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Benchmarking Methods for Detecting Present/Absent Genes Using RNA-seq Data

Year: 2025

Type: article

Abstract: Accurately identifying whether genes are expressed or inactive in RNA-seq datasets remains challenging due to biological and technical noise. Traditional threshold-based approaches don't take into account noise differences between experiments and species.. Here, we benchmark advanced statistical and Bayesian methods—including Zigzag, RefBool scGenexpress,, BulkECexplorer, DAFS, and BgeeCall to robustly distinguish true gene expression from noise, highlighting performance and reliabilityy across datasets.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15119148

Authors: Brandulas Cammarata, Alessandro, Bastian, Frederic, Robinson-Rechavi, Marc

Institution: University of Lausanne

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W6893310627

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