sourmash Python API examples

All of sourmash’s functionality is available via its Python API. Below are both basic and advanced examples that use the API to accomplish common tasks.

A first example: two k-mers

Define two sequences:

>>> seq1 = "ATGGCA"
>>> seq2 = "AGAGCA"

Create two MinHashes using 3-mers, and add the sequences:

>>> import sourmash
>>> mh1 = sourmash.MinHash(n=0, ksize=3, scaled=1)
>>> mh1.add_sequence(seq1)

>>> mh2 = sourmash.MinHash(n=0, ksize=3, scaled=1)
>>> mh2.add_sequence(seq2)

One of the 3-mers (out of 7) overlaps, so Jaccard index is 1/7:

>>> round(mh1.jaccard(mh2), 2)
0.14

and of course the MinHashes match themselves:

>>> mh1.jaccard(mh1)
1.0

We can add sequences to the MinHash objects and query at any time –

>>> mh1.add_sequence(seq2)
>>> x = mh1.jaccard(mh2)
>>> round(x, 3)
0.571

Introduction: k-mers, molecule types, and hashing.

DNA k-mers

The basis of sourmash is k-mers. Suppose we have a 35 base DNA sequence, and we break it into four 31-mers:

>>> K = 31
>>> dnaseq = "ATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGC"
>>> for i in range(0, len(dnaseq) - K + 1):
...    kmer = dnaseq[i:i+K]
...    print(i, kmer)
0 ATGCGAGTGTTGAAGTTCGGCGGTACATCAG
1 TGCGAGTGTTGAAGTTCGGCGGTACATCAGT
2 GCGAGTGTTGAAGTTCGGCGGTACATCAGTG
3 CGAGTGTTGAAGTTCGGCGGTACATCAGTGG
4 GAGTGTTGAAGTTCGGCGGTACATCAGTGGC

sourmash uses a hash function (by default MurmurHash) that converts each k-mer into 64-bit numbers. These numbers form the basis of everything else sourmash does; the k-mer strings are not used internally at all.

The easiest way to access the hash function is via the seq_to_hashes method on MinHash objects, which returns a list:

>>> from sourmash import MinHash
>>> mh = MinHash(n=0, ksize=K, scaled=1)
>>> for i in range(0, len(dnaseq) - K + 1):
...    kmer = dnaseq[i:i+K]
...    print(i, kmer, mh.seq_to_hashes(kmer))
0 ATGCGAGTGTTGAAGTTCGGCGGTACATCAG [7488148386897425535]
1 TGCGAGTGTTGAAGTTCGGCGGTACATCAGT [3674733966066518639]
2 GCGAGTGTTGAAGTTCGGCGGTACATCAGTG [2135725670290847794]
3 CGAGTGTTGAAGTTCGGCGGTACATCAGTGG [14521729668397845245]
4 GAGTGTTGAAGTTCGGCGGTACATCAGTGGC [15919051675656106963]

Note that this is the same as using the MurmurHash hash function with a seed of 42 and taking the first 64 bits.

Because DNA is double-stranded and has no inherent directionality, but computers represent DNA with only one strand, it’s important for sourmash to represent both strands. sourmash does this by building a canonical representation for each k-mer so that reverse-complement sequences match to their forward sequence.

Underneath, sourmash DNA hashing does this by taking each k-mer, building the reverse complement, choosing the lexicographically lesser of the two, and then hashes it - for example, for the first and second k-mers above, you get:

>>> from sourmash.minhash import hash_murmur
>>> from screed import rc
>>> kmer_1 = "ATGCGAGTGTTGAAGTTCGGCGGTACATCAG"
>>> hash_murmur(kmer_1)
7488148386897425535
>>> hash_murmur(kmer_1) == mh.seq_to_hashes(kmer_1)[0]
True
>>> kmer_2 = "TGCGAGTGTTGAAGTTCGGCGGTACATCAGT"
>>> hash_murmur(kmer_2)
6388498842523164783
>>> kmer_2_rc = rc(kmer_2)
>>> kmer_2_rc
'ACTGATGTACCGCCGAACTTCAACACTCGCA'
>>> hash_murmur(kmer_2_rc) == mh.seq_to_hashes(kmer_2)[0]
True

where the second k-mer’s reverse complement starts with ‘A’ and is therefore chosen for hashing by sourmash. This method was chosen to be compatible with [mash](https://mash.readthedocs.io/.

