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Updated: Feb 21, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

LSTrAP: efficiently combining RNA sequencing data into co-expression networks.

Sebastian Proost¹, Agnieszka Krawczyk¹, Marek Mutwil²

¹Max-Planck Institute for Molecular Plant Physiology, Am Muehlenberg 1, 14476, Potsdam, Germany.

BMC Bioinformatics

|October 12, 2017

Summary

This summary is machine-generated.

Predicting gene function is challenging. LSTrAP (Large-Scale Transcriptome Analysis Pipeline) efficiently builds gene co-expression networks from RNA-Seq data, enabling functional annotation of uncharacterized genes.

Keywords:

Co-expression Expression atlas Gene function prediction Large-scale biology Network analysis RNA-Seq analysis Transcriptomics

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Experimental gene function elucidation is laborious.
In silico methods predict gene function using co-expression patterns.
Co-expression network construction requires high-quality data and computational resources.

Purpose of the Study:

To develop an efficient pipeline for constructing gene co-expression networks from RNA-Seq data.
To address the lack of integrated tools for large-scale transcriptome analysis.

Main Methods:

LSTrAP (Large-Scale Transcriptome Analysis Pipeline) integrates essential tools for co-expression network construction.
Supports parallel computing for processing hundreds of RNA-Seq samples.
Includes quality control for detecting spurious or low-quality samples.

Main Results:

Demonstrated feasibility of processing large datasets (e.g., Arabidopsis thaliana with 876 samples, Sorghum bicolor with 215 samples).
LSTrAP effectively groups known photosynthesis genes.
Identified currently uncharacterized genes with potential roles in photosynthesis.

Conclusions:

LSTrAP provides an efficient and consistent workflow for co-expression network construction from RNA-Seq data.
Facilitates the processing of large-scale expression datasets.
The pipeline is implemented in Python and available under an MIT license.