Building sequencing, AI and network methods, and the open web servers & databases that make them usable.
I develop reproducible workflows for high-throughput sequencing and multi-omics, sequence-based machine learning predictors of molecular function, and systems-level interaction networks—deployed as open software, databases, and web servers across plant, microbial, animal, viral, and human systems.
From Sequencing Data to Predictive Models & Open Resources
Computational Genomics, NGS & Multi-Omics
Reproducible workflows that turn raw sequencing reads into biological insight across RNA-Seq, small RNA, metagenomics, targeted amplicons, and variant analysis—applied across plant stress biology, microbial and viral genomics, livestock markers, and human transcriptomes.
AI & Machine Learning for Molecular Function
Alignment-free deep learning and protein language models that infer hierarchical enzyme classes, disease resistance genes, and single, dual, and membrane subcellular localization directly from sequence, benchmarked under homology-aware splits with calibrated uncertainty.
Systems Biology & Interaction Networks
Intra- and inter-species protein–protein interaction networks integrated with expression, metabolic pathway, and regulatory layers to uncover candidate hubs, host co-factors, and stress or disease mechanisms across plant, microbial, animal, and human systems.
Web Servers, Databases & Reproducible Software
Public prediction servers, curated genomic and host–pathogen databases, and containerized Nextflow DSL2 and Python packages built to FAIR standards—accessed by more than 24,000 researchers across 140+ countries.
Connecting genomic variation, molecular function, and biological networks.
My research trajectory connects sequence-level modeling with multi-omics and proteome-scale network analysis. To automate variant and transcriptome workflows, I developed SNVguru (adopted by 50+ research groups), pySeqRNA (500+ users), and miPyRNA. In sequence-based machine learning, I developed deepNEC and deepNEC 2.0 for alignment-free hierarchical enzyme prediction, PRGminer for resistance-gene classification, and AtSubP-2.0 and RSLpred2 for proteome-wide subcellular localization.
At the systems level, I built deep learning frameworks and comparative interactome resources—including deepHPI, HuCoPIA, HuPoxNET, TritiKBdb, and GreeningDB—as part of 10 software packages and 19 web resources accessed by 24,000+ users across 140+ countries.
In comparative and pathogen genomics, I pair containerized Nextflow and Python workflows (MetaNextViro, PyVirSeq, CodonAdaptPy) with evolutionary and phylodynamic modeling—including leading whole-genome characterization of 91 emerging avian metapneumovirus genomes across subtypes A and B as first and co-corresponding author alongside studies in crops, microbes, livestock, and human disease.
Education, Academic Trajectory & Honors
Education & Academic Timeline
Ph.D. in Plant Sciences (Bioinformatics and Computational Biology)
M.Sc. in Bioinformatics
Add-on Certificate Diploma in Bioinformatics
B.Sc. in Biology
Honors & Awards
Doctoral Student Researcher of the Year
College of Agriculture and Applied Sciences (CAAS), Utah State University
Winner, Department of Plants, Soils and Climate and College of Agriculture and Applied Sciences (CAAS); Finalist, USU Robins Awards, Utah State University.
Young Scientist Award
National Conference on Technological Challenges (TECHSEAR-2017)
Conferred at ICAR-Indian Institute of Rice Research (IIRR), Hyderabad, India, in recognition of significant contributions to crop bioinformatics and molecular marker discovery.
Media & Institutional Press
Methodological Deep Dives & Computational Insights
Demystifying Intra-Host Single Nucleotide Variants (iSNVs) in Emerging Viral Surveillance
Why consensus genomes miss early transmission dynamics, and how to calibrate technical error thresholds to accurately detect low-frequency viral quasispecies.
Architecting Production Metagenomics Pipelines with Nextflow DSL2 and Singularity
Building scalable, deterministic bioinformatics workflows that survive high-throughput diagnostic loads on institutional HPC clusters.
Protein Language Models vs. Alignment-Free Classifiers in Enzyme Commission (EC) Prediction
Comparing deep contextual sequence embeddings (ESM-2, ProtTrans) against k-mer representations for enzymatic reaction classification below the twilight zone.
Teaching, Coursework & Computational Mentoring
Bioinformatics and Big Data Mining
Combined undergraduate and graduate course covering computational methods for biological big data: high-throughput sequencing analysis, sequence alignment algorithms, machine learning in genomics, and high-performance cluster computing.
Bioinformatics Tools and Their Application in Agriculture
Graduate course covering biological databases, molecular phylogenetics, pairwise and multiple sequence alignment, protein structure/function prediction, and applied genomics (full responsibility for lectures, practicals, assessments, and grading).
Advances in Bioinformatics
Doctoral course on computational frontiers: comparative genomics, transcriptomic differential expression modeling, protein–protein interaction networks, Hidden Markov Models, and structural docking.
Biological Reasoning, Reproducible Workflows & Trainee Ownership
Teaching computational methods as instruments for biological reasoning—connecting experimental questions with reproducible analysis, critical evaluation of model limits, and command-line autonomy across undergraduate and graduate training.