# PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets

## TL;DR

PLIT is a new computational tool that accurately identifies long non-coding RNAs in plant transcriptomic datasets using feature selection and machine learning, outperforming existing methods.

## Contribution

It introduces a novel feature selection and classification approach specifically tailored for plant RNA-seq data, improving lncRNA identification accuracy.

## Key findings

- PLIT achieved higher accuracy than existing CPC tools.
- 31 optimal features were selected for classification.
- The tool was validated across 7 plant datasets with 10-fold cross-validation.

## Abstract

Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify them in transcriptomic data. Well-known CPC tools such as CPC2, lncScore, CPAT are primarily designed for prediction of lncRNAs based on the GENCODE, NONCODE and CANTATAdb databases. The prediction accuracy of these tools often drops when tested on transcriptomic datasets. This leads to higher false positive results and inaccuracy in the function annotation process. In this study, we present a novel tool, PLIT, for the identification of lncRNAs in plants RNA-seq datasets. PLIT implements a feature selection method based on L1 regularization and iterative Random Forests (iRF) classification for selection of optimal features. Based on sequence and codon-bias features, it classifies the RNA-seq derived FASTA sequences into coding or long non-coding transcripts. Using L1 regularization, 31 optimal features were obtained based on lncRNA and protein-coding transcripts from 8 plant species. The performance of the tool was evaluated on 7 plant RNA-seq datasets using 10-fold cross-validation. The analysis exhibited superior accuracy when evaluated against currently available state-of-the-art CPC tools.