Skip to contents

Project Status: Active – The project has reached a stable, usable state and is being activelydeveloped. Lifecycle:stable

Welcome to rustytools

Version 0.0.2

rustytools is a high-performance bioinformatics toolkit that integrates Rust-based computational kernels into R. It provides fast and memory-efficient implementations of common algorithms used in genomics and single-cell analysis, with a focus on speed, safety, and interoperability.

Key Features

  • 🔍 Fuzzy motif detection
    Identify imperfect tandem repeats in DNA sequences using Rust-backed pattern matching.

  • 🧬 FASTA utilities
    Efficient sequence retrieval and scanning from large reference genomes.

  • 🧠 MAGIC diffusion
    Fast and scalable implementation of the MAGIC algorithm for imputing single-cell RNA-seq data
    van Dijk et al., Cell, 2018

  • 🧱 PCHA archetypal analysis
    Project data onto convex combinations of extreme states using Principal Convex Hull Analysis
    Groves et al., Cell Systems, 2022

  • ⚙️ R-friendly with CLI speed
    All core computations are written in Rust, with clean R interfaces for seamless integration.

Installation

Install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("furlan-lab/rustytools")

Example: Motif Finding 

library(rustytools)

seq <- charToRaw("ATGATGCTGATGATG")
res <- find_runs(seq, motif = charToRaw("ATG"), min_repeats = 3, max_mismatches = 1)
print(res)

Vignettes

Reference

See the function reference for full documentation.

Developed by the Furlan Lab at Fred Hutchinson Cancer Center MIT Licensed