Protein-based encodings

By default, MinHash objects work with DNA. However, sourmash supports amino acid, Dayhoff, and hydrophobic-polar (hp) encodings as well. The Dayhoff and hp encodings support degenerate matching that is less stringent than exact matches.

The simplest way to use a protein MinHash object is to create one and call add_protein on it –

>>> K = 7
>>> mh = MinHash(0, K, is_protein=True, scaled=1)
>>> protseq = "MVKVYAPAS"
>>> mh.add_protein(protseq)

This creates three 7-mers from the sequence and hashes them:

>>> list(sorted(mh.hashes))
[5834377656419371297, 8846570680426381265, 10273850291677879123]

As with DNA k-mers, above, you can also use seq_to_hashes to generate the hashes for protein k-mers, if you add the is_protein=True flag:

>>> for i in range(0, len(protseq) - K + 1):
...    kmer = protseq[i:i+K]
...    print(i, kmer, mh.seq_to_hashes(kmer, is_protein=True))
0 MVKVYAP [5834377656419371297]
1 VKVYAPA [10273850291677879123]
2 KVYAPAS [8846570680426381265]

In this case, the k-mers are always hashed in the forward direction (because protein sequence always has an orientation, unlike DNA).

sourmash also supports the Dayhoff and hydrophobic-polar encodings; here, amino acids are first mapped to their encodings and then hashed. So, for example, the amino acid sequence CADHIF* is mapped to abcdef* in the Dayhoff encoding:

>>> mh = MinHash(0, K, dayhoff=True, scaled=1)
>>> h1 = mh.seq_to_hashes('CADHIF*', is_protein=True)[0]
>>> h1
12061624913065022412
>>> h1 == hash_murmur('abcdef*')
True

Translating DNA into protein-based encodings.

If you use add_sequence(...) to add DNA sequence to a protein encoding, or call seq_to_hashes(...) on a protein encoding without is_protein=True, sourmash will translate the sequences in all possible reading frames and hash the translated amino acids. The k-mer size for the MinHash is used as the k-mer size of the amino acids, i.e. 7 aa is 21 DNA bases.

>>> dnaseq = "ATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGC"
>>> len(dnaseq)
35
>>> K = 7
>>> mh = MinHash(n=0, ksize=K, is_protein=True, scaled=1)
>>> mh.add_sequence(dnaseq)
>>> len(mh)
30

Here, 30 hashes are added to the MinHash object because we are starting with a 35 base DNA sequence, and using 21-mers of DNA (7-mer of protein), which give us 15 distinct 21-mers in the three forward frames, and 15 distinct 21-mers in the three reverse-complement frames, for a total of 30.

If a dayhoff or hp MinHash object is used, then add_sequence(...) will first translate each sequence into protein space in all six frames, and then further encode the sequences into Dayhoff or hp encodings.

Invalid characters in DNA and protein sequences

sourmash detects invalid DNA characters (non-ACTG) and raises an exception on add_sequence, unless force=True, in which case k-mers containing invalid characters are ignored.

>>> dnaseq = "nTGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGC"
>>> K = 31
>>> mh = MinHash(n=0, ksize=K, scaled=1)
>>> mh.add_sequence(dnaseq)
Traceback (most recent call last):
    ...
ValueError: invalid DNA character in input k-mer: NTGCGAGTGTTGAAGTTCGGCGGTACATCAG
>>> mh.add_sequence(dnaseq, force=True)
>>> len(mh)
4

For protein sequences, sourmash does not currently do any invalid character detection; k-mers are hashed as they are, and can only be matched by an identical k-mer (with the same invalid character). (Please file an issue if you’d like us to change this!)

>>> K = 7
>>> mh = MinHash(n=0, ksize=K, is_protein=True, scaled=1)
>>> protseq = "XVKVYAPAS"
>>> mh.add_protein(protseq)
>>> len(mh)
3

For the Dayhoff and hp encodings on top of amino acids, invalid amino acids (any letter for which no encoded character exists) are replaced with ‘X’.

