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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.

Maik Wolfram-Schauerte¹, Thomas Vogel¹, Hanati Tuoken²

¹Faculty of Science, Department of Computer Science, Eberhard-Karls University Tübingen, Sand 14, D-72076 Tübingen, Baden-Württemberg, Germany.

Briefings in Bioinformatics

|August 3, 2025

Summary

This summary is machine-generated.

This review explores transcriptome convolution and deconvolution methods for analyzing gene activity in complex tissues. A holistic approach is needed to overcome data limitations and improve single-cell and bulk RNA-seq integration.

Keywords:

bulk RNA-Seq cell-type proportions convolution deconvolution holistic transcriptome machine learning scRNA-seq transcriptomics

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An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

Published on: November 2, 2020

Area of Science:

Transcriptomics
Computational Biology
Bioinformatics

Background:

Tissues comprise diverse cell populations with unique gene activities.
Bulk RNA sequencing (RNA-seq) measures gene activity at the tissue level.
Pathological processes alter tissue composition and cell-specific gene expression, challenging bulk RNA-seq analysis.

Purpose of the Study:

To review and benchmark existing convolution and deconvolution methods for transcriptome analysis.
To introduce a "holistic transcriptome model" integrating both convolution and deconvolution.
To identify key challenges and propose a unified framework for advancing the field.

Main Methods:

Overview of existing single-cell and bulk RNA-seq (de)convolution methods.
Benchmarking of (de)convolution approaches.
Analysis of published (de)convolution studies.

Main Results:

Identified limited availability of suitable datasets as a major bottleneck.
Highlighted the use of inaccurate methods for model assessment and training.
Demonstrated the need for joint consideration of convolution and deconvolution.

Conclusions:

A holistic transcriptome model integrating convolution and deconvolution is essential.
A unified framework is proposed to foster collaborative advancements.
Addressing data limitations and improving assessment methods are critical for future progress.