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FLYNC: a machine-learning-driven framework for discovering long noncoding RNAs in Drosophila melanogaster.

Ricardo F Dos Santos¹, Tiago Baptista¹, Graça S Marques¹

¹iNOVA4Health, NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade Nova de Lisboa, Lisbon, 1150-082, Portugal.

NAR Genomics and Bioinformatics

|January 19, 2026

Summary

Researchers developed FLYNC, a machine learning tool to identify long noncoding RNAs (lncRNAs) in Drosophila. This advances understanding of the fruit fly genome and disease mechanisms by discovering novel lncRNAs.

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Noncoding RNAs play crucial roles in disease mechanisms.
The noncoding genome of Drosophila melanogaster, a key disease model organism, remains largely uncharacterized.

Purpose of the Study:

To develop and validate FLYNC, a novel machine learning model for accurate long noncoding RNA (lncRNA) discovery and classification in Drosophila.
To identify novel tissue- and cell-specific lncRNAs in Drosophila using FLYNC.

Main Methods:

Development of FLYNC, an explainable boosting machine model for predicting lncRNA probability.
Integration of FLYNC into a bioinformatics pipeline for processing RNA sequencing data (single-cell and bulk).
Application of FLYNC to Drosophila adult head and neural stem cell transcriptomic data.

Main Results:

FLYNC accurately predicts the likelihood of a transcript being an lncRNA.
Identification of several novel tissue- and cell-specific lncRNAs in Drosophila.
Experimental validation of predicted lncRNAs using RT-PCR and RNA PolII binding assays.

Conclusions:

FLYNC is a robust tool for identifying lncRNAs in Drosophila, overcoming current limitations.
This work expands the annotation of the Drosophila noncoding genome and provides a powerful machine learning approach for ncRNA discovery.