Genomics Deep Learning
MiniSpliceNet — Deep Learning for Splice-Site Classification
A PyTorch-based genome-annotation workflow for classifying DNA sequence windows into donor splice site, acceptor splice site, and non-splice background sequence classes.
Sequence Modelling Pipeline
DNA window to splice class
3
Sequence classes
donor, acceptor, background
201 bp
Window length
centered genomic context
1D CNN
Model architecture
PyTorch sequence classifier
F1
Primary metric
class-aware evaluation
Problem Framing
Splice-signal modelling for genome annotation
Splice-site recognition is central to transcript annotation. MiniSpliceNet implements this as a reproducible supervised learning workflow: construct labelled sequence windows, encode nucleotides, train a neural sequence classifier, and evaluate class-specific performance.
Sequence Windows
Fixed-length DNA windows are organized around donor, acceptor, and background sequence regions for supervised splice-signal learning.
Nucleotide Encoding
A, C, G, and T are converted into one-hot vectors so each sample becomes a model-ready sequence tensor.
CNN Classifier
A 1D convolutional network learns local splice-signal patterns from motif-centred genomic windows.
Benchmark Report
The training run reports class-balanced metrics including precision, recall, F1-score, and a confusion matrix.
Architecture
The core model uses 1D convolutions over nucleotide channels, nonlinear activations, global pooling, and a dense classifier for three-way splice-site prediction.
Evaluation
Stratified train and validation splits keep donor, acceptor, and background classes balanced while reporting precision, recall, F1-score, and confusion-matrix results.
Research Context
The workflow connects splice-signal detection with genome annotation tasks such as FASTA/GTF-derived sequence modelling and transcriptomics-informed isoform analysis.