Bioinformatics – Academy of Technology

Bio Informatics

Bioinformatics is an interdisciplinary field that combines biology, computer science, mathematics, and statistics to analyze and interpret complex biological data. In today’s era of high-throughput technologies such as next-generation sequencing, genomics, and proteomics, bioinformatics plays a pivotal role in extracting meaningful patterns from massive datasets to advance scientific understanding and improve healthcare outcomes.

At the Academy of Technology (AOT), research in bioinformatics focuses on developing computational tools and algorithms for the storage, retrieval, and analysis of biological information. Faculty members and students work on projects involving genome assembly, sequence alignment, protein structure prediction, and molecular modeling. Advanced machine learning techniques are applied to identify biomarkers, predict disease susceptibility, and optimize drug design, enabling precision medicine approaches that are tailored to individual genetic profiles.

Applications of AOT’s bioinformatics research span diverse domains. In healthcare, computational analyses of genetic data aid in early disease detection, personalized treatment strategies, and vaccine development. In agriculture, bioinformatics supports the development of high-yield, disease-resistant crops through genomic studies. In environmental science, microbial community analysis helps in monitoring ecosystem health and addressing bioremediation challenges. The research is supported by robust computational infrastructure and interdisciplinary collaboration with biologists, medical professionals, and industry experts. Students gain practical exposure through hands-on projects, internships, and the use of cutting-edge software tools for biological data analysis.

Future research directions at AOT aim to integrate bioinformatics with artificial intelligence, big data analytics, and cloud computing to manage the growing scale and complexity of biological data. Efforts will also focus on developing open-access tools and platforms to make bioinformatics resources more accessible for researchers and clinicians worldwide. Through its work in bioinformatics, AOT is contributing to breakthroughs that have the potential to transform medicine, agriculture, and environmental sustainability.

Researchers:

Mr. Subhankar Roy (CSE Department)

Problem Description: Next Generation Sequencing (NGS) technology generates massive amounts of genome sequence that increases rapidly over time. As a result, there is a growing need for efficient compression algorithms to facilitate the processing, storage, transmission, and analysis of large-scale genome sequences. Over the past 32 years, numerous state-of-the-art compression algorithms have been developed. The performance of any compression algorithm is measured by three main compression metrics: compression ratio, time, and memory usage. Although specialized compression algorithms have been created in an effort to replace Zstd or Gzip throughout the years, there is still opportunity for development in this area in terms of higher compression ratios, quicker processing times, and more effective CPU and memory usage

Focus of Research:

Developing lossless compression methods for NGS.
Making efficient storing, transferring, and medical analyzing.
Developing reference-free and reference-based compression.
General-purpose compressors’ performance on the normalized genome sequence (NGC)

Publications:

Roy, S., Kumar Maity, D., & Mukhopadhyay, A., A lossless reference-free sequence compression algorithm leveraging grammatical, statistical, and substitution rules, Briefings in functional genomics, 24, elae050, 2025. https://doi.org/10.1093/bfgp/elae050
Roy, S., & Mukhopadhyay, A., A randomized optimal k-mer indexing approach for efficient parallel genome sequence compression, Gene, 907, 148235, 2024. https://doi.org/10.1016/j.gene.2024.148235
S. Roy, P. Ghosh and A. Mukhopadhyay, TARG: A Reference-Free, Lossless, Customized General-Purpose Encoder for Genome Sequence in Raw, FASTA, or Multi-FASTA Formats, 2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT), Hooghly, India, 2024, pp. 1-6. https://doi.org/10.1109/C3IT60531.2024.10829491
Roy, S., Mukhopadhyay, A., A Comparative Study on the Evaluation of k-mer Indexing in Genome Sequence Compression, Computational Intelligence in Communications and Business Analytics, CICBA 2023, Communications in Computer and Information Science, vol 1955, Springer, Cham., 2023. https://doi.org/10.1007/978-3-031-48876-4_3
S. Roy, J. Mukherjee, P. Ghosh, M. Patra, A. Sadhukhan, A. Charit and A. Mukhopadhyay, A Study of Genome Compression Algorithms for Industrial versus Scientific Applications Focusing Sequences in Raw and FASTA/Q Formats. [Accepted in ICDMAI 2025]