Hello, I am

Vijender Singh

Bioinformatician | Data Analyst | Technology Enthusiast

I'm Vijender or VJ if you prefer. Nice to meet you!

Bioinformatician / Data Analyst

I'm a bioinformatician with passion to unreveal and understand the molecular basis of human pathologies using bioinformatics and data analysis skills. I believe that we are in very exciting phase of genomic studies as there are revolutionaly technologies being invented and cutting edge data analytics tools being build to help unravel the hidden mysteries of genome.

Personal Info

  • Current role : Lead Bioinformatics Consultant and Associate Director (Computational Biology Core, UConn Health)
  • Current role profile : 85% Data Analysis , 15% Administration/Management
  • Email : vijender.singh@uconn.edu, vijsingh@uchc.edu
  • Phone : +1-475-251-5506(US), +44-7383-797907(UK)
  • Location Farmington, Connecticut

My Interests

Cancer Genomics

Devoted to unraveling cancer's molecular underpinnings, I aim to enhance diagnostics and enable precision cancer medicine. Comprehensive studies reveal that diverse genetic irregularities can trigger a single cancer type. While infrequent recurrent genetic changes hinder driver mutation identification within a backdrop of passenger mutations, this challenge also presents an avenue to unearth novel drivers, fostering the development of biomarkers, drugs, and therapies..

Single cell and Spatial genomics

The realm of single-cell genomics has enabled isolated examination of individual cells, shedding light on intercellular variations within molecular processes. Recent strides in single-cell sequencing technologies, applied across domains like oncology, microbiology, neurology, and immunology, underscore the pivotal role these techniques hold in deciphering biological phenomena. Spatial genomics, on the other hand, empowers researchers to chart the spatial distribution of molecular occurrences within tissue sections, augmenting our comprehension by adding an extra dimension to the measurement of molecular changes at both single-cell and spatial levels.

Machine Learning

When faced with the complexities of considering numerous features that could influence an outcome (whether in classification, regression, prediction, or feature selection), human decision-making becomes notably formidable. The task of assigning priority and precise significance to these features, enabling informed decision-making, is precisely where machine learning intervenes, utilizing the entirety of accessible data to autonomously train and deliver optimal outcomes.

Building Edges to Connect Nodes

Confronting a fresh dataset or challenge necessitates an innovative approach, entailing the delineation of sequential stages (nodes) leading to the ultimate solution. I derive immense satisfaction from leveraging bioinformatics tools to establish the connections (building edges) between these stages, facilitating goal attainment. I construct workflows characterized by their resilience, scalability, and reproducibility, employing Nextflow and singularity containers for this purpose.

My Resume

Work Experience

2020 - Present

Lead Bioinformatics Consultant and Associate Director | Computational Biology Core | UConn Health, CT

Offer expertise in formulating and executing bioinformatics strategies for interpreting and managing genomics data, both within clinical and non-clinical contexts. Oversee and lead the computational biology core team, along with managing high-performance computing clusters. Guarantee the availability of current workflows while spearheading the creation of new analytical pipelines when needed. Provide valuable insights for research grant proposals by recommending suitable experimental and analytical approaches. Foster partnerships with pertinent academic and industrial collaborators to shape the core's strategic trajectory. Assume a leadership role in supervising administrative, technical, financial, and grant-related responsibilities that uphold the facility's operational efficiency.

2016 - 2020

Lead Bioinformatics Scientist | Computational Biology Core | UConn Health, CT

Offer expertise to research teams by providing consultation and practical assistance in genomics data analysis endeavors. Design and implement analytical workflows catering to various genomics data types such as transcriptomics, ChIP-seq, genome assembly, and single-cell genomics. Contribute to the setup and maintenance of the new computing cluster facility, manage public databases, and facilitate software package installations. Regularly convene meetings with researchers to collaboratively tackle bioinformatics-related challenges. Deliver hands-on analysis for a spectrum of genomics data including RNA-seq (mRNA, ncRNA, miRNA), ChIP-seq, transcriptome assembly, genome assembly, genome annotation, and single-cell transcriptomics, across both clinical and non-clinical datasets. Additionally, support the formulation of research grant proposals by advising on experiment and analysis designs tailored to genomics projects..

2011 - 2016

Senior Research Associate (NGS Analyst) | University of Dundee | Dundee, UK

Supervise research projects encompassing self-directed experimental design and execution, problem-solving, as well as the exploration and adoption of novel methodologies. Oversee laboratory equipment and data administration. Construct analytical frameworks for RNA-seq, ChIP-seq, and ATAC-seq investigations, while aiding fellow lab members in interpreting genomics datasets. Produce technical and scientific documentation, and evaluate scientific communication, reports, and publications. Provide guidance to PhD students and short-term trainees as their supervisor.

