pygenogrove 0.6.3

Python bindings for the genogrove C++ library - a specialized B+ tree for genomic intervals

pygenogrove

Python bindings for the genogrove C++ library - a specialized B+ tree data structure optimized for genomic interval storage and querying.

Installation

Building from Source

Requirements:

• C++20 compatible compiler
• CMake 3.15+
• Python 3.8+

# Clone with submodules
git clone --recursive https://github.com/genogrove/pygenogrove.git
cd pygenogrove

# Build using CMake
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build

# The built module will be in build/pygenogrove.so (or .pyd on Windows)

Using pip

pip install pygenogrove

Using conda/mamba

Quick Start

The standard key is a GenomicCoordinate (stranded, 0-based closed [start, end]), and the standard Grove stores any JSON-serializable payload (dict / list / scalar / None) per key:

import pygenogrove as pg

grove = pg.Grove()

# Insert stranded coordinates with arbitrary metadata (or no data at all)
grove.insert("chr1", pg.GenomicCoordinate("+", 100, 200), {"gene": "FOO", "score": 5})
grove.insert("chr1", pg.GenomicCoordinate("-", 100, 200), {"gene": "BAR"})
grove.insert("chr1", pg.GenomicCoordinate(".", 300, 400))   # data defaults to None

# Query is strand-aware: a '+' query matches only '+' (and '*' wildcards)
for key in grove.intersect(pg.GenomicCoordinate("+", 150, 160), "chr1"):
    print(key.value, key.data)        # GenomicCoordinate('+', 100, 200) {'gene': 'FOO', 'score': 5}

# '*' matches any strand; '.' is a concrete unstranded value (matches only '.')
len(grove.intersect(pg.GenomicCoordinate("*", 150, 160), "chr1"))   # 2

grove.serialize("out.gg")             # JSON-text payload; a C++ grove<gc, string> can read it

The payload round-trips transparently (no json import needed), and each key may carry a different shape — no schema is enforced.

Important — do not mutate an inserted coordinate. GenomicCoordinate.start, .end, and .strand are read-only; set_range() / set_strand() must only be used on coordinates you have NOT yet inserted (e.g. a query you want to reuse). Mutating a stored key silently corrupts B+ tree ordering.

Strand semantics

• '+' / '-' — forward / reverse strand
• '.' — a concrete unstranded value (matches only '.')
• '*' — wildcard query strand (matches any strand)

So plain unstranded intervals are just GenomicCoordinate('.', start, end).

Typed BED/GFF groves (for C++ interop)

The schemaless Grove is the everyday tool. When you need a guaranteed BED/GFF structure and full interop with typed C++ .gg files, use the typed groves (BedGrove / GffGrove, also genomic-coordinate keyed):

g = pg.BedGrove()
g.insert("chr1", bed_entry)           # entry-deriving: strand taken from the BED6 column

API Reference

GenomicCoordinate

GenomicCoordinate(strand: str, start: int, end: int)

A stranded genomic coordinate with closed [start, end] (0-based, both inclusive). strand is one of '+', '-', '.', '*'. Overlap requires both coordinate overlap AND strand compatibility ('*' matches any).

Attributes (read-only): strand, start, end

Methods:

• set_range(start, end) / set_strand(strand): pre-insertion only (mutating a stored key corrupts B+ tree ordering).
• GenomicCoordinate.overlaps(a, b): static strand-aware overlap check.

Grove

Grove(order: int = 3)

A B+ tree container for genomic intervals with multi-index support.

Parameters:

• order: Maximum branching factor (max keys per node = order - 1). Minimum 3.

Methods:

• len(grove) / size() / indexed_vertex_count(): Number of indexed intervals across all indices
• get_order(): Get the order (branching factor) of the tree
• insert(index: str, key: GenomicCoordinate, data=None) -> Key: Insert a coordinate (with an optional JSON-serializable payload) at the specified index
• intersect(query: GenomicCoordinate) -> QueryResult: Find strand-aware overlaps across all indices
• intersect(query: GenomicCoordinate, index: str) -> QueryResult: Find strand-aware overlaps in a specific index
• flanking(query: GenomicCoordinate, index: str) -> FlankingResult: Find the nearest non-overlapping keys on either side of the query (predecessor / successor). Also flanking(query, index, is_compatible) filters candidates by a bool(candidate, query) predicate (e.g. same strand)

FlankingResult (returned by flanking):

• predecessor: the closest key entirely before the query (a Key), or None
• successor: the closest key entirely after the query (a Key), or None

Keys overlapping the query are excluded; for nested intervals the predecessor is the one with the largest end (smallest gap). Compute the gap distance from the returned key, e.g. query.start - result.predecessor.value.end - 1 (closed coordinates). BedGrove/GffGrove expose flanking too (their results' keys carry .data).