>>> K = 7
>>> mh = MinHash(n=0, ksize=K, dayhoff=True, scaled=1)
>>> protseq1 = ".VKVYAPAS"
>>> hashes1 = mh.seq_to_hashes(protseq1, is_protein=True)
>>> protseq2 = "XVKVYAPAS"
>>> hashes2 = mh.seq_to_hashes(protseq2, is_protein=True)
>>> hashes1 == hashes2
True

Extracting both k-mers and hashes for a sequence

As of sourmash 4.2.2, MinHash objects provide a method called kmers_and_hashes that will return the k-mers and their corresponding hashes for an input sequence –

>>> mh = MinHash(n=0, ksize=31, scaled=1)
>>> dnaseq = "ATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGC"
>>> for kmer, hashval in mh.kmers_and_hashes(dnaseq):
...    print(kmer, hashval)
ATGCGAGTGTTGAAGTTCGGCGGTACATCAG 7488148386897425535
TGCGAGTGTTGAAGTTCGGCGGTACATCAGT 3674733966066518639
GCGAGTGTTGAAGTTCGGCGGTACATCAGTG 2135725670290847794
CGAGTGTTGAAGTTCGGCGGTACATCAGTGG 14521729668397845245
GAGTGTTGAAGTTCGGCGGTACATCAGTGGC 15919051675656106963

This works for protein MinHash objects as well, of course, although you have to provide the is_protein flag, since MinHash objects assume input sequence is DNA otherwise –

>>> K = 7
>>> mh = MinHash(n=0, ksize=K, is_protein=True, scaled=1)
>>> protseq = "XVKVYAPAS"
>>> for (kmer, hashval) in mh.kmers_and_hashes(protseq, is_protein=True):
...     print(kmer, hashval)
XVKVYAP 3140823561012061964
VKVYAPA 10273850291677879123
KVYAPAS 8846570680426381265

For translated MinHash, the k-mers and hashes corresponding to all six reading frames are returned.

>>> dnaseq = "ATGCGAGTGTTGAAGTTCGGCGGTA"
>>> K = 7
>>> mh = MinHash(n=0, ksize=K, is_protein=True, scaled=1)
>>> for (kmer, hashval) in mh.kmers_and_hashes(dnaseq):
...    print(kmer, hashval)
ATGCGAGTGTTGAAGTTCGGC 16652503548557650904
CGAGTGTTGAAGTTCGGCGGT 9978056796243419534
TACCGCCGAACTTCAACACTC 2748622134668949083
CGCCGAACTTCAACACTCGCA 4263227699724621735
TGCGAGTGTTGAAGTTCGGCG 14299765336094039482
GAGTGTTGAAGTTCGGCGGTA 18155608748862746902
ACCGCCGAACTTCAACACTCG 14490181201772650983
GCCGAACTTCAACACTCGCAT 17205086974168937105
GCGAGTGTTGAAGTTCGGCGG 13354527969598897281
CCGCCGAACTTCAACACTCGC 16506504121672505595

In all cases, the k-mers are taken from the sequence itself, so the k-mers will match to the input sequence, even when there are multiple k-mers that hash to the same value (e.g. in the case of reverse complements, or DNA k-mers that are translated to the same amino acid sequence).

Note that sourmash also provides a translate_codon function if you need to get the specific amino acids -

>>> from sourmash.minhash import translate_codon
>>> kmer = 'ATGCGAGT'
>>> for start in range(0, len(kmer) - 3 + 1, 3):
...    codon = kmer[start:start+3]
...    print(codon, translate_codon(codon))
ATG M
CGA R

Summary

In sum,

• MinHash.add_sequence(...) converts DNA sequence into DNA or protein k-mers, and then hashes them and stores them.

• MinHash.add_protein(...) converts protein sequence into protein k-mers, and then hashes them and stores them.

• MinHash.seq_to_hashes(...) will give you the hash values without adding them to the MinHash object.

• MinHash.kmers_and_hashes(...) will provide tuples of (kmer, hashval) for an input sequence.

