  Gene promoters are the key DNA regulatory elements positioned around the
transcription start sites and are responsible for regulating gene transcription
process. Various alignment-based, signal-based and content-based approaches are
reported for the prediction of promoters. However, since all promoter sequences
do not show explicit features, the prediction performance of these techniques
is poor. Therefore, many machine learning and deep learning models have been
proposed for promoter prediction. In this work, we studied methods for vector
encoding and promoter classification using genome sequences of three distinct
higher eukaryotes viz. yeast (Saccharomyces cerevisiae), A. thaliana (plant)
and human (Homo sapiens). We compared one-hot vector encoding method with
frequency-based tokenization (FBT) for data pre-processing on 1-D Convolutional
Neural Network (CNN) model. We found that FBT gives a shorter input dimension
reducing the training time without affecting the sensitivity and specificity of
classification. We employed the deep learning techniques, mainly CNN and
recurrent neural network with Long Short Term Memory (LSTM) and random forest
(RF) classifier for promoter classification at k-mer sizes of 2, 4 and 8. We
found CNN to be superior in classification of promoters from non-promoter
sequences (binary classification) as well as species-specific classification of
promoter sequences (multiclass classification). In summary, the contribution of
this work lies in the use of synthetic shuffled negative dataset and
frequency-based tokenization for pre-processing. This study provides a
comprehensive and generic framework for classification tasks in genomic
applications and can be extended to various classification problems.
