Faculty Details

Faculty Details

  • Name: Dr. Pankaj Singh Dholaniya
  • Mail: pankazuohyd.ac.in
  • Contact: 040-2313-4591
  • Area Of Research: Theoretical and Data BIology
    Bioinformatics Data Science, Neurological Disorders, Disease Biology



Ph.D Biotechnology (2017)

Department of Biotechnology & Bioinformatics, University of Hyderabad, Hyderabad, Telangana.


M. Tech. Bioinformatics (2011)

University of Hyderabad, Hyderabad, Andhra Pradesh;


B. Tech. Biotechnology (2009)
SVBP University of Agri. & Tech., Meerut, U.P; 


German Academic Exchange Service (DAAD) fellowship under NPTI program 2012

CSIR-NET Lectureship 2010

DBT-JRF 2009 and 2010

GATE 2009


  • Computers and Programming
  • Machine learning methods and Data analytics
  • Database design and development
  • (For M.Tech Bioinformatics and integrated M.Sc. Systems Biology students)

.     .         .      

Scholar    Facebook   LinkedIn     Twitter

No Records Available

Students/Projects Trainees

Name Joined As Mail Contact
Baby Kumari Ph.D. baby059696@gmail.com ---
Neha Ph.D. nehaskumar101@gmail.com ---
Ahmad Ph.D. ahmadmailbook@gmail.com ---
MD Zainul Ali Ph.D. mdzainulali@gmail.com ---
Rittik Mandal Summer Intern rittikhtml@gmail.com ---
Shashaank Varma Summer Intern shashaankvarma@iitkgp.ac.in ---
Mallikarjun Ratnam P. Project Student (M.Tech., Bioinformatics) 21lsmi11@uohyd.ac.in ---
E. Sai Suvani Project Student (M.Tech., Bioinformatics) 21lsmi12@uohyd.ac.in ---
Kata Krishna Prasad Project Student (M.Tech., Bioinformatics) 21lsmi16@uohyd.ac.in ---
Prashant Kumar Project Student (M.Sc., Bioitechnology) pkprashantsingh5577@gmail.com ---