• The dayhoff and hp encodings can be calculated on amino acid k-mers as well, using MinHash objects.

Note that this is the code that is used by the command-line functionality in sourmash sketch, so the results at the command-line will match the results from the Python API.

Set operations on hashes

All of the hashes in a MinHash object are available via the hashes property:

>>> mh1 = sourmash.MinHash(n=0, ksize=3, scaled=1)
>>> seq1 = "ATGGCA"
>>> mh1.add_sequence(seq1)
>>> seq2 = "AGAGCA"
>>> mh1.add_sequence(seq2)
>>> list(mh1.hashes)
[1274996984489324440, 2529443451610975987, 3115010115530738562, 5059920851104263793, 5740495330885152257, 8652222673649005300, 18398176440806921933]

and you can easily do your own set operations with .hashes - e.g. the following calculates the Jaccard similarity (intersection over union) of two

>>> s1 = set(mh1.hashes)
>>> s2 = set(mh2.hashes)
>>> round(len(s1 & s2) / len(s1 | s2), 3)
0.571

However, the MinHash class also supports a number of basic operations - the following operations work directly on the hashes:

>>> combined = mh1 + mh2
>>> combined += mh1
>>> combined.remove_many(mh1.hashes)
>>> combined.add_many(mh2.hashes)

You can create an empty copy of a MinHash object with copy_and_clear:

>>> new_mh = mh1.copy_and_clear()

and you can also access the various parameters of a MinHash object directly as properties –

>>> mh1.ksize
3
>>> mh1.scaled
1
>>> mh1.num
0
>>> mh1.is_dna
True
>>> mh1.is_protein
False
>>> mh1.dayhoff
False
>>> mh1.hp
False
>>> mh1.moltype
'DNA'

see the “Advanced” section, below, for a more complete discussion of MinHash objects.

Creating MinHash sketches programmatically, from genome files

Suppose we want to create MinHash sketches from genomes –

>>> import glob, pprint
>>> genomes = glob.glob('data/GCF*.fna.gz')
>>> genomes = list(sorted(genomes))
>>> pprint.pprint(genomes)
['data/GCF_000005845.2_ASM584v2_genomic.fna.gz',
 'data/GCF_000006945.1_ASM694v1_genomic.fna.gz',
 'data/GCF_000783305.1_ASM78330v1_genomic.fna.gz']

We have to read them in (here using screed), but then they can be fed into add_sequence directly; here we set force=True in add_sequence to skip over k-mers containing characters other than ACTG, rather than raising an exception.

(Note, just for speed reasons, we’re truncating the sequences to 50kb in length.)

>>> import screed
>>> minhashes = []
>>> for g in genomes:
...     mh = sourmash.MinHash(n=500, ksize=31)
...     for record in screed.open(g):
...         mh.add_sequence(record.sequence[:50000], True)
...     minhashes.append(mh)

And now the resulting MinHash objects can be compared against each other:

>>> import sys
>>> for i, e in enumerate(minhashes):
...    _ = sys.stdout.write(genomes[i][:20] + ' ')
...    for j, e2 in enumerate(minhashes):
...       x = e.jaccard(minhashes[j])
...       _ = sys.stdout.write(str(round(x, 3)) + ' ')
...    _= sys.stdout.write('\n')
data/GCF_000005845.2 1.0 0.0 0.0 
data/GCF_000006945.1 0.0 1.0 0.0 
data/GCF_000783305.1 0.0 0.0 1.0 

Note that the comparisons are quite quick; most of the time is spent in building the minhashes.

Plotting dendrograms and matrices

If you’re interested in building comparison matrices and dendrograms, please see the notebook Building plots from sourmash compare output.

