Using sourmash: a practical guide¶
So! You’ve installed sourmash, run a few of the tutorials and commands, and now you actually want to use it. This guide is here to answer some of your questions, and explain why we can’t answer others.
(If you have additional questions, please file an issue!)
What k-mer size(s) should I use?¶
You can build signatures at a variety of k-mer sizes all at once, and (unless you are working with very large metagenomes) the resulting signature files will still be quite small.
We suggest including k=31 and k=51. k=51 gives you the most stringent matches, and has very few false positives. k=31 may be more sensitive at the genus level.
Why 31 and 51, specifically? To a large extent these numbers were picked out of a hat, based on our reading of papers like the Metapalette paper (Koslicki and Falush, 2016. You could go with k=49 or k=53 and probably get very similar results to k=51. The general rule is that longer k-mer sizes are less prone to false positives. But you can pick your own parameters.
One additional wrinkle is that we provide a number of precomputed databases at k=21, k=31, and k=51. It is often convenient to calculate signatures at these sizes so that you can use these databases.
You’ll notice that all of the above numbers are odd. That is to avoid occasional minor complications from palindromes in numerical calculations, where the forward and reverse complements of a k-mer are identical. This cannot happen if k is odd. It is not enforced by sourmash, however, and it probably doesn’t really matter.
(When we have blog posts or publications providing more formal guidance, we’ll link to them here!)
What resolution should my signatures be / how should I compute them?¶
sourmash supports two ways of choosing the resolution or size of
your signatures: using
-n to specify the maximum number of hashes,
--scaled to specify the compression ratio. Which should you use?
We suggest calculating all your signatures using
--scaled 1000. This will give you a compression ratio of 1000-to-1 while
making it possible to detect regions of similarity in the 10kb range.
For comparison with more traditional MinHash approaches like
if you have a 5 Mbp genome and use
--scaled 1000, you will extract
approximately 5000 hashes. So a scaled of 1000 is equivalent to using
-n 5000 with mash on a 5 Mbp genome.
The difference between using
--scaled is in metagenome
analysis: fixing the number of hashes with
-n limits your ability to
detect rare organisms, or alternatively results in very large
signatures (e.g. if you use n larger than 10000).
--scaled will scale
your resolution with the diversity of the metagenome.
You can read more about this in this blog post from the mash folk,
Mash Screen: What’s in my sequencing run? What
we do with sourmash and
--scaled is similar to the ‘modulo hash’
mentioned in that blog post.
(Again, when we have formal guidance on this based on benchmarks, we’ll link to it here.)
What kind of input data does sourmash work on?¶
sourmash has been used most extensively with Illumina read data sets and assembled genomes, transcriptomes, and metagenomes. The high error rate of PacBio and Nanopore sequencing is problematic for k-mer based approaches and we have not yet explored how to tune parameters for this kind of sequencing.
On a more practical note,
sourmash compute should autodetect FASTA,
FASTQ, whether they are uncompressed, gzipped, or bzip2-ed. Nothing
special needs to be done.
How should I prepare my data?¶
Raw Illumina read data sets should be k-mer abundance trimmed to get rid of the bulk of erroneous kmers. We suggest a command like the following, using trim-low-abund from the khmer project –
trim-low-abund.py -C 3 -Z 18 -V -M 2e9 <all of your input read files>
This is safe to use on genomes, metagenomes, and transcriptomes. If you
are working with large genomes or diverse metagenomes, you may need to
-M parameter to use more memory.
For high coverage genomic data, you can do very stringent trimming with an absolute cutoff, e.g.
trim-low-abund.py -C 10 -M 2e9 <all of your input read files>
will eliminate all k-mers that appear fewer than 10 times in your data set. This kind of trimming will dramatically reduce your sensitivity when working with metagenomes and transcriptomes, however, where there are always real low-abundance k-mers present.
Could you just give us the !#%#!$ command line?¶
Sorry, yes! See below.
Computing signatures for read files:¶
trim-low-abund -C 3 -Z 18 -V -M 2e9 input-reads-1.fq input-reads-2.fq ... sourmash compute --scaled 1000 -k 21,31,51 input-reads*.fq.abundtrim \ --merge SOMENAME -o SOMENAME-reads.sig
The first command trims off low-abundance k-mers from high-coverage
reads; the second takes all the trimmed read files, subsamples k-mers
from them at 1000:1, and outputs a single merged signature named
‘SOMENAME’ into the file
Computing signatures for individual genome files:¶
sourmash compute --scaled 1000 -k 21,31,51 *.fna.gz --name-from-first
This command computes signatures for all
*.fna.gz files, and names
each signature based on the first FASTA header in each file (that’s
what the option
--name-from-first does). The signatures will be placed
Computing signatures from a collection of genomes in a single file:¶
sourmash compute --scaled 1000 -k 21,31,51 file.fa --singleton
This computes signatures for all individual FASTA sequences in
names them based on their FASTA headers, and places them all in a single
file.fa.sig. (This behavior is triggered by the option
--singleton, which tells sourmash to treat each individual sequence in
the file as an independent sequence.)