https://github.com/mills-lab/spectre

Spectral coherence classification of actively translated regions in ribosome profiling sequence data.
https://github.com/mills-lab/spectre

Last synced: 2 months ago
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Spectral coherence classification of actively translated regions in ribosome profiling sequence data.

• Host: GitHub
• URL: https://github.com/mills-lab/spectre
• Owner: mills-lab
• License: bsd-3-clause
• Created: 2015-10-12T18:34:14.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2019-02-07T13:42:06.000Z (over 5 years ago)
• Last Synced: 2024-06-30T15:44:44.540Z (3 months ago)
• Language: Python
• Size: 32.3 MB
• Stars: 6
• Watchers: 7
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

• awesome-riboseq - Code - 016-1355-4) | Python; Spectral analysis of sliding windows | (ORF Calling)

README

        
# SPECtre

## To Do

Major revision under active developemnt, please refer to this section before submitting a bug report for ongoing amendments and pending changes. Last stable release can be found under the 'Releases' tab in the main repository.

* Individual modules should be working, but unified pipeline is not

* Add support for multi-threading

* Work on visualization module

* Work on metagene module

* Add test annotation files and test data (both UCSC and Ensembl)

* Add docstrings for all modules and functions

* Develop module tests

* Additional documentation

## Description

This software is designed to identify regions of active translation from ribosome profiling sequence data. The analytical pipeline scores the translational status of each annotated region (5'UTR, CDS, exon, 3'UTR) as a function of its spectral coherence to an idealized trinucleotide signal.

## Requirements

* Python (v.3.6.0+): https://www.python.org or https://anaconda.com (recommended)

* HTSeq:	https://pypi.org/project/HTSeq (or conda install -c bioconda htseq)

* NumPy:	https://pypi.org/project/numpy (or conda install -c anaconda numpy)

* SciPy:	https://pypi.org/project/scipy (or conda install -c anaconda scipy)

* Pandas: https://pypi.org/project/pandas (or conda install -c anaconda pandas)

* docopt:	https://pypi.org/project/docopt (or conda install -c anaconda docopt)

## Quick Start

## Required Files

## Output

## Usage

### Parameters

### Required File Arguments