Saving and loading signature files

Signature files encapsulate MinHashes in JSON, and provide a way to wrap MinHash objects with some metadata (the name and filename). To save signatures, use save_signatures with a list of signatures and a Python file pointer:

>>> from sourmash import SourmashSignature, save_signatures
>>> from tempfile import mkdtemp
>>> sig1 = SourmashSignature(minhashes[0], name=genomes[0][:20])
>>> tempdir = mkdtemp(suffix = "temp")
>>> with open(tempdir + '/genome1.sig', 'wt') as fp:
...   save_signatures([sig1], fp)

Here, genome1.sig is a JSON file that can now be loaded and compared – first, load it using load_one_signature:

>>> from sourmash import load_one_signature
>>> loaded_sig = load_one_signature(tempdir + '/genome1.sig')

then compare:

>>> loaded_sig.jaccard(sig1)
1.0
>>> sig1.jaccard(loaded_sig)
1.0

There are two primary signature loading functions - load_one_signature, used above, which loads exactly one signature or else raises an exception; and the powerful and more generic load_file_as_signatures, which takes in a filename or directory containing a collection of signatures and returns the individual signatures – for example, you can load all of the signatures under the tempdir created above like so,

>>> loaded_sigs = list(sourmash.load_file_as_signatures(tempdir))

Both load_file_as_signatures and load_one_signature take molecule type and k-mer size selectors, e.g.

>>> loaded_sigs = load_one_signature(tempdir + '/genome1.sig', select_moltype='DNA', ksize=31)

will load precisely one signature containing a DNA MinHash created at k-mer size of 31.

Going from signatures back to MinHash objects and their hashes -

Once you load a signature, you can go back to its MinHash object with .minhash; e.g.

First, load two signatures:

>>> sigfile1 = 'tests/test-data/genome-s10.fa.gz.sig'
>>> sig1 = load_one_signature(sigfile1, ksize=21, select_moltype='DNA')
>>> sigfile2 = 'tests/test-data/genome-s11.fa.gz.sig'
>>> sig2 = load_one_signature(sigfile2, ksize=21, select_moltype='DNA')

Then, get the hashes, and (e.g.) calculate the union:

>>> hashes1 = set(sig1.minhash.hashes.keys())
>>> hashes2 = set(sig2.minhash.hashes.keys())
>>> hash_union = hashes1.union(hashes2)
>>> print(f'{len(hash_union)} hashes in union of {len(hashes1)} and {len(hashes2)}')
1000 hashes in union of 500 and 500

Advanced features of sourmash MinHash objects - scaled and num

sourmash supports two basic kinds of signatures, MinHash and modulo hash signatures. MinHash signatures are equivalent to mash signatures; they are limited in size, and very effective for comparing genomes and other data sets that are of similar size. The key parameter for MinHash signatures is num, which specifies the maximum number of hashes to be collected for a given input data set.

>>> signum = sourmash.MinHash(n=500, ksize=31)

Because of this parameter, below we’ll call them ‘num’ signatures.

Modulo hash (or ‘scaled’) signatures are specific to sourmash and they enable containment operations that are useful for metagenome analyses. The tradeoff is that unlike num MinHashes, they can become arbitrarily large. The key parameter for modulo hash signatures is scaled, which specifies the average sampling rate for hashes for a given input data set. A scaled factor of 1000 means that, on average, 1 in 1000 k-mers will be turned into a hash for later comparisons; this is a sort of compression factor, in that a 5 Mbp genome will yield approximately 5000 hash values with a scaled factor of 1000 (5000 x 1000 = 5,000,000).

>>> sigscaled = sourmash.MinHash(n=0, ksize=31, scaled=1000)

Note also that with a scaled factor of 1, the signature will contain all of the k-mers.

---

You can differentiate between num signatures and scaled signatures by looking at the num and scaled attributes on a MinHash object:

>>> signum.num
500
>>> signum.scaled
0
>>> sigscaled.num
0
>>> sigscaled.scaled
1000

The MinHash class is otherwise identical between the two types of signatures.

You cannot calculate Jaccard similarity or containment for MinHash objects with different num or scaled values (or different ksizes):

>>> signum2 = sourmash.MinHash(n=1000, ksize=31)
>>> signum.jaccard(signum2)
Traceback (most recent call last):
  ...
TypeError: must have same num: 500 != 1000

However, you can make signatures compatible by downsampling; see the next sections.

A brief introduction to MinHash object methods and attributes

MinHash objects have the following methods and attributes:

• ksize, num, and scaled - the basic parameters used to create a MinHash object.

• hashes - retrieve all of the hashes contained in this object.

• add_sequence(seq) - hash sequence and add hash values.

