StackCirRNAPred: computational classification of long circRNA from other lncRNA based on stacking strategy

Abstract

Background CircRNAs are essential for the regulation of post-transcriptional gene expression, including as miRNA sponges, and play an important role in disease development. Some computational tools have been proposed recently to predict circRNA, since only one classifier is used, there is still much that can be done to improve the performance. Results StackCirRNAPred was proposed, the computational classification of long circRNA from other lncRNA based on stacking strategy. In order to cope with the potential problem that a single feature might not be able to distinguish circRNA well from other lncRNA, we first extracted features from different sources, including nucleic acid composition, sequence spatial features and physicochemical properties, Alu and tandem repeats. We innovatively apply the stacking strategy to integrate the more advantageous classifiers of RF, LightGBM, XGBoost. This allows the model to incorporate these features more flexibly. StackCirRNAPred was found to be significantly better than other tools, with precision, accuracy, F1, recall and MCC of 0.843, 0.833, 0.831, 0.819 and 0.666 respectively. We tested it directly on the mouse dataset. StackCirRNAPred was still significantly better than other methods, with precision, accuracy, F1, recall and MCC of 0.837, 0.839, 0.839, 0.841, 0.677. Conclusions We proposed StackCirRNAPred based on stacking strategy to distinguish long circRNAs from other lncRNAs. With the test results demonstrating the validity and robustness of StackCirRNAPred, we hope StackCirRNAPred will complement existing circRNA prediction methods and is helpful in down-stream research.

Computational approaches and challenges in the analysis of circRNA data

“…circRNA biogenesis can be attributed to hallmarks within the flanking intronic regions: reverse complimentary matching (RCM) sequences [ 159 ] (also referred to as inverted repeats [ 160 ]), and more specifically, ALU and tandem repeat motifs in humans [ 20 ] facilitating the generation of RNA hairpin structures that bring distal splice sites within close spatial proximity. These hallmarks coupled with evolutionary conservation scores, secondary structure information and the density of single nucleotide polymorphisms (SNP) within conserved miRNA binding sites [ 161 ] have been identified as predictive features for discriminating circRNAs from other classes of long non-coding RNAs (lncRNAs) using statistical and machine learning (ML) based approaches [ 84 , 108 , 128 , 135 ]. Released in 2015, PredcircRNA [ 128 ] represents the earliest attempt at leveraging multiple layers of contextual sequence information to discriminate circRNAs vs. lncRNAs.…”

New insight into circRNAs: characterization, strategies, and biomedical applications

“…Machine learning algorithms establish some mapping rules based on the knowledge and characteristics of the real known circRNAs (Table 1 ). For example, PredcircRNA [ 76 ] and StackCirRNAPred [ 107 ] predict whether an unknown RNA sequence possibly comes from circRNA by some common reliable features, such as ALU repeats, structural motifs and sequence motifs [ 15 , 76 ]. Other machine learning circRNA prediction tools based on the characteristics of nucleotide sequences are PredicircRNATool [ 108 ], DeepCirCode [ 77 ], CirRNAPL [ 109 ], PCirc [ 110 ], circDeep [ 111 ], etc.…”

Computational approaches for circRNAs prediction andin silicocharacterization

“…This tool also incorporates a visualization method to represent circRNA formation features as sequence motifs. CirRNAPL [ 54 ] predicts circRNAs based on structural features and composition of sequences, and the recently released StackCirRNAPred [ 55 ] classifies circRNAs from other lncRNAs using a stacking strategy. These tools can complement established split-bases and pseudo reference-based identification methods available in the literature.…”