Skip to main content

PyMisha

PyPI CI

Python interface for misha genomic databases. PyMisha provides full read/write access to misha track databases with C++ streaming backends for genome-scale operations.

PyMisha

Features

  • 1D and 2D track support: Dense, sparse, and 2D (rectangle/point) tracks with full CRUD operations.
  • C++ streaming backends: Extraction, summary, quantiles, distribution, lookup, segmentation, Wilcoxon tests, correlation, and sampling all stream through C++ for performance.
  • Virtual tracks: Computed-on-the-fly track views with filtering, shifting, and 30+ aggregation functions.
  • Interval operations: Union, intersection, difference, canonicalization, neighbors, annotation, normalization, random generation, and liftover.
  • Sequence analysis: Extraction, k-mer counting, PWM/PSSM scoring, and Markov-chain synthesis (gsynth).
  • Database management: Create, link, convert, and manage misha-compatible genomic databases.
  • R misha compatibility: Reads and writes the same on-disk formats as R misha (123/145 R exports covered).

Installation

pip install pymisha

Pre-built wheels are available for Linux (x86_64) and macOS (x86_64 and arm64), Python 3.10-3.12.

To install from source (requires a C++17 compiler and numpy):

pip install -e ".[dev]"

Quick start

PyMisha ships with a built-in examples database so you can start exploring immediately -- no external data needed:

import pymisha as pm

# Option 1: one-liner to load the bundled examples database
pm.gdb_init_examples()

# Option 2: equivalent explicit form
pm.gsetroot(pm.gdb_examples_path())

# List available tracks and extract data
print(pm.gtrack_ls())
print(pm.gextract("dense_track", pm.gintervals("chr1", 0, 1000)))

To connect to your own misha database, use gsetroot:

import pymisha as pm

# Initialize the database
pm.gsetroot("/path/to/misha_db")

# Create intervals and extract data
intervals = pm.gintervals_from_strings(["chr1:0-1000", "chr1:2000-2600"])
out = pm.gextract("track1", intervals, iterator=100)

# Filter and summarize
filtered = pm.gscreen("track1 > 0.5", intervals)
stats = pm.gsummary("track1", intervals)

Thread safety

PyMisha inherits R misha's single-threaded design. Keep the following constraints in mind:

  • Not thread-safe. All module-level state (_GROOT, _UROOT, _VTRACKS, CONFIG) is process-global and unsynchronized. Do not call PyMisha from multiple threads concurrently.
  • One database per process. You cannot have two databases open simultaneously; gsetroot() replaces the active database globally.
  • CONFIG is global. Changing settings like max_processes affects every subsequent operation in the process.
  • Multiprocessing uses fork(). The C++ backend parallelizes via fork() with shared memory (mmap) and semaphores. This is transparent to the caller but means PyMisha should not be used inside already-forked worker processes or with fork-unsafe libraries.

Examples

Using the built-in example database:

import pymisha as pm

# Quickest way to get started
pm.gdb_init_examples()

# Or equivalently, using gsetroot with the examples path
pm.gsetroot(pm.gdb_examples_path())

print(pm.gtrack_ls())
print(pm.gextract("dense_track", pm.gintervals("chr1", 0, 1000)))

Creating a genome database

PyMisha ships prebuilt genome databases for common assemblies. Download and set up with a single call:

import pymisha as pm

# Download a prebuilt genome (mm9, mm10, mm39, hg19, hg38)
pm.gdb_create_genome("hg38", path="/data/genomes")   # creates /data/genomes/hg38/
pm.gsetroot("/data/genomes/hg38")

pm.gchrom_sizes()  # verify it worked

To build a database from your own FASTA files (e.g. a custom assembly):

pm.gdb_create("/data/my_genome", "genome.fa.gz", verbose=True)
pm.gsetroot("/data/my_genome")

See the Creating Genome Databases tutorial for UCSC download workflows and advanced options.

Optional dependencies

  • pyBigWig: For BigWig import in gtrack_import.
  • pyreadr + Rscript: For loading R-serialized big interval sets.
  • PyYAML: For richer gdataset_info metadata parsing.

Using pymisha with an LLM agent

LLM coding agents (Claude Code, Copilot, Cursor) writing pymisha analysis code can pre-load these reference docs into context for fewer hallucinated APIs and more idiomatic recipes:

Drop-in prompt (no clone needed). Paste the block below into your agent at the start of a pymisha task. It points the agent at the raw files on GitHub, so it works without a local checkout:

Before writing any pymisha code, fetch and read:

- https://raw.githubusercontent.com/tanaylab/pymisha/main/agent-guides/pymisha-core.md  (mandatory: concepts + everyday recipes)
- https://raw.githubusercontent.com/tanaylab/pymisha/main/agent-guides/pymisha-anti-patterns.md  (silent footguns; cross-referenced from core)
- https://raw.githubusercontent.com/tanaylab/pymisha/main/agent-guides/pymisha-advanced.md  (consult on demand: 2D/Hi-C, PWM, import/export, new genomes)

Follow the conventions in those files. When you hit a recipe with an
"Avoid:" block, treat it as a hard rule.

Pin to a release tag for stability by replacing main with any tag that contains agent-guides/. The skills/importing-tracks/SKILL.md guide listed above is load-on-demand; pull it in only when the task specifically calls for track import.

The guides mirror the equivalent set in R misha — same section numbering, same recipes, translated to the pymisha API.

