---
id: rust
version: "1.3.1"
license: MIT license
license_treatment: permissive
maintenance: abandoned
---

# RUST — Unit step transformation of Ribo-Seq data

License: permissive · Maintenance: abandoned · Downloads: 276.8K/mo

## What it is and what it does

RUST is a normalization method for ribosome profiling (Ribo-seq) data that addresses the characteristic problem of high-density peaks and alignment gaps in ribosome footprint measurements. It implements a unit step transformation designed to reduce the impact of data heterogeneity and noise, making it easier to identify which mRNA sequence features correlate with ribosome footprint densities.

The package depends on pysam for sequence alignment handling, matplotlib for visualization, and numpy for numerical computation. It targets Python 3.8 and later. The tool was developed as part of published research demonstrating that RUST outperforms other normalization techniques and can extract parameters sufficient for predicting experimental densities with high accuracy.

Use it for:

- Normalize Ribo-seq datasets before analyzing how codon usage or secondary structure affects ribosome occupancy
- Perform quality control on ribosome profiling experiments to identify protocol-related artifacts before downstream analysis
- Extract normalized ribosome footprint parameters for predictive modeling of local ribosome densities
- Compare ribosome decoding rates across different mRNA regions after removing heterogeneous noise

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

RUST normalizes ribosome profiling (Ribo-seq) data to reduce noise and heterogeneity, enabling analysis of how mRNA sequence features affect ribosome footprint densities.

No. The project is abandoned (last commit March 2023, over 1200 days ago) with no maintenance signal and zero repository stars. While the MIT license is permissive and installation friction is low, the lack of active maintenance means bugs, security issues, or compatibility problems with current dependency versions will not be fixed. For active Ribo-seq analysis work, seek a maintained alternative or fork.

## Install

pip install rust
uv add rust
poetry add rust

## Installing RUST

Before you install: Installation is straightforward with low friction. However, the project is abandoned as of March 2023 with no recent maintenance, and the last release was over 1200 days ago, meaning any bugs or compatibility issues discovered will not be addressed.

License in practice: MIT license is permissive, allowing free use, modification, and distribution with minimal restrictions, making it suitable for academic and commercial projects.

Quickstart:

pip install RUST

import RUST
# Apply unit step transformation to Ribo-seq data

Requires Python 3.8 or later; pysam may require system-level dependencies

Verify before relying:

- Whether pysam, matplotlib, and numpy versions have known compatibility constraints with Python 3.10
- Whether the package has been tested against current versions of its dependencies
- Specific performance characteristics or scalability limits for large Ribo-seq datasets
- Exact API surface and usage patterns beyond the installation documentation provided

## Package facts

- License: MIT license (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: abandoned
- Downloads: 276.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ribo-seq normalization, ribosome profiling analysis, ribo-seq data transformation, ribosome footprint density, ribo-seq quality control, mRNA sequence feature analysis, ribosome decoding rates, bioinformatics, ribo-seq, abandoned

[View on SkillFed](https://skillfed.io/packages/rust) · [View on PyPI](https://pypi.org/project/rust/)