Former Students/Projects Trainees

Name Joined As Working
Samreen Project Student (M.Tech., Bioinformatics)
Mukulika Mandal Project Student (M.Tech., Bioinformatics) Ph.D., University of Hyderabad
MD Zainul Ali Project Student (M.Sc., Bioitechnology) Continuing Ph.D. with same group
Kushagra Project Student (M.Tech., Bioinformatics) Ph.D., CDRI Lucknow
Vikram Teja Naik Project Student (M.Tech., Bioinformatics)
Swarna Kundu Project Student (M.Tech., Bioinformatics) Healthcare Analyst, Apollo Health
Itishree Jali Project Student (M.Tech., Bioinformatics) Ph.D., NIAB, Hyderabad
Aishwarya Project Student (M.Sc., Bioitechnology) Ph.D., CCMB, Hyderabad
Manish Project Student (I.M.S., Systems Biology)
Neha Project Student (M.Sc., Bioitechnology) Continuing Ph.D. with same group
Sarthak Srivastava Project Student (M.Sc., Bioitechnology)
Aishwarya Subramaniam Project Student (M.Tech., Bioinformatics) Sofware Engineer (ML in Healthcare), Tech Mahindra
Mayur Saini Project Student (M.Tech., Bioinformatics)
Sreenidhi Ramamoorthy Project Student (M.Tech., Bioinformatics)
LOGA PRIYA R Project Student (M.Tech., Bioinformatics)
Sourav Jana Project Student (M.Sc., Bioitechnology)
14 Ali Zainul MD, Dholaniya, Pankaj Singh* (2022) , Oxidative phosphorylation mediated pathogenesis of Parkinson\'s disease and its implication via Akt signaling , Neurochemistry International, Elsevier,152,105344,.
13 Sahu S., Dholaniya, Pankaj Singh*., T Sobha Rani*. (2022) , Identifying the candidate genes using co-expression, GO, and machine learning techniques for Alzheimer’s disease , Network Modeling and Analysis in Health Informatics and Bioinformatics, Springer,11,10,.
12 Muttineni, Radhakrishna., Kammili, Nagamani., Bingi, Thrilok Chander., Rao M, Raja., Putty, Kalyani., Dholaniya, Pankaj Singh., Puli, Ravi Kumar., Pakalapati, Sunitha., Doodipala, Mallikarjuna Reddy., Upadhyay, Amit A., (2021) , Clinical and whole genome characterization of SARS-CoV-2 in India , Plos one, Public Library of Science San Francisco, CA USA,16,2,e0246173.
11 Dholaniya, Pankaj Singh*., Rizvi, Samreen. (2021) , Effect of various sequence descriptors in predicting human protein-protein interactions using ANN-based prediction models , Current Bioinformatics, Bentham Science,16,8,1024 - 1033.
10 S, Neha., Dholaniya, Pankaj Singh* (2021) , The Prevailing Role of Topoisomerase 2 Beta and its Associated Genes in Neurons , Molecular Neurobiology, Springer,58,,6443–6459.
9 Kumari, Baby., Mandal, Mukulika., Dholaniya, Pankaj Singh*, (2020) , Analysis of multiple transcriptome data to determine age-associated genes for the progression of Parkinson\'s disease , Meta Gene, Elsevier,25,,100712.
8 Gupta, Prateek., Dholaniya, Pankaj Singh., Devulapalli, Sameera., Tawari, Nilesh Ramesh., Sreelakshmi, Yellamaraju., Sharma, Rameshwar., (2020) , Reanalysis of Genome Sequences of tomato accessions and its wild relatives: Development of tomato genomic variation (TGV) database integrating SNPs and INDELs polymorphisms , Bioinformatics, Oxford University Press,36,20,4984-4990.
7 Kakade, Aishwarya., Kumari, Baby., Dholaniya, Pankaj Singh.*, (2018) , Feature selection using logistic regression in case–control DNA methylation data of Parkinson\'s disease: A comparative study , Journal of theoretical biology, Academic Press,457,,14-18.
6 Bollimpelli, V Satish., Dholaniya, Pankaj S., Kondapi, Anand K., (2017) , Topoisomerase IIβ and its role in different biological contexts , Archives of biochemistry and biophysics, Academic Press,633,,78-84.
5 Gupta, K Preeti., Dholaniya, Pankaj Singh., Chekuri, Anil., Kondapi, Anand K., (2015) , Analysis of gene expression during aging of CGNs in culture: implication of SLIT2 and NPY in senescence , Age (GeroScience), Springer International Publishing,37,3,1-14.
4 Dholaniya, Pankaj Singh., Ghosh, Soumitra., Surampudi, Bapi Raju., Kondapi, Anand K., (2015) , A knowledge driven supervised learning approach to identify gene network of differentially up-regulated genes during neuronal senescence in Rattus norvegicus , Biosystems, Elsevier,135,,9-14.
3 Meetei, Potshangbam Angamba., Singh, Pankaj., Nongdam, Potshangbam., Prabhu, N Prakash., Rathore, RS., Vindal, Vaibhav., (2012) , NeMedPlant: a database of therapeutic applications and chemical constituents of medicinal plants from north-east region of India , Bioinformation, Biomedical Informatics Publishing Group,8,4,209.
2 Arun, PV Parvati Sai., Bakku, Ranjith Kumar., Subhashini, Mranu., Singh, Pankaj., Prabhu, N Prakash., Suzuki, Iwane., Prakash, Jogadhenu SS., (2012) , CyanoPhyChe: a database for physico-chemical properties, structure and biochemical pathway information of cyanobacterial proteins , PloS one, Public Library of Science,7,11,e49425.
1 Singh, Pankaj., Suryavanshi, R., Sailu, S., Vindal, V., Kondapi, AK., Rathore, RS., (2010) , HIV Information REsources (HIRE): An Integrated Knowledgebase for Immunodeficiency Viruses , Proceedings of 2010 First International Conference on Cellular, Molecular Biology, Biophysics and Bioengineering (Volume 3), ,,,.
Title Year Duration Amount(In Lakhs) PI/I
DBT-Centre for Microbial Informatics (DBT-CMI) (approved by the Taskforce, DBT; awaiting financial sanction) 2021 5 176 Prof. H.A.Nagarajaram (PI), Prof. Appa Rao Podile (Co-PI), Prof. Dayananda Siddavattam (Co-PI), Prof. Ch Venkataramana (Co-PI), Dr. Pankaj Singh (Co-PI), Dr. Vivek (Co-PI), Dr. Manjari Kiran (Co-PI)
Investigating the miRNA based biomarkers for early diagnosis of Parkinson’s Disease for chemically induced PD model in Rat. 2021 3 40 Dr. Pankaj Singh Dholaniya
A novel computational approach to generate genome based barcode intended to catalog Indian microbial species. 2020 5 68.6 Dr. Manjari Kiran, Dr. Pankaj S Dholaniya
Modulating the expression of topo2b to enhance the viability of CGNs through genome editing 2018 3 59.2 Dr. Pankaj Singh Dholaniya