2007 - 2011

Postdoctoral Research Associate (Molecular Biologist) | University of Dundee | Dundee, UK

Lead independent research projects involving the planning and execution of experiments, troubleshooting, and the incorporation of emerging techniques. Manage lab instruments and oversee data organization. Develop analysis workflows for RNA-seq, ChIP-seq, and ATAC-seq studies, and support colleagues in their genomics dataset analyses. Craft technical and scientific reports, and review communication, reports, and publications within the field. Provide supervision to both PhD students and short-term trainees.

Education

2007

PhD in Life Sciences | Indian Institute of Science (IISc), India,

Relevant Techniques: Microarray data analysis, Molecular biology assays, NMR

2000

M.Sc. Chemistry | Bangalore University, India.

Relevant Courses: Biochemistry, Biophysics, Organic Chemistry, Molecular Biology

1997

B.Sc Life Sciences | Rajasthan University, India

Relevant Courses: Chemistry, Botany, and Zoology

2018

Biostatistics for Health Professionals, UConn (Audit)

Relevant Courses: Clinical and non-clinical data analysis, SPSS

2012 - 2016

Certificates:

Supervised Machine Learning: Regression and Classification (Stanford University)

Statistics for Genomic Data Science (John Hopkins University)

Principles of Management, University of Dundee (Jan 2011)

Strategic Management, University of Dundee (Jun 2012)

Skills

Communication
Team player
Critical Thinking / TroubleShooting
Time Mangement/ SMART goal setting
Self Awareness
Willingness to learn and unlearn

Programming Languages

R
Linux
Python
Containers
Machine Learning Methods
Nextflow (Workflow Development)
High Performance Computing (HPC)
AWS

Bioinformatics Skills

WGS/WXS/Varaint calling (GATK, Freebayes, DeepVariant)
ChipSeq/ATAC-seq/WGBS
Single Cell Genomics: Transcriptomics, CITE-seq, ATACseq
Genome Assembly and Annotation (Illumina Short read, ONT, PacBio)
Transcriptome Assembly
Spatial Transcriptomics

Current Projects

Project 1
Elucidating the role of GNAS in Albright hereditary osteodystrophy (AHO)

Current

I am currently engaged in a collaborative effort with Dr. Emily Germain-Lee's team, which is dedicated to exploring the function of the GNAS gene that encodes the alpha stimulatory subunit of the G protein in the context of Albright hereditary osteodystrophy (AHO). As part of this project, we have created a mouse model with mutated GNAS and are conducting analyses on biopsied tissues from affected regions. These analyses aim to uncover alterations in gene expression, utilizing RNA sequencing (RNAseq) techniques to gain insights into shifts in gene activity and signaling pathways. .

Tools used: HPC, Cluster, FastQC, Cutadapt, HiSAT2, Stringtie, HTSeq-count, Ballgown, DESeq2, R, Bioconductor

Project 2
Undestanding the connection between major depressive disorder and Alzheimer’s disease and related dementia (ADRD)

Current

Major depressive disorder (MDD) is one of the most common psychiatric disorders across the lifespan. Besides the disability associated with acute depressive episodes, MDD is also a major risk factor for Alzheimer’s disease and related dementia (ADRD). The mechanisms by which MDD leads to a higher risk of ADRD later in life are poorly understood but probably involve the abnormal regulation of multiple biological processes related to accelerated biological aging (BA). We are investigating the associations of MDD with different biological aging processesand exploring if accelerated BA mediates the association between MDD and the risk of ADRD.

Tools used: R, Bioconductor, IPA, GSEA and others

Project 3

Understanding the process of healing in hip and elbow injury.

Current

In partnership with Professor Guzzo's team at UConn Health, we are delving into the molecular aspects of the healing process after hip and elbow injuries using a mouse model system. Our approach involves employing single-cell multimodal analysis, which combines RNA sequencing (RNAseq) and Assay for Transposase-Accessible Chromatin sequencing (ATAC seq). Through this innovative technique, we aim to uncover intricate changes occurring at the single-cell level, ultimately enhancing our comprehension of the underlying biological mechanisms governing the healing process.

Tools used: CellRanger, R, Bioconductor, Seurat, Monocle, IPA, GSEA and others

Project 2
Understanding the association between genetic variability within critically ill children and morbidity after PICU discharge.

Stage: Preliminary discussions

In this study we will assess the effects of genetic variation in candidate SNPs on functional status of pediatric survivors of critical illness, focusing on those with acute respiratory failure including children with sepsis and/or pneumonia. Blood plasma profiles will be used in the analysis.

workflow to be finalised.