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.
See the main sourmash
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 algorithms in sourmash to do this.
One algorithm 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 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
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
has some sample output from using gather with GenBank. See the appendix 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.
lca gather applies the
gather algorithm to search an
LCA database; it reports taxonomy.
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 particular species isn’t known.
If you compute your input signatures with
sourmash gather and
sourmash lca gather will use that information
to calculate an abundance-weighted result. Briefly, 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
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.
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;
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…)