Graph overlay (directed edges between keys):

• add_edge(source: Key, target: Key): Add a directed edge (raises ValueError if a key is None)
• remove_edge(source: Key, target: Key) -> bool: Remove an edge; True if one was removed
• has_edge(source: Key, target: Key) -> bool: Test whether an edge exists
• get_neighbors(source: Key) -> list[Key]: Keys directly reachable from source
• out_degree(source: Key) -> int: Number of outgoing edges from source
• edge_count() -> int: Total number of edges in the overlay
• vertex_count_with_edges() -> int: Number of keys with at least one outgoing edge
• add_external_key(key: GenomicCoordinate, data=None) -> Key: Add a key outside the index that can still participate in the graph (not returned by intersect)

Labelled edges — on the universal Grove, edges carry a JSON-serializable payload (the typed BedGrove/GffGrove keep unlabelled edges for binary interop, so these methods are absent there):

• add_edge(source: Key, target: Key, data): Add an edge with a metadata payload. The 2-argument add_edge attaches None
• get_edges(source: Key) -> list: The edge payloads of source's outgoing edges, parallel to get_neighbors(source)
• get_edge_list(source: Key) -> list[tuple[Key, object]]: The outgoing edges as (target, metadata) pairs — the zip of get_neighbors and get_edges (metadata is None for payload-less edges)
• get_neighbors_if(source: Key, predicate) -> list[Key]: Target keys whose edge metadata satisfies predicate(metadata) — the predicate receives the decoded payload (edges added without a payload yield None, so guard for it when mixing labelled and unlabelled edges)
• link_with(keys: list[Key], predicate): Label adjacent pairs — predicate(k1, k2) returns the edge payload to attach, or None to skip

Edge removal / bulk linking (on every grove):

• remove_edges_from(source: Key) -> int / remove_edges_to(target: Key) -> int / remove_all_edges(key: Key) -> int: Remove outgoing / incoming / all touching edges; each returns the count removed
• remove_edges_if(predicate) -> int: Remove every edge matching a predicate. On the universal Grove the predicate is predicate(target: Key, metadata) -> bool (sees both target and edge metadata); on void-edge BedGrove/GffGrove it is predicate(target: Key) -> bool. Returns the count removed
• clear_graph(): Remove all edges (keys are left intact); graph_empty() -> bool
• link_if(keys: list[Key], predicate): Add an unlabelled edge between each adjacent pair (keys[i], keys[i+1]) for which predicate(k1, k2) returns True (typically over the keys returned by a bulk insert)

import pygenogrove as pg

g = pg.Grove()
a = g.insert("chr1", pg.GenomicCoordinate("+", 100, 200))
b = g.insert("chr1", pg.GenomicCoordinate("+", 300, 400))
g.add_edge(a, b, {"type": "exon->transcript", "weight": 7})
g.get_edges(a)                                    # [{"type": ..., "weight": 7}]
g.get_neighbors_if(a, lambda m: m["weight"] > 5)  # [b]

Serialization (zlib-compressed .gg binary):

• serialize(path: str): Write the grove (coordinates + payloads + graph overlay) to path
• deserialize(path: str) -> Grove (static): Load a grove written by serialize

Removal / storage:

• remove_key(index: str, key: Key) -> bool: Remove a key (and its graph edges); True if found. None/unknown index → False
• compact(): Reclaim dead slots left by remove_key(). ⚠️ Invalidates every previously-returned indexed Key — re-discover via a fresh query afterward
• vertex_count() / external_vertex_count() / key_storage_size(): counts (indexed + external; external-only; total storage slots incl. dead)

Key

Wrapper object for a coordinate stored in the grove. Returned by insert operations and yielded by query results. Keeps its Grove alive.

Attributes:

• value: the GenomicCoordinate (returned by copy — mutating it cannot corrupt ordering)
• data: the payload. On the universal Grove this is the JSON value you stored (dict / list / scalar / None), returned as a freshly decoded copy each access. On the typed BedKey/GffKey it is a live, mutable reference to the record.

