Post doctoral fellows

Sheikh Nizamuddin

(Postdoctoral Fellow; Oct 01, 2017 onwards)

Previous affiliation: Ph.D. from CSIR-Centre for Cellular and Molecular Biology, Hyderabad India.

Research Interests: My interests lies in statistical methodology, data analysis and programming languages. Notably, (1) I have generated Indian specific haplotype reference panel for statistical imputation of missing genotype in Affymetrix array (6.0) data-sets, (2) identified Indian specific gene THSD7A in association with obesity, (3) explored genetics behind Indian traditional medicine Ayurveda, (4) identified novel marker rs2470102 (SLC24A5 ) in association with Skin pigmentation and (5) c.*84G>A in CETP associated with coronary artery disease.

Current Project: Currently, I am working as a postdoctoral fellow under the mentorship of Professor S. Ahmad. My research work focus on the implication of DNA shape to understand genetic mechanism in various disorders including cancer and tuberculosis. To establish DNA shape as better predictor, comparative to DNA variations, in non-coding region of genome.

Doctoral students

Ajay Kumar Verma

(Ph.D.; July 31, 2013 onwards)

Previous affiliation: M.Tech in Computer Science & Technology from Jawaharlal Nehru University; M.A. in Mathematics, B.A.(Hons) in Mathematics from Banaras Hindu University.

Research Interests: My interests are in the area of Machine learning, Data mining, and Bioinformatics

Current Project:I am currently working on denoising microarray data for integrative meta analysis using deep learning.

Sucheta Chauhan

(PhD; July 14, 2014 onwards)

Previous affiliation: M.Tech. in Computer Science and Engineering from Mody Institute of Technology and Science, Lakshmangarh, Rajasthan; B.Tech. in Information Technology from Uttar Pradesh Technical University, Lucknow.

Research Interests: Machine learning, Artificial neural network, Deep learning. 

Current Project: My aim is to develop an efficient computational model for disease diagnosis. Deep learning architectures like Convolutional Neural Network, Recurrent Neural Network, Long-short Term Memory etc. have been used for classification and prediction of time series sequences. Deep learning have been successfully applied to a variety of applications, including image classification, prediction, segmentation and denoising.

Ajay Arya

(PhD; Jan 06, 2017 onwards)

Previous affiliation: Completed M. Tech. in Bioinformatics from University of Hyderabad

Research Interests: I am interested in machine learning (classical and deep learning) techniques to predict DNA-protein interactions, drug resistance prevalence/pattern and binding/active sites of DNA or proteins. I am interested in applications of these methods to the development of new drugs/vaccines and disease diagnosis.

Current Project: Transcription factors (TFs) have been known for long to bind to specific conserved motifs (TF binding sites TFBS), thereby initiating and regulating genes associated with those sequences. This is the key step or precursor to cell development and organization, to generate a functional unit of life. Most recently our lab has developed a novel method to study DNA shape viz. DynaSeq. In this approach, the scope of shape parameters has been broadened to look into the dynamics instead of static properties and into the other conformational features, which improve the insights earlier obtained by DNAshape. Our lab is working on novel sequence-based conformational ensemble prediction method(DynaSeq) and cell specific protein-DNA interactions. So I am currently working on improvement of methods to model protein-DNA complexes using DynaSeq-predicted DNA structures and investigating their specificity.

Dana Mary Varghese

(PhD; July 28, 2017 onwards)

Previous affiliation: M.Tech. in Bioinformatics from Karunya University,Coimbatore; M.Sc. in Biotechnology from VIT University, Vellore; B.Sc. in Biotechnology from Dr.G.R.Damodaran College of Science, Bharatiar University, Coimbatore.

Research Interests: My interest lies in applying machine learning approach to understand the condition specific functional regulation and interactions of protein and DNA.

Current Project: In this post genomic era functional annotation of genomes is a challenging process, discovery of moonlighting proteins, nature's way of multitasking has lead to many compelling questions. A lot of proteomics studies are carried out to identify drug targets but the moonlighting ability of certain proteins further complicates the analysis of gene expression, protein-protein interaction and other such studies. Moonlighting properties of proteins have been implicated in several diseases, bacterial virulence .It may possibly play a role in side effects caused by drugs. I am currently working on function prediction of moonlighting proteins. In future I hope to study condition specific interactions of these proteins in systems level.

