Search Packages

pydeseq2

A Python implementation of the DESeq2 pipeline for bulk RNA-seq DEA.

geoparse

Python library to access Gene Expression Omnibus Database (GEO)

gget

🧬 gget enables efficient querying of genomic reference databases

deeptools

Tools to process and analyze deep sequencing data.

helical

A framework for state-of-the-art pre-trained bio foundation models on genomics and transcriptomics modalities.

sequana

Sequana: a set of Snakemake NGS pipelines

velocyto

RNA velocity estimation in Python

rnalysis

Analyze your RNA sequencing data without writing a single line of code

cutseq

✂️ Trim sequencing adapters from NGS data automatically

isoquant

Transcript discovery and quantification with long RNA reads (Nanopores and PacBio)

pygtftk

A python package and a set of shell commands to handle GTF files

agfusion

Python package to annotate and visualize gene fusions.

cytetype

Multi-agent LLM driven cell type annotation for single-cell RNA-Seq data

sequana-rnaseq

RNA-seq, QC and differential analysis pipeline

scdataset

scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics

svist4get

A simple visualisation tool for genomic tracks from sequencing experiments.

isograph

Python toolkit for discovering isoform-switch and splicing modules from bulk RNA-seq using gene-aware latent network inference.

scprint

single cell foundation model for Gene network inference and more

wot

A software package for analyzing snapshots of developmental processes

broad-genepy

Genepy is an open source utils package covering a range of useful functions for large scale genomics data analysis in python

rnapysoforms

A Python adaptation of ggtranscript with similar functionality, using Plotly and Polars for fast and interactive RNA isoform visualizations.

ore

Associate outliers with rare variation

splam

✂️ Deep learning-based splice site predictor that improves spliced alignments

cellxgene-gateway

Cellxgene Gateway allows you to use the Cellxgene Server provided by the Chan Zuckerberg Institute (https://github.com/chanzuckerberg/cellxgene) with multiple datasets.