QueryResult

Result object containing matching intervals from a query.

Attributes:

• query: The query interval used for the search
• keys: List of matching keys

Methods:

• __len__(): Number of results
• __iter__(): Iterate over matching keys

Point key types — Numeric & Kmer

Two non-interval key types whose overlap is exact equality (not range intersection), so their groves act as point dictionaries. Each has its own *Grove / *Key / *QueryResult with the same surface as Grove — optional JSON payload, labelled edges, serialize / deserialize.

import pygenogrove as pg

# Numeric — integer point keys (ids, timestamps, …)
g = pg.NumericGrove()
g.insert("ids", pg.Numeric(42), {"label": "answer"})
list(g.intersect(pg.Numeric(42), "ids"))[0].data   # {'label': 'answer'}
len(g.intersect(pg.Numeric(43), "ids"))            # 0 — exact match only

# Kmer — 2-bit-encoded DNA k-mers (k ≤ 32, A/C/G/T case-insensitive)
km = pg.KmerGrove()
km.insert("seqs", pg.Kmer("ACGT"), {"count": 3})
str(pg.Kmer("acgt"))                                # 'ACGT' (normalized)
pg.Kmer.is_valid("ACGN")                            # False

Numeric: value (read-only; set_value pre-insertion only), overlaps(a, b), comparisons, str/repr. Kmer: Kmer(sequence) or Kmer(encoding, k), encoding / k / len(), overlaps(a, b), static is_valid(sequence) and max_k (32). Invalid bases or k > 32 raise ValueError.

BedGrove (typed BED grove)

BedGrove (grove<genomic_coordinate, bed_entry>) is the typed alternative to the schemaless Grove: instead of a JSON payload, each key carries a structured BedEntry. Use it when you want a guaranteed BED schema and full interop with typed C++ .gg files (prebuilt BED groves load/save with their records intact, and the GTF-style helpers are available on GffGrove).

import pygenogrove as pg

g = pg.BedGrove(100)

# insert(index, coord, data) — the GenomicCoordinate is the key, BedEntry is the payload
entry = pg.BedEntry("chr1", 1000, 2000)   # BED-native coords (0-based, half-open)
entry.name = "BRCA1"
entry.score = 900
entry.strand = "+"
key = g.insert("chr1", pg.GenomicCoordinate(".", 1000, 1999), entry)

# the returned key exposes both the interval value and the BED payload
print(key.value.start, key.data.name)     # 1000 BRCA1

for hit in g.intersect(pg.GenomicCoordinate(".", 1500, 1600), "chr1"):
    print(hit.data.name, hit.data.score)

# serialize/deserialize preserves the BedEntry data
g.serialize("genes.gg")
reloaded = pg.BedGrove.deserialize("genes.gg")

BedGrove exposes the same surface as Grove (multi-index insert/intersect, the graph overlay, and serialize/deserialize), with these differences:

• insert(index: str, key: GenomicCoordinate, data: BedEntry) -> BedKey takes the BED payload.
• add_external_key(key: GenomicCoordinate, data: BedEntry) -> BedKey takes the payload too.
• Entry-deriving inserts (no hand-conversion of coordinates):
  • insert(index, entry) -> BedKey — a 2-argument overload: pass a bare BedEntry and the GenomicCoordinate key is derived from its native coordinates (BED's half-open [s, e) → closed [s, e-1]; GFF's 1-based [s, e] → [s-1, e-1]). This is the foolproof way to load records from a reader.
  • insert_bulk(index, entries, presorted=False) -> list[BedKey] — same idea for a whole list of bare entries.
• Fast-path inserts (data-carrying groves only):
  • insert_sorted(index, interval, data) -> BedKey — single insert on the rightmost-append path (skips tree traversal).
  • insert_bulk(index, items, presorted=False) -> list[BedKey] — insert many explicit (GenomicCoordinate, BedEntry) records at once (10–20× faster for large datasets; an empty index is built bottom-up in O(n)). presorted=True assumes the records are already sorted by interval (skips the internal sort).
  • Precondition: sorted/bulk inserts require ascending intervals, and when appending to a non-empty index every new interval must be greater than all existing ones. Violating this corrupts B+ tree ordering — use plain insert if unsure. (GffGrove has all the same methods.)

