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Related Experiment Video

Updated: Jun 23, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

A computational framework to improve cross-platform implementation of transcriptomics signatures.

Louis Kreitmann¹, Giselle D'Souza², Luca Miglietta¹

¹Section of Adult Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom; Centre for Antimicrobial Optimisation, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom.

|June 20, 2024

Summary

This summary is machine-generated.

This study addresses challenges in using transcriptomic biomarkers for disease diagnosis. It proposes a computational framework to integrate cross-platform implementation constraints for RNA signatures, accelerating clinical use.

Keywords:

Diagnostics Host-response Molecular test Multiplex PCR Nucleic acid amplification techniques PCR-based technologies RNA sequencing Transcriptomic signatures

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Last Updated: Jun 23, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Published on: March 5, 2022

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

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Published on: May 9, 2017

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Area of Science:

Genomics and Bioinformatics
Molecular Diagnostics
Computational Biology

Background:

Next-generation sequencing and computational advances have improved understanding of transcriptomics.
Transcriptomic biomarkers show promise for disease diagnosis, prognosis, and treatment response prediction.
Integrating transcriptomic signatures into clinical diagnostics faces technical hurdles, particularly cross-platform implementation.

Purpose of the Study:

To discuss challenges in integrating high-throughput transcriptomic signatures into clinical diagnostic tools using nucleic acid amplification (NAA) techniques.
To propose embedding cross-platform implementation constraints into the signature discovery process.
To introduce a computational framework for accelerating the clinical application of RNA signatures.

Main Methods:

Discussing challenges in transcriptomic signature integration for clinical diagnostics.
Proposing the incorporation of cross-platform implementation constraints (e.g., amplification platform limitations, multiplexing capacity, genomic context) into signature discovery.
Outlining a computational framework combining these constraints with statistical and machine learning models.

Main Results:

Identification of key technical obstacles hindering the clinical integration of transcriptomic signatures.
A novel approach to embed cross-platform implementation constraints during signature discovery.
A proposed computational framework to bridge high-throughput discovery and NAA-based clinical application.

Conclusions:

Addressing cross-platform implementation constraints is crucial for clinical translation of transcriptomic signatures.
Integrating these constraints into signature discovery can overcome technical barriers.
The proposed computational framework can accelerate the adoption of RNA signatures in clinical diagnostics via NAA techniques.