to fall outside the specified slice categories above — that’s presumably because their coverage was predicated on the coverage of other reads in the whole data set, and now that we’ve sliced out various reads their coverage has dropped.

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C. Titus Brown
C. Titus Brown is an assistant professor in the Department of Computer Science and Engineering and the Department of Microbiology and Molecular Genetics. He earned his PhD ('06) in developmental molecular biology from the California Institute of Technology. Brown is director of the laboratory for Genomics, Evolution, and Development (GED) at Michigan State University. He is a member of the Python Software Foundation and an active contributor to the open source software community. His research interests include computational biology, bioinformatics, open source software development, and software engineering.