背景:CircRNAs对于转录后基因表达的调节至关重要,包括miRNA海绵,并在疾病发展中发挥重要作用。最近已经提出了一些计算工具来预测circRNA,由于只使用一个分类器,还有很多可以做的,以提高性能。
结果:提出了StackCirRNAPred,基于堆叠策略的长circRNA与其他lncRNA的计算分类。为了解决一个潜在的问题,即单个特征可能无法很好地区分circRNA和其他lncRNA,我们首先从不同的来源提取特征,包括核酸成分,序列空间特征和物理化学性质,Alu和串联重复。我们创新地应用堆叠策略来集成RF的更有优势的分类器,LightGBM,XGBoost。这允许模型更灵活地结合这些特征。发现StackCirRNAPred明显优于其他工具,精确地,准确度,F1、召回率和MCC分别为0.843、0.833、0.831、0.819和0.666。我们直接在鼠标数据集上测试了它。StackCirRNAPred仍然明显优于其他方法,精确地,准确度,F1、召回率和MCC的0.837、0.839、0.839、0.841、0.677。
结论:我们基于堆叠策略提出了StackCirRNAPred,以区分长circRNAs与其他lncRNAs。测试结果证明了StackCirRNAPred的有效性和鲁棒性,我们希望StackCirRNAPred将补充现有的circRNA预测方法,并有助于下游研究。
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.