Manisha Kalsan

(PhD; July 30, 2017 onwards)

Previous affiliation: M.Tech. in Bioinformatics from Delhi Technological University, Delhi; B.Tech. in Biotechnology from D C R University of Science & Technology, Murthal, Sonepat

Research Interests: My interest lies in the applications of machine learning techniques to study the sequential and structural features of different transcription factors and their target sites, and to predict the different states of a genome on the basis of the conformational parameters of the DNA to annotate the genome for novel insights. I am also interested in developing predictors based on shape profiles for different types of target sites for a transcription factor, showing different temporal pattern.

Current Project: Transcription factors (TFs) have been broadly divided into three different classes- pioneer, migrant and settler, on the basis of their ability to recognize different target chromatin. My work aims to apply machine learning techniques to learn the structural features of TFs and/or target sites to classify these TFs into the above mentioned classes. My work also aims to gain insights into the spatiotemporal dynamics of different TFs and the cellular specificity of their targets which hold great importance for the regulation of gene expression.

Shruti Gupta

(PhD; July 28, 2017 onwards)

Previous affiliation: M.Sc. Computational and Integrative Sciences, School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi;B.Sc.(H) Biomedical Sciences, Acharya Narendra Dev College, University of Delhi, New Delhi

Research Interests: I am interested in predictive analytics and re-purposing of biological data to address questions in systems biology and to develop computational tools which can aid in the application of these studies further.

Current Project: I am currently working on protein complexes classification problem as an extension to the previously published tool PPiPP. Along with that, I am looking into profile based mining of gene expression datasets and effects of query partialisation on the overall data.

Project students/Interns

Anuja Jain

(PhD. candidate)

Research Interests: I have worked on the analysis of apomictic and sexual small RNA, illumina sequenced data of pennecitum glaucum for identification of trans-actingRNA and miRNA. Demultiplexing of sequenced data, quality checking of reads, adaptor trimming, filtering and prediction of small RNA involves several genomics tools. Script were used for visualization of phasing of trans-acting RNA and abundance count (expression) of up-regulated and down-regulated miRNA. For both apomictic and sexual condition, some of putative trans-actingRNA based on minimum p-value and miRNA according to their normalized expression count have selected and will confirm by PCR. Key learning's- Above mention study enhanced my knowledge about NGS technology and their implementation on gene regulation, miRNA and transactingRNA prediction. Earlier, I worked on the identification of linear and conformational B cell epitopes of major and minor peanut allergens using a consensus approach employing sequence, structure and protein-protein interaction tools. Several physiochemical properties like hydrophilicity, accessibility, coil residues, exposed surface and antigenicity were used to analyse predicted epitopes. The predicted epitopes show a good correlation with that of experimentally elucidated epitopes. The T cell epitopes for the above allergens were predicted using inhibitory concentration (IC50) value based quantitative prediction methods and binding status scoring qualitative prediction methods. A similar approach may be employed for prediction of epitopes in novel proteins. Key learning's- This helped me to understand the fundamentals of protein modelling, epitope prediction and how to co-relate bioinformatics with immunology.

Arfa Jabin

(M.Sc; July 26, 2017 onwards)

Previous affiliation: Bachelor of Computer Application from Aligarh Muslim University, Aligarh

Research Interests: Machine Learning, Neural Networks, Genome Analysis, Big Data Analytics, Computational Biology, Cloud Computing, Data Mining.

Current Project: Data Compression is a technique to reduce the number of bits to store and transmit the data. There are various data compression techniques that are lossy as well as lossless. The project is about finding new and better algorithm for data compression by getting ideas from previous techniques like Huffman Coding, Burrows Wheeler Transform , gzip2 etc. with a primary focus on compressing DNA sequence data.

Joshua Baskaran

(M.Sc; July 26, 2017 onwards)

Previous affiliation: B.Sc. with Honours in Chemistry from St. Stephen’s College, University of Delhi, New Delhi

Research Interests: Chemical Biology, Medicinal Chemistry, Molecular Biology, Bioinformatics and Scientific Computing, Data Analytics.

Current Project: Currently, I am working on the understanding the development of anthers in plants based on the transcriptome and the gene expression data using bioinformatics tools and data analytics.