• add(hash) and add_many(hashvals) - add hash values directly.

• similarity(other) - calculate Jaccard similarity with the other MinHash object.

• contained_by(other) - calculate the Jaccard containment of self by other.

• copy_and_clear() - make an empty copy of a MinHash object with the same parameters.

• __len__() - return the number of actual hash values. Note you can also do len(mh), where mh is a MinHash object.

Downsampling signatures

Num and scaled signatures can always be downsampled without referring back to the original data.

Let’s start by loading 50kb of genomic sequence in to memory:

>>> genomes = glob.glob('data/GCF*.fna.gz')
>>> genomes = list(sorted(genomes))
>>> genome = genomes[0]
>>> record = next(iter(screed.open(genome)))
>>> sequence = record.sequence[:50000]

Now, suppose we make a num signature limited to 1000 hashes:

>>> larger = sourmash.MinHash(n=1000, ksize=31)
>>> larger.add_sequence(sequence)
>>> len(larger)
1000

We can downsample this to 500 by extracting the hashes and using add_many to add them to a new MinHash like so:

>>> hashvals = larger.hashes.keys()
>>> smaller = sourmash.MinHash(n=500, ksize=31)
>>> smaller.add_many(hashvals)
>>> len(smaller)
500

Also note that there’s a convenience function that does the same thing, faster!

>>> smaller2 = larger.downsample(num=500)
>>> smaller2 == smaller
True

The same can be done with scaled MinHashes:

>>> large_scaled = sourmash.MinHash(n=0, ksize=31, scaled=100)
>>> large_scaled.add_sequence(sequence)
>>> len(large_scaled)
459
>>> small_scaled = sourmash.MinHash(n=0, ksize=31, scaled=500)
>>> small_scaled.add_many(large_scaled.hashes.keys())
>>> len(small_scaled)
69

And, again, there’s a convenience function that you can use:

>>> small_scaled2 = large_scaled.downsample(scaled=500)
>>> small_scaled == small_scaled2
True

Converting between num and scaled signatures

(Beware, these are confusing techniques for working with hashes that are easy to get wrong! We suggest posting questions in the issue tracker as you go, if you are interested in exploring this area!)

The hashing function used is identical between num and scaled signatures, so the hash values themselves are compatible - it’s the comparison between collections of them that doesn’t work.

But, in some circumstances, num signatures can be extracted from scaled signatures, and vice versa. We haven’t yet implemented a Python API for this in sourmash, but you can hack it together yourself quite easily, and a conversion utility is implemented through the command line in sourmash signature downsample.

To extract a num MinHash object from a scaled MinHash, first create or load your MinHash, and then extract the hash values:

>>> num_mh = sourmash.MinHash(n=1000, ksize=31)
>>> num_mh.add_sequence(sequence)
>>> hashvals = num_mh.hashes.keys()

Now, create the new scaled MinHash object and add the hashes to it:

>>> scaled_mh = sourmash.MinHash(n=0, ksize=31, scaled=10000)
>>> scaled_mh.add_many(hashvals)

and you are done!

The same works in reverse, of course:

>>> scaled_mh = sourmash.MinHash(n=0, ksize=31, scaled=50)
>>> scaled_mh.add_sequence(sequence)
>>> hashvals = scaled_mh.hashes.keys()
>>> num_mh = sourmash.MinHash(n=500, ksize=31)
>>> num_mh.add_many(hashvals)

So… when can you do this extraction reliably?

You can extract num MinHashes from scaled MinHashes whenever the maximum hash value in the num MinHash is greater than or equal to the max_hash attribute of the scaled MinHash.

You can extract scaled MinHashes to num MinHashes whenever there are more hash values in the scaled MinHash than num.

Yoda sayeth: When understand these two sentences you can, use this code may you.

(You can also take a look at the logic in sourmash signature downsample if you are interested.)

Working with indexed collections of signatures

If you want to search large collections of signatures, sourmash provides two different indexing strategies, together with a generic Index class that supports a common API for searching the collections.

The first indexing strategy is a Sequence Bloom Tree, which is designed to support fast and efficient containment operations on large collections of signatures. SBTs are an on disk search structure, so they are a low-memory way to search collections.

