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Updated: Sep 17, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

ICARus: a pipeline to extract robust gene expression signatures from transcriptome datasets.

Zhaorong Li¹, Juan I Fuxman Bass^1,2,3,4

¹Bioinformatics Program, Boston University, Boston, MA, United States.

Frontiers in Bioinformatics

|July 4, 2025

Summary

The ICARus package uses independent component analysis (ICA) to robustly identify gene signatures from transcriptomics data. This tool enhances biomarker discovery and understanding of disease prognosis by analyzing gene expression patterns.

Keywords:

independent component analysis machine learning robustness signatures transcriptomics

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Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

Published on: May 21, 2020

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene signatures from transcriptomics data are crucial for identifying co-regulated genes, prognostic associations, and biomarkers.
Independent Component Analysis (ICA) is a powerful method for uncovering hidden patterns and coherent gene sets in complex biological data.

Purpose of the Study:

To introduce ICARus, a robust pipeline for performing ICA on transcriptome datasets.
To address limitations in existing ICA packages by identifying a range of near-optimal parameter values and assessing signature robustness and reproducibility.

Main Methods:

Developed and applied the ICARus package for independent component analysis on transcriptome datasets.
Analyzed transcriptomic data from COVID-19 patients and lung adenocarcinoma cohorts.
Assessed the robustness and reproducibility of identified gene signatures across a range of parameter values.

Main Results:

Identified reproducible gene expression signatures significantly associated with prognosis, temporal patterns, and cell type composition in COVID-19 and lung adenocarcinoma.
Gene Set Enrichment Analysis (GSEA) of identified signatures corroborated previous clinical findings.
Discovered potentially novel biological mechanisms underlying disease phenotypes.

Conclusions:

ICARus provides a robust and reproducible method for gene signature extraction from transcriptomics data.
The identified signatures offer valuable insights into disease prognosis, temporal dynamics, and cellular composition.
ICARus facilitates biomarker discovery and the elucidation of new biological mechanisms.