Missing features

Compared to R misha, the following are not yet implemented:

  • Track Arrays: gtrack.array.* and gvtrack.array.slice.
  • Legacy Conversion: gtrack.convert (for migrating old 2D formats).

License

MIT. See LICENSE for details.

Release files for pymisha 0.11.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pymisha 0.11.0
File Size Uploaded
pymisha-0.11.0.tar.gz 1.7 MB Details

Built distributions (wheels)

Table of built distributions (wheels) for pymisha 0.11.0
File Interpreter ABI Platform
pymisha-0.11.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 Details
pymisha-0.11.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 Details
pymisha-0.11.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 Details

Total release size: 38.2 MB

Release files / pymisha-0.11.0.tar.gz

Download URL pymisha-0.11.0.tar.gz
Size 1.7 MB
Tags Source
SHA-256 checksum
How to use checksums
7c6e466e3ee41ccaa2c3518e14dff72268e827021552ca64f681abcc2b99541f
BLAKE2b-256 checksum
How to use checksums
5f39657fd2e569ac479388388c6fe6e0e59d3eba297b6d257b4410c71acd7e09
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 25, 2026.

Transparency log

Release files / pymisha-0.11.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL pymisha-0.11.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Size 12.2 MB
Tags CPython 3.12 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64
SHA-256 checksum
How to use checksums
64331b545750189931c0cd94e7df30ae9c7a272dad0ba42ccc18d0eef87e0c14
BLAKE2b-256 checksum
How to use checksums
7c68ee4ea1f5a7973842f115a912740bb4a275e29bb48a5b656df18770c5c0af
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 25, 2026.

Transparency log

Release files / pymisha-0.11.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL pymisha-0.11.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Size 12.2 MB
Tags CPython 3.11 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64
SHA-256 checksum
How to use checksums
4d697abf67f55ccfa9329fa012a228d1372e7846ef1c9e821e09bd267a2db759
BLAKE2b-256 checksum
How to use checksums
ff435d986fe3230d18171ada3e92eabde59d5bca01d4f289b432911f73df45f7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 25, 2026.

Transparency log

Release files / pymisha-0.11.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL pymisha-0.11.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Size 12.2 MB
Tags CPython 3.10 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64
SHA-256 checksum
How to use checksums
6516fa4b8827c2828f53e4425eb6980c06d55e76a57919a5488ee3081ab86eca
BLAKE2b-256 checksum
How to use checksums
401bb43d1b07df57d8a9804c5ca44b6e7d33b6e619dc468e0d4479fbe5fb8f84
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 25, 2026.

Transparency log

Release history Release notifications | RSS feed

0.11.1

6 release files

This release

0.11.0 This release

4 release files

0.10.1

6 release files

0.10.0

6 release files

0.9.5

6 release files

0.9.4

6 release files

0.9.3

6 release files

0.9.2

6 release files

0.9.1

6 release files

0.9.0

6 release files

0.8.22

6 release files

0.8.18

6 release files

0.8.17

6 release files

0.8.16

6 release files

0.8.15

6 release files

0.8.14

6 release files

0.8.9

6 release files

0.8.8

6 release files

0.8.7

6 release files

0.8.6

6 release files

0.8.5

6 release files

0.8.4

6 release files

0.8.3

6 release files

0.8.2

6 release files

0.8.1

6 release files

0.8.0

6 release files

0.7.1

6 release files

0.7.0

6 release files

0.6.0

6 release files

0.5.2

6 release files

0.5.1

6 release files

0.5.0

6 release files

0.4.0

6 release files

0.3.0

6 release files

0.2.4

6 release files

0.2.3

6 release files

0.2.2

6 release files

0.2.1

6 release files

0.2.0

6 release files

0.1.86

6 release files

0.1.84

6 release files

0.1.74

6 release files

0.1.73

6 release files

0.1.72

6 release files

0.1.71

6 release files

0.1.70

6 release files

0.1.69

6 release files

0.1.68

6 release files

0.1.67

6 release files

0.1.66

6 release files

0.1.65

6 release files

0.1.64

6 release files

0.1.63

6 release files

0.1.62

6 release files

0.1.61

6 release files

0.1.60

6 release files

0.1.59

4 release files

0.1.56

6 release files

0.1.55

6 release files

0.1.54

6 release files

0.1.53

6 release files

0.1.52

6 release files

0.1.51

6 release files

0.1.50

6 release files

0.1.49

6 release files

0.1.48

6 release files

0.1.47

6 release files

0.1.46

6 release files

0.1.42

6 release files

0.1.41

6 release files

0.1.40

6 release files

0.1.39

6 release files

0.1.33

6 release files

0.1.32

6 release files

0.1.31

6 release files

0.1.30

6 release files

0.1.29

6 release files

0.1.28

6 release files

0.1.27

6 release files

0.1.26

6 release files

0.1.25

6 release files

0.1.24

6 release files

0.1.21

6 release files

0.1.20

6 release files

0.1.19

4 release files

0.1.18

4 release files

0.1.17

4 release files

0.1.16

4 release files

0.1.9

10 release files

0.1.8

10 release files

0.1.7

10 release files

0.1.6

10 release files

0.1.5

10 release files

0.1.4

10 release files

0.1.3

10 release files

0.1.2

10 release files

0.1.1

10 release files

0.1.0

10 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page