Python package with CLI for the analysis of single cell methylation data.

Introduction to Computational Biology and Bioinformatics Course at Caltech, 2023

Detecting DNA methylation from PacBio CCS reads

Practical Full-Stack Machine Learning

WGBS/NOMe-seq Data Processing & Differential Methylation Analysis

A comprehensive library for computational molecular biology

Codebase for PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs) for generative modeling of single-cell time-series.

Multi-omic single-cell optimal transport tools

scikit-bio: a community-driven Python library for bioinformatics, providing versatile data structures, algorithms and educational resources.

A handy navigation pane for Jupyter Notebook that follows markdown headers

PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

Statsmodels: statistical modeling and econometrics in Python

Bayesian Modeling and Probabilistic Programming in Python

The set of NGS processing pipelines used at Babraham

resources for faculty

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.

Empowering everyone to build reliable and efficient software.

Tools for working with BUS files

A functional standard library for Python.

Single-cell epigenomics analysis tools

ENCODE ATAC-seq pipeline

A module for working with snap files in Python

Benchmarking computational single cell ATAC-seq methods

chromatin Variability Across Regions (of the genome!)

Companion repository to Lause, Berens & Kobak (2021): "Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data", Genome Biology

Work with trained factor models in Python

"Probabilistic Machine Learning" - a book series by Kevin Murphy