CNNSplice

CNNSplice predicts splice sites in eukaryotic genomic sequences using convolutional neural network models trained to distinguish true and false splice junctions.

Key Features:

• Convolutional Neural Network Models: Implements multiple CNN-based models to detect splice sites at 5′ and 3′ exon–intron boundaries in RNA genes.
• Balanced and Imbalanced Dataset Performance: Accurately predicts true and false splice sites in both balanced and imbalanced genomic datasets.
• Cross-Organism Prediction Capability: Demonstrates predictive performance across datasets derived from five different organisms, supporting cross-species splice site identification.
• Multiple Model Framework: Provides five trained models that capture sequence patterns associated with splice junctions.

Scientific Applications:

• Gene Annotation: Supports identification of exon–intron boundaries to improve annotation of eukaryotic genes.
• Functional Genomics: Facilitates analysis of RNA splicing mechanisms involved in gene expression and protein production.
• Genome Analysis: Enables splice site prediction in newly sequenced or poorly annotated genomes.

Methodology:

CNNSplice trains convolutional neural network models using genomic sequence data and evaluates them with five-fold cross-validation to classify true and false splice sites across datasets from multiple organisms.