Research Volunteers/Collaborative visitors

Saurabh Sugha

(Volunteer, Sep 18, 2016 onwards)

Primary affiliation: Currently I am associated with Samsung India as a Business Analyst in Consumer Electronics Division. I have over 7-year work experience in Big Data Analytics, Data Visualization, Visual Basic Programming, Qlick View, Microsoft Dynamic AX - Business Intelligence System Automations, Database Management and Strategic Consulting. Past association was with Canon, Haier, Videocon Industries as a Business Operation Consultant.

Research Interests: My interest lies in exploring the dynamics of Computational Biology, Machine learning, Neural networking, Deep learning, Text mining and Big data analytics. I am passionate about conducting an Integrative analysis of Biological data and developing an algorithm for Text mining to facilitate understanding of the Whole-Genome Sequencing and Molecular Epidemiology of infectious diseases. I am excited about the increasing role of Social Media and Mobile Technologies while taking a “Big data approach” to conduct Epidemiological disease modeling.

Current Project: My current project involves the use of bioinformatics analytical tools and high level programing languages like Python/Java for streaming into the Application programming interface (API) of the social networking site (Twitter/Facebook). Idea is to extract epidemiologically relevant tweets or responses of the users and performing a comparative study of the medical symptoms or its side effects while developing a correlative mapping of diseases using unified medical language system (UMLS), SIDER (medical side effect) and other medical portals. The Project involves taking a Big data approach for performing disease modelling and sentiment analysis so as to developing an algorithm to expand the meta-thesaurus of the infectious diseases and its symptoms.

Saumya Priyadarshini

(Research Assistant; Oct 01, 2017 onwards)

Previous affiliation: M.Tech in Biomedical Engineering from Delhi Technological University; B.Tech in Electronics and Communication from Rajasthan Technical university.

Research Interests: My research interests lies in the area of Signal Processing, Machine Learning, Data Analysis. I want to implement these tools for understanding brain dynamics and neural connectivity.

Current Project: In SciWhy Lab, my project concerns with the classification of Brain MRI Data using Machine Learning technique. I am interested in knowing the capabilities of neuroinformatic tools and brain simulation tools which are important for the analysis of increasingly large-volume, high-dimensional experimental data.

Past Members

Aditya Bajaj

(Research Intern (Completed); June 25-October 30, 2016)

Previous affiliation: Integrated M.Sc. – Ph.D. in Molecular Medicine at the Special Centre for Molecular Medicine, Jawaharlal Nehru University

Research Interests: Broadly include Medical Microbiology- molecular basis of infectious diseases, host microbe relationships and human gut microbiome and its roles to modulate human physiological processes. In future I would like to emphasise on bacterial biofilms and quorum sensing in health and diseases and to study aspects of microbial antibiotic resistance and nosocomial infections.

Project completed: To utilise Computational biology tools to study the Spoligotyping patterns in different members of Mycobacterium tuberculosis complex (MTBC). Spacer oligonucleotide typing, or spoligotyping, is a rapid, polymerase chain reaction (PCR)-based method for genotyping strains of the Mycobacterium tuberculosis complex. The project aims to decipher links between these spoligotyping patterns and the clinical and epidemiological characteristics of various Mycobacterial strains.

Manasvini Markandey

(Research Intern (Completed); June 25-October 30, 2016)

Previous affiliation: Compeleted MSc in Biotechnology from Faculty of Life Sciences & Biotechnology, South Asian University, New Delhi

Previous Qualification: BSc (H) Microbiology, Ram Lal Anand College, University of Delhi

Research Interests: My interest lies in the field of medical microbiology. Particularly I wish to study the molecular basis of host-pathogen interactions and unravelling how the host physiology is altered as an infection ensues. Furthermore, I wish to develop on these mechanisms to design drugs that can possibly be used to cure the common, yet severe diseases. Another field of interest to me is the resident micro-flora of the human body and its effects on the host physiology. These microbes aren’t pathogenic, but can turn from friends to foes under certain circumstances. Studying the mechanisms by which this symbiotic relationship operates in its bidirectional fashion is a key area of research these days, since it’s now known that these micro-organisms may play substantial roles in altering the vulnerability of the human system towards various pathogenic infections.

Project completed: To use bioinformatic tools to identify the genes and proteins in host system and the respective pathogenic molecules of Zika Virus interacting with these host molecules, so as to be able to define an outlining network of host-pathogen interactions underlying ZIKV infection in humans. We’d also like to extend it by carrying out co-infection analysis and identifying other pathogens that share similar molecular pathways during infection.