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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Related Experiment Video

Updated: Jun 30, 2025

A Standardized Liquid Biopsy Preanalytical Protocol for Downstream Circulating-Free DNA Applications
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Assessing the complementary information from an increased number of biologically relevant features in liquid

Stavros Giannoukakos1,2,3, Silvia D'Ambrosi4, Danijela Koppers-Lalic5

  • 1Department of Genetics, Faculty of Science, University of Granada, Granada, 18071, Spain.

Heliyon
|March 22, 2024
PubMed
Summary

This study introduces a robust liquid biopsy RNA sequencing workflow for cancer diagnostics. The machine learning-based method enhances prediction accuracy and clinical utility by integrating diverse biofeatures and validating results on independent datasets.

Keywords:
BioinformaticsCancer diagnosticsEnsemble learningLiquid biopsyMachine learningNormalisationRNA-SeqTranscriptomicslbRNA-seq

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Liquid biopsy-derived RNA sequencing (lbRNA-seq) offers non-invasive cancer diagnostics but faces challenges with technical artifacts and standardization.
  • Low reproducibility of liquid biopsy biomarkers is often due to a lack of result validation on independent datasets.

Purpose of the Study:

  • To develop and validate a robust workflow for clinic-oriented cancer diagnostics using lbRNA-seq.
  • To integrate diverse biological features via a Machine Learning-based Ensemble Classification framework.
  • To rigorously benchmark normalization methods and assess workflow efficacy on independent datasets.

Main Methods:

  • Utilized ten datasets from multiple studies across three biological material sources.
  • Implemented and benchmarked intra-sample normalization methods, including Counts Per Million (CPM).
  • Introduced novel biofeature types, such as Fraction of Canonical Transcript, alongside gene expression data.
  • Employed a Machine Learning-based Ensemble Classification framework for data integration and analysis.

Main Results:

  • The Counts Per Million (CPM) normalization method demonstrated robustness and comparable performance to cross-sample methods.
  • Novel biofeatures, like Fraction of Canonical Transcript, provided complementary information, consistently enhancing prediction power.
  • The developed workflow proved robust on independent datasets from different labs and protocols, outperforming standard methods.

Conclusions:

  • The presented workflow enhances prediction accuracy for liquid biopsy-derived RNA sequencing in cancer diagnostics.
  • The integration of diverse biofeatures and rigorous validation on independent datasets contribute to its robustness and potential clinical utility.
  • This methodology addresses key limitations in standardizing lbRNA-seq for seamless clinical integration.