  Genome sequencing technology has improved significantly in few last years and
resulted in abundance genetic data. Artificial intelligence has been employed
to analyze genetic data in response to its sheer size and variability. Gene
prediction on single DNA has been conducted using various deep learning
architectures to discover splice sites and therefore discover intron and exon
region. Recent predictions are carried out with models trained on sequence with
fixed splice site location which eliminates possibility of multiple splice
sites existence in single sequence. This paper proposes sequential labelling to
predict splice sites regardless their position in sequence. Sequential
labelling is carried out on DNA to determine intron and exon region and thus
discover splice sites. Sequential labelling models used are based on pretrained
DNABERT-3 which has been trained on human genome. Both fine-tuning and
feature-based approach are tested. Proposed model is benchmarked against latest
sequential labelling model designed for mutation type and location prediction.
While achieving high F1 scores on validation data, both baseline and proposed
model perform poorly on test data. Error and test results analysis reveal that
model experience overfitting and therefore, model is deemed not suitable for
splice site prediction.
