Although genes carry information, proteins are the main role player in providing all the functionalities of a living organism. Massive amounts of different proteins involve in every function that occurs in a cell. These amino acid sequences can be hierarchically classified into a set of families and subfamilies depending on their evolutionary relatedness and similarities in their structure or function. Protein characterization to identify protein structure and function is done accurately using laboratory experiments. With the rapidly increasing huge amount of novel protein sequences, these experiments have become difficult to carry out since they are expensive, time-consuming, and laborious. Therefore, many computational classification methods are introduced to classify proteins and predict their functional properties. With the progress of the performance of the computational techniques, deep learning plays a key role in many areas. Novel deep learning models such as DeepFam, ProtCNN have been presented to classify proteins into their families recently. However, these deep learning models have been used to carry out the non-hierarchical classification of proteins. In this research, we propose a deep learning neural network model named DeepHiFam with high accuracy to classify proteins hierarchically into different levels simultaneously. The model achieved an accuracy of 98.38% for protein family classification and more than 80% accuracy for the classification of protein subfamilies and sub-subfamilies. Further, DeepHiFam performed well in the non-hierarchical classification of protein families and achieved an accuracy of 98.62% and 96.14% for the popular Pfam dataset and COG dataset respectively.

DNA N6-methyladenine (6mA) is an epigenetic modification, which is involved in many biological regulation processes like DNA replication, DNA repair, transcription, and gene expression regulation. The widespread presence of this 6mA modification in eukaryotes has been unclear until recently. Studying the genome-wide distribution of 6mA can provide a deeper understanding of the epigenetic modification process and the biological processes it involves. Existing experimental techniques are time-consuming and computational machine learning methods have room for performance improvement. DNA N6-methyladenine prediction in eukaryotic cross-species shows low performance. Hence, there is a need for a more accurate, time-efficient method to predict the distribution of 6mA sites in eukaryotes. Since deep learning architectures have shown higher accuracy, we develop a customized VGG16 architecture-based model named 6mAVGG using convolution neural networks for the prediction of DNA 6mA sites in eukaryotes. We introduce a novel 3-dimensional encoding mechanism extending the one-hot encoding method to support the input of the VGG16 model. Specifically, the 10-fold cross-validation on the benchmark datasets for the proposed model achieves higher accuracies of 98.01%, 97.44%, 99.56% respectively for cross-species, Rice, and M. musculus genomes. The proposed model outperforms existing tools for the prediction of 6mA sites and has enhanced accuracies by 2.88%, 4.2%, 0.9% respectively for cross-species, Rice, and M. musculus genomes compared to the state of the art method SNNRice6mA. The model trained with benchmark data predicts 6mA sites of other species ArabidopsisThaliana, RosaChinensis, Drosophila, and Yeast with prediction accuracy over 70%. Thus, this model can be used for the genome-wide prediction of 6mA sites in eukaryotes.

Constructing Phylogenetic tree from DNA of species is an important problem in Bioinformatics. These trees are used to study the evolutionary relationship between species and to study which species are more closely related or distantly related. Ant colony optimization (ACO) paradigm is one of the best solutions through meta heuristics solutions which can be used to construct phylogenetic trees. Some researches present solutions using ACO, better than mostly used methods like Neighbor-Joining, UPGMA, etc. In this research we propose a modified ACO algorithm which can perform better than conventional ACO approaches in more divergent species by abstracting some features in developed ACO solutions. We construct distance matrix calculating Kolmogorov Complexity as distance between species from complete mitochondrial genome without applying Multiple Sequence Alignment (MSA). Then we obtain optimal sequence of combinations of species applying modified ACO algorithm. Using a new scenario, we construct phylogenetic tree from that sequence of combinations and test with two types of datasets. First, we tuned the model using twenty species of mammalians dataset. As second type ten divergent datasets are obtained from different divergent levels of real benchmark trees which are used to evaluate our method. Our results show that our proposed method performs better than conventional methods for divergent species. As well as it gives results in less processing time than the conventional ACO algorithm.