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

## TL;DR

FLYNC is a machine learning tool that identifies long noncoding RNAs in fruit flies, improving our understanding of their roles in disease.

## Contribution

FLYNC introduces an explainable boosting machine model for accurate lncRNA prediction in Drosophila.

## Key findings

- FLYNC identifies novel tissue- and cell-specific lncRNAs in Drosophila.
- Predicted lncRNAs were experimentally validated using RT-PCR and RNA PolII binding.
- FLYNC improves current limitations in noncoding RNA identification using machine learning.

## Abstract

Noncoding RNAs have increasingly recognized roles in critical molecular mechanisms of disease. However, the noncoding genome of Drosophila melanogaster, one of the most powerful disease model organisms, has been understudied. Here, we present FLYNC—FLY noncoding RNA discovery and classification—a novel explainable boosting machine model that accurately predicts the probability of a newly identified RNA transcript being a long noncoding RNA (lncRNA). Integrated into an end-to-end bioinformatics pipeline capable of processing single cell or bulk RNA sequencing data, FLYNC outputs potential new noncoding RNA genes. FLYNC leverages large-scale genomic and transcriptomic datasets to identify patterns and features that distinguish noncoding genes from protein-coding genes, thereby facilitating lncRNA prediction. We demonstrate the application of FLYNC to publicly available Drosophila adult head bulk transcriptome and single-cell transcriptomic data from Drosophila neural stem cell lineages and identify several novel tissue- and cell-specific lncRNAs. We have further experimentally validated the existence of a set of FLYNC predicted lncRNAs by RT-PCR and RNA PolII binding. Overall, our findings demonstrate that FLYNC serves as a robust tool for identifying lncRNAs in D. melanogaster, transcending current limitations in ncRNA identification and harnessing the potential of machine learning.