sourmash provides several different techniques for doing classification and breakdown of signatures.
Searching for similar samples with
sourmash search command is most useful when you are looking for
high similarity matches to other signatures; this is the most basic use
case for MinHash searching. The command takes a query signature and one
or more search signatures, and finds all the matches it can above a particular
search will find matches with high Jaccard
similarity, which will
consider all of the k-mers in the union of the two samples.
Practically, this means that you will only find matches if there is
both high overlap between the samples and relatively few k-mers that
are disjoint between the samples. This is effective for finding genomes
or transcriptomes that are similar but rarely works well for samples
of vastly different sizes.
One useful modification to
search is to calculate containment with
--containment instead of the (default) similarity; this will find
matches where the query is contained within the subject, but the
subject may have many other k-mers in it. For example, if you are using
a plasmid as a query, you would use
--containment to find genomes
that contained that plasmid.
gather (discussed below) uses containment
See the main sourmash tutorial
for information on using
search with and without
Breaking down metagenomic samples with
Neither search option (similarity or containment) is effective when comparing or searching with metagenomes, which typically have a mixture of many different genomes. While you might use containment to see if a query genome is present in one or more metagenomes, a common question to ask is the reverse: what genomes are in my metagenome?
We have implemented two approaches in sourmash to do this.
One approach uses taxonomic information from e.g. GenBank to classify
individual k-mers, and then infers taxonomic distributions of
metagenome contents from the presence of these individual
k-mers. (This is the approach pioneered by
Kraken and used by many other tools.)
sourmash lca can be used to classify individual genome bins with
classify, or summarize metagenome taxonomy with
sourmash lca tutorial
shows how to use the
lca classify and
lca summarize commands, and also
provides guidance on building your own database.
The other approach,
gather, breaks a metagenome down into individual
genomes based on greedy partitioning. Essentially, it takes a query
metagenome and searches the database for the most highly contained
genome; it then subtracts that match from the metagenome, and repeats.
At the end it reports how much of the metagenome remains unknown. The
basic sourmash tutorial
has some sample output from using gather with GenBank. See Appendix A at
the bottom of this page for more technical details.
Some benchmarking on CAMI suggests that
gather is a very accurate
method for doing strain-level resolution of genomes. More on
that as we move forward!
To do taxonomy, or not to do taxonomy?¶
By default, there is no structured taxonomic information available in sourmash signatures or SBT databases of signatures. Generally what this means is that you will have to provide your own mapping from a match to some taxonomic hierarchy. This is generally the case when you are working with lots of genomes that have no taxonomic information.
lca subcommands, however, work with LCA databases, which contain
taxonomic information by construction. This is one of the main
differences between the
sourmash lca subcommands and the basic
sourmash search functionality. So the
lca subcommands will generally
output structured taxonomic information, and these are what you should look
to if you are interested in doing classification.
It’s important to note that taxonomy based on k-mers is very, very specific and if you get a match, it’s pretty reliable. On the converse, however, k-mer identification is very brittle with respect to evolutionary divergence, so if you don’t get a match it may only mean that the specific species or genus you’re searching for isn’t in the database.
By default, sourmash tracks k-mer presence, not their abundance. The
proportions and fractions reported also ignore abundance. So, if
sourmash gather reports that a genome is 5% of a metagenome, it is
reporting Jaccard containment of that genome in the metagenome, and it
is ignoring information like the number of reads in the metagenome
that come from that genome. Similarly, when
compares genome or metagenome signatures, it’s reporting Jaccard
similarity without abundance.
However, it is possible to take into account abundance information by
computing signatures with
-p abund. The abundance
information will be used if it’s present in the signature, and it can
be ignored with
--ignore-abundance in any signature comparison.
There are two ways that abundance weighting can be used. One is in
containment queries for metagenomes, e.g. with
sourmash gather, and the other is in comparisons of abundance-weighted signatures,
sourmash search and
sourmash compare. Below, we refer to the
first as “abundance projection” and the second as “angular similarity”.
Projecting abundances in
sourmash gather can report approximate abundance information for
containment queries against genome databases. This will give you
numbers that (approximately) match what you get from counting mapped
If you create your input signatures with
sourmash gather will use that information
to calculate an abundance-weighted result. This will weight
each match to a hash value by the multiplicity of the hash value in
the query signature. You can turn off this behavior with
For example, if you have a metagenome composed of two equal sized genomes
A and B, with A present at 10 times the abundance of B,
abundance-weighted signatures will report that approximately 91% of the
metagenome is A and approximately 9% is B. (If you use
gather will report approximately 50:50, since the genomes are equal
You can also get count-like information from the CSV output of
sourmash gather; the column
median_abund contains the median abundance of the k-mers
in the match to the given genome.
Please see Appendix B, below, for some actual numbers and output.
Buyer beware: There are substantial challenges in doing this kind of analysis on real metagenomic samples, relating to genome representation and strain overlap; see this issue for a discussion.
Computing signature similarity with angular similarity.¶
If signatures that have abundance information are compared with
sourmash search or
sourmash compare, the default comparison is
angular similarity. This
is a distance metric based on cosine similarity, and it is suitable
for use in clustering.
For more information on the value of this kind of comparison for metagenomics, please see the simka paper, Multiple comparative metagenomics using multiset k-mer counting, Benoit et al., 2016.
