iLEC-DNA: Identifying Long Extra-chromosomal Circular DNA by Fusing Sequence-derived Features of Physicochemical Properties and Nucleotide Distribution Patterns

Abstract

ABSTRACTLong extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding these associations can provide valuable insights about disease mechanisms and potential therapeutic approaches. Conventionally, wet lab-based methods are utilized to identify leccDNA, which are hindered by the need for prior knowledge, and resource-intensive processes, potentially limiting their broader applicability. To empower the process of leccDNA identification across multiple species, the paper in hand presents the very first computational predictor. The proposed iLEC-DNA predictor makes use of SVM classifier along with sequence-derived nucleotide distribution patterns and physicochemical properties-based features. In addition, the study introduces a set of 12 benchmark leccDNA datasets related to three species, namely HM, AT, and YS. It performs large-scale experimentation across 12 benchmark datasets under different experimental settings using the proposed predictor and more than 140 baseline predictors. The proposed predictor outperforms baseline predictors across diverse leccDNA datasets by producing average performance values of 80.699%, 61.45% and 80.7% in terms of ACC, MCC and AUC-ROC across all the datasets. The source code of the proposed and baseline predictors is available athttps://github.com/FAhtisham/Extrachrosmosomal-DNA-Prediction.