sshash 0.2.0

Python bindings for the sshash compressed k-mer dictionary

sshash

Python bindings for sshash-rs — a compressed dictionary for DNA k-mers based on Sparse and Skew Hashing.

sshash stores a set of k-mers (strings of length k over {A, C, G, T}) compactly using minimal perfect hashing and succinct data structures (Elias-Fano, BitFieldVec), and supports fast individual and streaming lookups. It is the k-mer index underlying the piscem read mapper.

Installation

pip install sshash

Building an index

From a FASTA/FASTQ file

import sshash

config = sshash.BuildConfig(k=31, m=19)
config.canonical = True   # k-mer and its reverse complement map to the same entry
config.threads = 8        # parallel build (0 = all cores)
config.verbose = False

dict = config.build_from_file("sequences.fa.gz")
dict.save("my_index")

From a list of sequences in memory

config = sshash.BuildConfig(k=31, m=19)
sequences = ["ACGTACGTACGTACGTACGTACGTACGTACG",
             "TTGCAACCGTTAGCAACGTACGTACGTACGT"]
dict = config.build(sequences)

From a Cuttlefish .cf_seg file

When sequences come from Cuttlefish, build_from_cf_seg also returns a mapping from sshash string IDs back to the original Cuttlefish node IDs:

config = sshash.BuildConfig(k=31, m=19)
dict, segment_ids = config.build_from_cf_seg("unitigs.cf_seg")
# segment_ids[i] is the Cuttlefish node ID for sshash string_id i

Loading and saving

# Save to disk (writes <prefix>.ssi and <prefix>.ssi.mphf)
dict.save("my_index")

# Load from disk
dict = sshash.Dictionary.load("my_index")

Querying

Single k-mer lookup

# Returns a Hit object, or None if not found
hit = dict.query("ACGTACGTACGTACGTACGTACGTACGTACG")
if hit is not None:
    print(hit.kmer_id)           # global k-mer ID
    print(hit.string_id)         # unitig containing this k-mer
    print(hit.kmer_id_in_string) # position within that unitig
    print(hit.orientation)       # +1 forward, -1 reverse complement

# Just the k-mer ID (faster if location info isn't needed)
kmer_id = dict.lookup("ACGTACGTACGTACGTACGTACGTACGTACG")  # None if absent

# Membership test
present = dict.contains("ACGTACGTACGTACGTACGTACGTACGTACG")

Streaming queries over a sequence

The streaming engine maintains minimizer state across consecutive k-mers, avoiding redundant MPHF lookups for adjacent positions. This is significantly faster than calling query in a loop when processing full reads or contigs.

engine = dict.streaming_query()

# Query all k-mers in a sequence at once (returns list)
hits = engine.query_sequence("ACGTACGTACGTACGTACGTACGTACGTACGTACGT")
for hit in hits:
    if hit is not None:
        print(hit.kmer_id, hit.string_id)

# Lazy iterator (memory-efficient for long sequences)
for hit in engine.iter_sequence(b"ACGTACGT..."):
    if hit is not None:
        print(hit.kmer_id)

# Efficiency statistics
print(engine.num_searches)    # full MPHF lookups performed
print(engine.num_extensions)  # k-mers resolved by sliding-window extension

Index properties

print(dict.k)           # k-mer length
print(dict.m)           # minimizer length
print(dict.canonical)   # canonical mode flag
print(dict.num_strings) # number of unitigs
print(dict.num_bits)    # total index size in bits

BuildConfig options

Property	Default	Description
canonical	False	Map each k-mer and its reverse complement to the same entry
threads	0	Worker threads during build (0 = all available cores)
ram_limit_gib	8	RAM budget (GiB) before switching to external sort
seed	internal	Seed for internal hash functions
verbose	True	Print progress during building
tmp_dir	"sshash_tmp"	Directory for temporary files during external sort

k and m are set at construction time and cannot be changed afterwards.

References

• Giulio Ermanno Pibiri. "Sparse and Skew Hashing of K-Mers." Bioinformatics, 2022.
• Giulio Ermanno Pibiri and Rob Patro. "Optimizing sparse and skew hashing: faster k-mer dictionaries." bioRxiv, 2026.

License

BSD 3-Clause