Implementation note: Angular similarity searches cannot be done on
SBT or LCA databases currently; you have to provide lists of signature
sourmash search and
sourmash compare. sourmash will
provide a warning if you run
sourmash search on an LCA or SBT with
an abundance-weighted query, and automatically apply
Estimating ANI from FracMinHash comparisons.¶
As of v4.4,
sourmash can estimate Average Nucleotide Identity (ANI)
between two FracMinHash (“scaled”) sketches.
sourmash compare can now
produce a matrix of ANI values estimated from Jaccard, Containment,
or Max Containment by specifiing
--ani (optionally along with search type,
sourmash prefetch, and
sourmash gather will now output ANI estimates to output csvs.
Note that while ANI can be estimated from either the Jaccard Index or
the Containment Index, ANI from Containment is preferable (more accurate).
sourmash prefetch, and
sourmash gather, you can
optionally return confidence intervals around containment-derived ANI estimates,
which take into account the impact of the scaling factor (via
For details on ANI estimation, please see our preprint “Debiasing FracMinHash and deriving confidence intervals for mutation rates across a wide range of evolutionary distances,” here, Hera et al., 2022.
What commands should I use?¶
It’s not always easy to figure that out, we know! We’re thinking about better tutorials and documentation constantly.
We suggest the following approach:
build some signatures and do some searches, to get some basic familiarity with sourmash;
explore the available databases;
then ask questions via the issue tracker and we will do our best to help you out!
This helps us figure out what people are actually interested in doing, and any help we provide via the issue tracker will eventually be added into the documentation.
Appendix A: how
sourmash gather works.¶
The sourmash gather algorithm works as follows:
find the best match in the database, based on containment;
subtract that match from the query;
when the number of shared hashes between the remaining query and the best match drops below
threshold_bp/scaled(or is zero), break out of the loop.
The output below is the CSV output for a fictional metagenome.
The first column,
f_unique_to_query, is the fraction of the database
match that is unique with respect to the original query. It will
always decrease as you get more matches.
The second column,
f_match_orig, is how much of the match is in the
original query. For this fictional metagenome, each match is
entirely contained in the original query. This is the number you would
get by running
sourmash search --containment <match> <metagenome>.
The third column,
f_match, is how much of the match is in the remaining
query metagenome, after all of the previous matches have been removed.
The fourth column,
f_orig_query, is how much of the original query
belongs to the match. This is the number you’d get by running
sourmash search --containment <metagenome> <match>.
f_unique_to_query f_match_orig f_match f_orig_query 0.3321964529331514 1.0 1.0 0.3321964529331514 0.13096862210095497 1.0 1.0 0.13096862210095497 0.11527967257844475 1.0 0.898936170212766 0.12824010914051842 0.10709413369713507 1.0 1.0 0.10709413369713507 0.10368349249658936 1.0 0.3134020618556701 0.33083219645293316
A few quick notes for the algorithmic folk out there –
the key innovation for gather is that it looks for groups of k-mers in the databases, and picks the best group (based on containment). It does not treat k-mers individually.
because of this, gather does not saturate as databases grow in size, and in fact should only become more sensitive and specific as we increase database size. (Although of course it may get a lot slower…)
Appendix B: sourmash gather and signatures with abundance information¶
Below is a discussion of a synthetic set of test cases using three randomly generated (fake) genomes of the same size, with two even read data set abundances of 2x each, and a third read data set with 20x.
Data set prep¶
First, we make some synthetic data sets:
r1.fa with 2x coverage of genome s10
r2.fa with 20x coverage of genome s10.
r3.fa with 2x coverage of genome s11.
then we make signature s10-s11 with r1 and r3, i.e. 1:1 abundance, and make signature s10x10-s11 with r2 and r3, i.e. 10:1 abundance.
A first experiment: 1:1 abundance.¶
When we project r1+r3, 1:1 abundance, onto s10, s11, and s12 genomes with gather:
sourmash gather r1+r3 genome-s10.sig genome-s11.sig genome-s12.sig
overlap p_query p_match avg_abund --------- ------- ------- --------- 394.0 kbp 49.6% 78.5% 1.8 genome-s10.fa.gz 376.0 kbp 50.4% 80.0% 1.9 genome-s11.fa.gz
approximately 50% of each query matching (first column,
approximately 80% of subject genome’s contents being matched (this is due to the low coverage of 2 used to build queries;
approximately 2.0 abundance (third column,
no match to genome s12.
A second experiment: 10:1 abundance.¶
When we project r2+r3, 10:1 abundance, onto s10, s11, and s12 genomes with gather:
sourmash gather r2+r3 genome-s10.sig genome-s11.sig genome-s12.sig
overlap p_query p_match avg_abund --------- ------- ------- --------- 0.5 Mbp 91.0% 100.0% 14.5 tests/test-data/genome-s10.fa.gz 376.0 kbp 9.0% 80.0% 1.9 tests/test-data/genome-s11.fa.gz
approximately 91% of s10 matching
approximately 9% of s11 matching
approximately 100% of the high coverage genome being matched, with only 80% of the low coverage genome
approximately 2.0 abundance (third column, avg_abund) for s11, and (very) approximately 20x abundance for genome s10.
The cause of the poor approximation for genome s10 is unclear; it could be due to low coverage, or small genome size, or something else. More investigation needed.