Knowledge Discovery in Disease Biology:
As more sophisticated technologies in life science/healthcare are constantly being developed, an enormous amount of data has been generated and deposited on various platforms. Looking from the perspective of a particular disease, these data appear to be the pieces of a broken puzzle. This poses a challenge for data scientists to put these parts together and draw conclusive information out of it and to put forward the potential therapeutic targets for the disease. For example, in order to understand the disease mechanism, the data from heterogeneous resources could be collected and analyzed in relation to the molecular pathways centralized to the disease. The behaviour of such pathways can be studied under normal vs diseased conditions for identifying the probable drug targets for the disease. The data resources that can be considered for such studies are genome-wide expression/mutation studies of patients and healthy controls, patients’ specific genetic mutations, expression of non-coding DNA elements, clinical characteristics, and integrated pathway information.
Understanding Sequence-Function Relationship in Proteins/DNA:
The behaviour of a protein inside or outside the cell is defined by its interaction with the elements present in the surrounding environment, which include small metabolites to the macromolecules such as RNA, DNA, or proteins. The interactions of a protein with these elements are determined by how it folds in 3-dimensional space, and this three-dimensional folding of a protein largely depends on the linear sequence of amino acids. In summary, it can be stated that the function of a protein is determined by its interaction, the interaction of a protein is determined by its structure and the structure of a protein is determined by its sequence. As determining the 3D structure of a protein is a challenging task (computationally as well experimentally), we deploy various ML-based to understand the molecular interactions of protein from its sequence properties. This has large application in terms of predicting potential interactions of a protein with that of small molecules (drug-like/metabolites) and macromolecules such as RNA, DNA, or proteins.

Neurological Disorders:
We are interested in investigating the mechanism and the molecular markers for neuronal senescence and age-associated neurodegenerative diseases. Currently, we are working towards the computational modelling of Parkinson’s disease from an integrated analysis of the GWAS, gene expression, proteome, and pathway data to explain the disease and non-disease states in terms of the gene/protein interaction networks. We are also working on the characterization of the role of Topoisomerase 2 beta (Top2b) and its associated genes in neuronal senescence by modulating the expression of the Top2b enzyme through genome editing techniques.

Genome Sequences:

7 Whole-genome sequences of Severe Acute Respiratory Syndrome Coronavirus 2 isolate (SARS-CoV-2) from Hyderabad, India#

  1. GenBank: MT415320.1; https://www.ncbi.nlm.nih.gov/nuccore/MT415320 
    GISAID: EPI_ISL_431101
  2. GenBank: MT415321.1; https://www.ncbi.nlm.nih.gov/nuccore/MT415321
  3. GenBank: MT415322.1; https://www.ncbi.nlm.nih.gov/nuccore/MT415322 
  4. GenBank: MT415323.1; https://www.ncbi.nlm.nih.gov/nuccore/MT415323 
  5. GenBank: MT477885.1; https://www.ncbi.nlm.nih.gov/nuccore/MT477885 
  6. GenBank: MT457402.1; https://www.ncbi.nlm.nih.gov/nuccore/MT457402 
  7. GenBank: MT457403.1; https://www.ncbi.nlm.nih.gov/nuccore/MT457403 

Tools and Databases:

  1. RTGR-iTGV: Induced Tomato genomic variation database#
  2. RTGR-TGV: Tomato genomic variation database#

Microarray Datasets:

  1. E-MTAB-3977 - Transcription profiling by array of Human SUP-T1cells infected with HIV and/or treated with curcumin
  2. E-MTAB-4552 - Expression profiling of cerebellar granule neurons from Rattus norvegicus cultured for four week

#Work in collaboration