Rajeev Azad, PhD. has research interests in the area of Bioinformatics and Computational Biology, particularly developing and applying mathematical and statistical tools to decipher structural and functional features in genomes and elucidate their relationships in the context of evolution. He has developed novel computational methodologies for gene discovery, alien gene identification and genome comparison. Many of these methods and algorithms are based on probabilistic models that provide a rigorous theoretical framework for studying nucleotide ordering patterns within genomes and have proved powerful tools for biological sequence analysis. He has extensive training and experiences in computational genomics and transcriptomics, including differential gene regulation and genome evolution.

CURRENT RESEARCH INTERESTS

• Microbial genome plasticity: Evolution of bacterial virulence and drug resistance
• Differential gene regulation: Whole transcriptome profiling and analysis
• Assessment of statistical methods for genome sequence analysis
• Gene / Gene family discovery; Gene network analysis
• Sex chromosome evolution

For Prospective Graduate Students

Apply to the Graduate Program in Biochemistry & Molecular Biology

Apply to the Graduate Program in BIology

CURRENT GRANT-FUNDED PROJECTS

• Characterization of the carbon dioxide starvation response in a promising greenhouse gas mitigation biocatalyst. UNT BioDiscovery Institute. PI: Calvin Henard; Co-PI-Rajeev Azad, $30,000
• The Renin-Angiotensin System in Air Pollution-Mediated Exacerbation of Obesity (renewal). National Institute of Health / National Institute of Environmental Health Sciences. PI: Amie Lund; Co-PI: Rajeev Azad, $435,982

MOST SIGNIFICANT PUBLICATIONS

1. Reconstructing horizontal gene flow network to understand prokaryotic evolution. Sengupta S, Azad RK. Open Biol. 2022 Nov;12(11):220169. doi: 10.1098/rsob.220169.
2. Phytochrome B regulates reactive oxygen signaling during abiotic and biotic stress in plants. Fichman Y, Xiong H, Sengupta S, Morrow J, Loog H, Azad RK, Hibberd JM, Liscum E, Mittler R. New Phytol. 2022 Nov 19. doi: 10.1111/nph.18626.
3. Identification of Novel Antimicrobial Resistance Genes Using Machine Learning, Homology Modeling, and Molecular Docking. Sunuwar J, Azad RK. Microorganisms. 2022 Oct 23;10(11):2102. doi: 10.3390/microorganisms10112102.
4. Mapping Strengths and Weaknesses of Different Clustering Approaches to Deciphering Bacterial Chimerism. Burks DJ, Azad RK. OMICS. 2022 Aug;26(8):422-439. doi: 10.1089/omi.2022.0062.
5. CancerNet: a unified deep learning network for pan-cancer diagnostics. Gore S, Azad RK. BMC Bioinformatics. 2022 Jun 13;23(1):229. doi: 10.1186/s12859-022-04783-y.
6. Factors That Influence the Choice of Markov Model Order in Discriminating DNA Sequences from Different Sources. Pandey RS, Azad RK. OMICS. 2022 Jun;26(6):348-355. doi: 10.1089/omi.2022.0043.
7. The Arabidopsis gene co-expression network. Burks DJ, Sengupta S, De R, Mittler R, Azad RK. Plant Direct. 2022 Apr 26;6(4):e396. doi: 10.1002/pld3.396.
8. Analysis of transcribed sequences from young and mature zebrafish thrombocytes. Fallatah W, De R, Burks D, Azad RK, Jagadeeswaran P. PLoS One. 2022 Mar 23;17(3):e0264776. doi: 10.1371/journal.pone.0264776.
9. Inhaled diesel exhaust particles result in microbiome-related systemic inflammation and altered cardiovascular disease biomarkers in C57Bl/6 male mice. Phillippi DT, Daniel S, Pusadkar V, Youngblood VL, Nguyen KN, Azad RK, McFarlin BK, Lund AK. Part Fibre Toxicol. 2022 Feb 9;19(1):10. doi: 10.1186/s12989-022-00452-3.
10. A novel predictor of ACE2-binding ability among betacoronaviruses. Dixson JD, Azad RK.Evol Med Public Health. 2021 Oct 13;9(1):360-373. doi: 10.1093/emph/eoab032.
11. Role of ribosomal RNA released from red cells in blood coagulation in zebrafish and humans. Alharbi A, Iyer N, Al Qaryoute A, Raman R, Burks DJ, Azad RK, Jagadeeswaran P. Blood Adv. 2021 Nov 23;5(22):4634-4647. doi: 10.1182/bloodadvances.2020003325.
12. SSG-LUGIA: Single Sequence based Genome Level Unsupervised Genomic Island Prediction Algorithm. Ibtehaz N, Ahmed I, Ahmed MS, Rahman MS, Azad RK, Bayzid MS. Brief Bioinform. 2021 Nov 5;22(6):bbab116. doi: 10.1093/bib/bbab116.
13. A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains. Sunuwar J, Azad RK. Brief Bioinform. 2021 Nov 5;22(6):bbab179. doi: 10.1093/bib/bbab179.
14. Discovery of mosaic genomic islands in Pseudomonas spp. Jani M, Azad RK. Arch Microbiol. 2021 Jul;203(5):2735-2742. doi: 10.1007/s00203-021-02253-2.
15. Traffic generated emissions alter the lung microbiota by promoting the expansion of Proteobacteria in C57Bl/6 mice placed on a high-fat diet. Daniel S, Pusadkar V, McDonald J, Mirpuri J, Azad RK, Goven A, Lund AK. Ecotoxicol Environ Saf. 2021 Apr 15;213:112035. doi: 10.1016/j.ecoenv.2021.112035.
16. The impact of multifactorial stress combination on plant growth and survival. Zandalinas SI, Sengupta S, Fritschi FB, Azad RK, Nechushtai R, Mittler R. New Phytol. 2021 May;230(3):1034-1048. doi: 10.1111/nph.17232.