Principal Bioinformatician · Cambridge, UK
Computational biology for understanding cells at scale.
I lead computational research at the Wellcome Sanger Institute, connecting single-cell and spatial genomics, regulatory biology and machine learning.
Experience
Scientific leadership and computational research
Principal Bioinformatician
Leading projects across large-scale single-cell, multiome and spatial genomics, with a focus on development, ageing, inflammation and malignancy.
Consultant
Large-scale single-cell perturbation analysis and modelling.
Senior Bioinformatician & Research Fellow
Research across single-cell RNA sequencing, CITE-seq, ChIP-seq and CRISPR.
BBSRC Research Fellow
Integrative regulatory genomics using RNA-seq, DNase-seq and ChIP-seq.
Research
Selected areas of work
Human developmental atlas
Integrating millions of cells across scRNA-seq, multiome and spatial data to map prenatal human development.
Perturbation modelling
Using machine learning to predict how genetic and molecular perturbations reshape cellular trajectories.
Skin and inflammation
Studying immune memory, atopic dermatitis, development, ageing and skin malignancies at single-cell resolution.
Regulatory genomics
Identifying transcription-factor programmes and regulatory mechanisms that govern differentiation and disease.
Tools
Open-source software
Python · Single cell
scanpy_plus
A practical toolbox of extensions and utilities for Scanpy and AnnData workflows.
Workflow · 10x Genomics
single-cell-10x
Resources and workflows for processing and analysing 10x single-cell data.
R · Regulatory genomics
lenhancer
Enhancer identification using LASSO-based modelling of regulatory genomics data.
Python · Command line
solosis
A plug-and-play command-line toolkit for reproducible bioinformatics in the laboratory.
Spatial · Multi-omics
SpaceMOFA
Tools for factor-based integration and exploration of spatial multi-omics data.
Publications
Selected research
An integrated single-cell and spatial omics atlas of human prenatal development
Webb et al. · bioRxiv · Under revision at Nature
Predicting how perturbations reshape cellular trajectories with PerturbGen
Ly et al. · bioRxiv · Under review at Nature
Self-supervised learning for a gene program-centric view of cell states
Moullet et al. · bioRxiv · Under review at Science
Hidden immune memory niches in inflammatory skin diseases
Steele et al. · Accepted at Nature Medicine
The longitudinal dynamics and natural history of clonal haematopoiesis
Fabre et al. · Nature
Identification of gene-specific cis-regulatory elements during differentiation
Vijayabaskar et al. · PLOS Computational Biology
Skills
Expertise and education
Single-cell & spatial
scRNA-seq, scATAC-seq, multiome, CITE-seq, Xenium, Visium, Scanpy, SnapATAC2, Muon, SCENIC+
Genomics
RNA-seq, ChIP-seq, DNase-seq, CRISPR screens, DNA methylation, gene networks and motif analysis
Computation
Python, R, Bash, Linux, Git, Docker, Singularity, HPC, foundation models and multimodal learning
Leadership
Research strategy, team management, project delivery, doctoral supervision, teaching and collaboration
PhD Computational Biology · Indian Institute of Science, 2012
Pre-doctoral degree Biophysics · Indian Institute of Science, 2008
BEng Chemical Engineering & Biotechnology, 2005
Contact
Let’s work together
For research collaborations, scientific leadership and computational biology consulting.
vjbaskar@gmail.com