BedKey is like Key but adds a data attribute:

• value: the interval (returned by copy; do not rely on mutating it)
• data: the associated BedEntry — a live, mutable reference (unlike value, the payload is not part of the B+ tree ordering, so editing it in place is safe)

BedQueryResult is the BedGrove analog of QueryResult (its keys are BedKeys).

BedEntry

A single BED record. Coordinates are BED-native: 0-based, half-open [start, end) (distinct from the closed [start, end] of GenomicCoordinate used as the grove key).

BedEntry(chrom: str, start: int, end: int)

Attributes (read/write):

• chrom (str), start (int), end (int)
• name: Optional[str] (BED4+)
• score: Optional[int] (BED5+)
• strand: Optional[str] — a single character ('+', '-', '.'); assigning an empty or multi-character string raises ValueError, None clears it (BED6+)
• thickness: Optional[ThickInfo] (BED7+)
• item_rgb: Optional[RgbColor] (BED9+)
• blocks: Optional[BlockInfo] (BED12)

ThickInfo(start, end), RgbColor(red, green, blue) (channels 0–255), and BlockInfo(count, sizes, starts) (with list[int] sizes/starts) are the supporting value types. List fields are returned/assigned by copy.

GffGrove (typed GFF/GTF grove)

GffGrove (grove<genomic_coordinate, gff_entry>) is the same typed grove for GFF3/GTF records — identical surface to BedGrove, with a GffEntry payload instead of BedEntry:

import pygenogrove as pg

g = pg.GffGrove(100)

entry = pg.GffEntry("chr1", 1000, 2000, "gene")   # GFF-native coords (1-based, inclusive)
entry.source = "ensembl"
entry.strand = "+"
entry.attributes = {"gene_id": "ENSG1", "gene_name": "BRCA1"}
key = g.insert("chr1", pg.GenomicCoordinate(".", 999, 1999), entry)

print(key.data.type, key.data.get_gene_id())      # gene ENSG1

for hit in g.intersect(pg.GenomicCoordinate(".", 1500, 1600), "chr1"):
    print(hit.data.type, dict(hit.data.attributes))

g.serialize("genes.gg")
reloaded = pg.GffGrove.deserialize("genes.gg")

GffKey mirrors BedKey (value is a copy, data is a live mutable GffEntry reference); GffQueryResult is the GffGrove analog of QueryResult.

GffEntry

A single GFF3/GTF record. Coordinates are GFF-native: 1-based, both endpoints inclusive (distinct from GenomicCoordinate's 0-based closed and BedEntry's 0-based half-open).

GffEntry(seqid: str, start: int, end: int, type: str)

Attributes (read/write):

• seqid (str), source (str), type (str), start (int), end (int)
• score: Optional[float]
• strand: Optional[str] — a single character ('+', '-', '.', '?'); empty or multi-character raises ValueError, None clears it
• phase: Optional[int] (CDS phase 0/1/2)
• attributes: dict[str, str] — the column-9 key/value pairs (returned/assigned by copy)
• format: a GffFormat enum (GFF3 / GTF / UNKNOWN)

Methods: is_gtf(), is_gff3(), get_attribute(key), and the GTF helpers get_gene_id(), get_transcript_id(), get_exon_number(), get_gene_name(), get_gene_biotype() (each returns None when the attribute is absent).

BedReader / GffReader (file iterators)

BedReader and GffReader are single-pass iterators over BED and GFF3/GTF files. Iterate them to get BedEntry / GffEntry records. Plain and gzip/BGZF-compressed (.gz) files are both accepted (auto-detected).

import pygenogrove as pg

# read records one at a time
for entry in pg.BedReader("peaks.bed"):
    print(entry.chrom, entry.start, entry.end, entry.name)

# the common workflow: load a file into a grove. The 2-argument insert derives
# the grove's 0-based closed GenomicCoordinate key from each entry's native coordinates,
# so you don't hand-convert (BED half-open, GFF 1-based) yourself.
g = pg.BedGrove(256)
for e in pg.BedReader("peaks.bed"):
    g.insert(e.chrom, e)

gff = pg.GffGrove(256)
for e in pg.GffReader("genes.gff3"):
    gff.insert(e.seqid, e)

# bulk-load one chromosome at a time (insert_bulk is per-index):
g2 = pg.BedGrove(256)
g2.insert_bulk("chr1", [e for e in pg.BedReader("peaks.bed") if e.chrom == "chr1"])

