ArcInstitute

Genome modeling and design across all domains of life

State is a machine learning model that predicts cellular perturbation response across diverse contexts

Arc Virtual Cell Atlas

LLM agents for working with the SRA and associated bioinformatics databases.

Stack is a single-cell foundation model that enables in-context learning at inference time.

Comprehensive suite for evaluating perturbation prediction models

An efficient CLI to extract sequences from the SRA

A high efficiency binary format for sequencing data

PIE is a perturbation effect prediction model that generalizes to unseen biological conditions

pytorch dataloaders for single-cell perturbation data

A mapper for 10x-flex single cell sequencing reads with fixed abstract geometries

A command line utilty for working with BINSEQ files