Microbial genomes are being analyzed for revealing plant microbes’ interaction to understand ow plants cope up with stress with the supports from microbial world. Also genomic characterization of microbes isolated from polluted environments are being carried out specially focused on hydrocarbon eating bacteria to remove oil spil.
This project aims in analyzing publicly available sequencing data to reveal patterns and connection to understand stress tolerance mechanism in rice, a major staple food crop. Due to climate change number of cultivable agricultural lands are reducing, hence stress tolerant plants are necessary to feed the population. Along with computational data gene expression and phenotyping data are integrated to explore the biological relevance of sequence data. From the learned mechanism candidate genes are selected for further functional genomics analysis to develop climate smart plants. The project collaborates with Prof Zeba Islam Seraj’s group at University of Dhaka for information and resource exchanges.
In order to gain an in-depth understanding of the change in Hilsa migration, adaptation and reproduction emanating from climate change an integrated omics (genomics, transcriptomics, proteomics, metabolomics and metagenomics) approach has to be adopted. Use and development of different bioinformatics algorithms, tools, and scripts as well as high-performance computing facilities are required to analyze these omics datasets. Moreover, GIS and Remote sensing technologies are required to identify deep sea habitats and collect samples. Here at AGenCy lab IUB, we made connections with the group, led by Professor Haseena Khan, who were responsible for first sequencing of Hilsa. In this project, we will work in direct collaboration with this group. Professor Haseena Khan and her team will lead the sample collection, quality control and sequence data generation and analysis. We at IUB, in collaboration with Dr. A Baten from AgResearch, NZ and Professor M Shoyaib from IIT, DU will lead the computational analysis of the data extracted. In this project we aim to focus on the first phase of the integrated omics approach
Heterogeneity is inherent in biological networks which consist of different entities as nodes (i.e., genes, diseases, drugs, function) and represent the relationships between these entities as edges. Predicting potential associations between biological entities currently has been an important problem in biomedical research. In general, a deep learning model uses the contextual information and structures of the heterogeneous networks to identify the associations. This project will utilize powerful tools, e.g., GNN & MRF, to develop a more accurate, explainable model for link predictions in heterogeneous networks. Dr. Azad Abul Kalam will collaborate with us on this project.