BedReader(path: str, skip_invalid_lines: bool = False, region: str = "")
GffReader(path: str, skip_invalid_lines: bool = False, validate_gtf: bool = False, region: str = "")

• A missing/unreadable path raises on construction.
• With skip_invalid_lines=False (default) a malformed line raises RuntimeError mid-iteration; with True such lines are skipped. The first data record is validated when the reader is constructed, so a malformed first record raises immediately regardless of this flag.
• GffReader(..., validate_gtf=True) enforces the mandatory GTF2 attributes (gene_id, transcript_id).
• region (e.g. "chr1", "chr1:1000-2000") restricts iteration to records overlapping that range, in tabix/1-based-inclusive coordinates (distinct from BED's 0-based ends). It requires a bgzip-compressed, tabix-indexed file; the default empty string streams the whole file.
• Both expose get_error_message() and get_current_line() for diagnostics.
• The readers are single-pass — they own an htslib file handle and cannot be restarted or iterated twice.

Coordinate systems — GenomicCoordinate is 0-based closed [start, end]; BedEntry is 0-based half-open [start, end); GffEntry is 1-based inclusive [start, end]. Convert deliberately when building grove keys, as shown above.

BamReader (SAM/BAM alignments)

BamReader is a single-pass iterator over SAM/BAM files (htslib auto-detects the format) yielding SamEntry records, with filtering options applied during iteration.

import pygenogrove as pg

for aln in pg.BamReader("reads.bam", min_mapq=30):
    print(aln.qname, aln.chrom, aln.start, aln.end, aln.get_strand())

# load alignments into the universal Grove (sam_entry isn't serializable, so
# there's no typed BamGrove — route through to_coordinate() + to_dict())
g = pg.Grove(256)
for aln in pg.BamReader("reads.bam"):
    if aln.is_mapped():
        g.insert(aln.chrom, aln.to_coordinate(), aln.to_dict())

BamReader(path, skip_unmapped=True, skip_secondary=False,
          skip_supplementary=False, skip_qc_fail=False,
          skip_duplicates=False, min_mapq=0)

• SamEntry fields: qname, chrom, start, end (0-based half-open), mapq, sequence, quality, cigar (string form), flags (an AlignmentFlags). Helpers: get_strand(), is_primary() / is_mapped() / is_reverse() / is_secondary() / is_supplementary() / is_duplicate() / is_paired() / … , consumes_reference(), has_flag(flag), to_coordinate() (strand-aware key) and to_dict() (JSON payload).
• SamFlags exposes the standard FLAG bit constants; AlignmentFlags (the .flags object) has value() plus the same is_*() predicates.
• CIGAR element detail, mate info, and aux tags are not yet exposed.

FastaReader (FASTA/FASTQ sequences)

FastaReader is a single-pass iterator over FASTA/FASTQ files (auto-detected; .gz accepted) yielding FastaEntry records. Sequences are named records, not intervals, so this reader is standalone (no grove integration).

import pygenogrove as pg

for rec in pg.FastaReader("genome.fa"):
    print(rec.name, rec.comment, len(rec), rec.is_fastq())

FastaReader(path, skip_empty_sequences=False)

• FastaEntry fields: name, comment, sequence, quality (Optional[str], FASTQ only); is_fastq(), len(entry).

FastaIndex (random-access FASTA)

FastaIndex provides random-access region fetches over a FASTA file, backed by an .fai index (built on first open — the directory must be writable then).

import pygenogrove as pg

fa = pg.FastaIndex("genome.fa")
fa.fetch("chr1", 1000, 2000)   # bases of the 0-based half-open region [1000, 2000)
fa.fetch("chrM")               # the whole sequence
fa.sequence_length("chr1")     # length in bases
list(fa.names()), "chr1" in fa, len(fa)

# fetch a feature's bases: GenomicCoordinate is closed, fetch is half-open
gc = pg.GenomicCoordinate("+", 4, 7)
fa.fetch("chr1", gc.start, gc.end + 1)

• Methods: fetch(name, start, end) / fetch(name), sequence_count(), sequence_name(i), sequence_length(name), has_sequence(name), plus the Pythonic len() / in / names(). Unknown name / invalid region raise IndexError.