To use SBTs from the command line, we first need to create some scaled signatures:

sourmash sketch dna -p scaled=10000 data/GCF*.fna.gz --outdir data/

and then build a Sequence Bloom Tree (SBT) index with sourmash index, like so:

sourmash index foo.sbt.zip data/GCF*.sig -k 31

Here, sourmash is storing the entire SBT in a single portable Zip file.

Creating an on-disk SBT in Python

Let’s start by using ‘glob’ to grab some example signatures from the test data in the sourmash repository:

>>> import glob
>>> input_filenames = glob.glob('tests/test-data/doctest-data/GCF*.sig')

Now, create an SBT:

>>> import sourmash.sbtmh
>>> tree = sourmash.sbtmh.create_sbt_index()

Load each signature, and add it to the tree:

>>> from sourmash.sbtmh import SigLeaf
>>> for filename in input_filenames:
...     sig = sourmash.load_one_signature(filename, ksize=31)
...     leaf = SigLeaf(sig.md5sum(), sig)
...     tree.add_node(leaf)

(note, you’ll need to make sure that all of the signatures are compatible with each other! The sourmash index command does all of the necessary checks, but the Python API doesn’t.)

Now, save the tree:

>>> filename = tree.save(tempdir + '/test.sbt.zip')

Loading and searching SBTs

How do we load the SBT and search it with a DNA sequence, from within Python?

The SBT filename is test.sbt.zip, as above:

>>> SBT_filename = tempdir + '/test.sbt.zip'

and with it we can load the SBT:

>>> tree = sourmash.load_file_as_index(SBT_filename)

Now, load a DNA sequence:

>>> filename = 'data/GCF_000005845.2_ASM584v2_genomic.fna.gz'
>>> query_seq = next(iter(screed.open(filename))).sequence
>>> print(f'got {len(query_seq)} DNA characters to query')
got 4641652 DNA characters to query

and create a scaled signature:

>>> minhash = sourmash.MinHash(ksize=31, n=0, scaled=10000)
>>> minhash.add_sequence(query_seq)

>>> query_sig = sourmash.SourmashSignature(minhash, name='my favorite query')

Now do a search –

>>> for similarity, found_sig, filename in tree.search(query_sig, threshold=0.1):
...    print(query_sig)
...    print(found_sig)
...    print(similarity)
my favorite query
NC_000913.3 Escherichia coli str. K-12 substr. MG1655, complete genome
1.0

et voila!

In-memory databases: the LCA or “reverse index” database.

The LCA database lets you work with large collections of signatures in memory.

The LCA database was initially designed to support individual hash queries for taxonomic operations - hence its name, which stands for “Lowest Common Ancestor.” However, it supports all of the standard Index operations, just like the SBT.

First, let’s create an LCA database programmatically.

>>> from sourmash.lca import LCA_Database
>>> db = LCA_Database(ksize=31, scaled=10000, moltype='DNA')

Now, let’s load in all of the signatures from the test directory:

>>> for sig in sourmash.load_file_as_signatures('tests/test-data/doctest-data', ksize=31):
...    hashes_inserted = db.insert(sig)
...    print(f"Inserted {hashes_inserted} hashes into db.")
Inserted 493 hashes into db.
Inserted 490 hashes into db.
Inserted 525 hashes into db.

and now you have an Index class that supports all the generic index operations (below). You can save an LCA Database to disk with db.save(filename), and load it with sourmash.load_file_as_index, below.

The Index class API.

The Index class supports a generic API for SBTs, LCAs, and other collections of signatures.

To load an SBT or an LCA database from a file, use sourmash.load_file_as_index:

>>> sbt_db = sourmash.load_file_as_index('tests/test-data/prot/protein.sbt.zip')
>>> lca_db = sourmash.load_file_as_index('tests/test-data/prot/protein.lca.json.gz')

Index objects provide search, insert, load, save, and __len__. The signatures can be accessed directly via the .signatures() method, which returns an iterable. Last but not least, Index.select(ksize=..., moltype=...) will return a view on the Index object that contains only signatures with the desired k-mer size/molecule type.