FiletypeDetector (format detection)

FiletypeDetector infers a file's format and compression from its extension (compression extension stripped first) and magic bytes.

import pygenogrove as pg

ftype, comp = pg.FiletypeDetector().detect_filetype("peaks.bed.gz")
# (Filetype.BED, CompressionType.GZIP)

• Filetype: BED / BEDGRAPH / GFF / GTF / VCF / SAM / BAM / FASTA / FASTQ / GG / UNKNOWN.
• CompressionType: NONE / GZIP / BZIP2 / XZ / ZSTD / LZ4 / UNKNOWN.

Registry

A process-wide singleton that interns a string identity into a small, stable integer id (deduplicated), mapping it to any JSON-serializable payload — handy for collapsing repeated gene ids, chromosome names, or sources into a 4-byte id plus a single stored record.

import pygenogrove as pg
r = pg.Registry.instance()

# plain string interning — the string is its own payload
a = r.intern("chr1")     # 0
r.intern("chr1")         # 0  (deduplicated)
r.get(a)                 # "chr1"

# key -> JSON payload (first write wins on re-intern)
g = r.intern("ENSG001", {"name": "BRCA2", "biotype": "protein_coding"})
r.get(g)                 # {"name": "BRCA2", "biotype": "protein_coding"}
r.find("ENSG001")        # g
r.find("missing")        # None
r.serialize("genes.gg")  # also: Registry.deserialize(path), reset(), null_id

Current Status

Currently exposed features:

• Strand-aware coordinates — GenomicCoordinate is the standard key ('+' / '-' / '.' / '*'); overlap and flanking are strand-aware
• Universal Grove (grove<genomic_coordinate, json>) storing arbitrary JSON payloads (dict / list / scalar / None), or no payload at all
• Insert / query, multi-index support (per chromosome)
• Graph overlay (directed edges, external keys), including labelled edges on the universal Grove — add_edge(s, t, data) / get_edges / get_edge_list / get_neighbors_if / link_with — and edge cleanup / bulk linking on every grove (remove_edges_from/to, remove_all_edges, remove_edges_if, clear_graph, graph_empty, link_if)
• Key removal + storage compaction: remove_key(), compact(), vertex_count() / external_vertex_count() / key_storage_size()
• Serialization / deserialization to compressed .gg files (an edgeless JSON Grove .gg is readable by a C++ grove<genomic_coordinate, std::string>; with labelled edges, grove<genomic_coordinate, std::string, std::string>)
• SIF export — to_sif(path) writes the grove's B+ tree structure and graph-overlay edges as a SIF (Simple Interaction Format) text file for visualization (e.g. Cytoscape)
• Nearest-neighbour queries: flanking() (predecessor / successor), incl. a predicate-filtered overload (e.g. same-strand neighbours)
• Point key types — Numeric (integer keys: ids / timestamps) and Kmer (2-bit-encoded DNA k-mers, k ≤ 32, a membership dictionary), each with its own NumericGrove / KmerGrove carrying the same universal surface (optional JSON payload, labelled edges, serialization). Overlap is exact equality
• Typed data groves for C++ interop: BedGrove (grove<genomic_coordinate, bed_entry>) and GffGrove (grove<genomic_coordinate, gff_entry>), with the BedEntry / GffEntry value types
• File readers: BedReader, GffReader, BamReader (SAM/BAM), FastaReader (FASTA/FASTQ), VcfReader (VCF/BCF — variant records with INFO + per-sample genotypes), plus FastaIndex (random-access) and FiletypeDetector (format detection)
• Fast-path inserts on the typed groves: insert_sorted / insert_bulk, plus entry-deriving insert(index, entry) / insert_bulk(index, entries) that derive a stranded key from a BED/GFF record's native coordinates
• Registry — interning singleton mapping a string identity to any JSON payload (plain string interning via single-arg intern)

Not yet exposed (tracked in #1):

• BAM CIGAR-element detail, mate info, and aux tags

Performance Tips

1. Choose appropriate order: Higher order (e.g., 100-500) reduces tree height for large datasets
2. Separate by chromosome: Use the index parameter to maintain separate trees per chromosome
3. Query specific indices: Query specific chromosomes instead of all indices when possible

License

This project inherits the license from the genogrove C++ library and is therefore licensed under the GPLv3 license.

Related Projects

• genogrove: The underlying C++ library

Citation

If you use pygenogrove in your research, please cite the original genogrove library