Computational Biology Support


The Computational Biology Support team (CBS) provides bioinformatics support and collaborative data analysis solutions to researchers across the CRUK Manchester Institute. Our core activities reside at the interface between biology, computer science and statistics, particularly in the application of high-throughput omics technologies. The primary goal of the CBS team is to build and maintain an infrastructure that enables the application of advanced bioinformatics analysis to cutting-edge scientific research within CRUK MI.

A major aspect of our work has been to develop pipelines and computational approaches for analysing high-throughput datasets generated from next generation sequencing (NGS) and mass-spectrometry platforms. Most of the workflows that we developed utilise a combination of open-source and custom-built software tools running on the on-site high performance computing facility provided by the Scientific Computing team. Working closely with the SciCom team, we are currently building automated pipelines to handle routine tasks associated with the pre-processing of deep sequencing dataset, freeing valuable time for the more challenging downstream analysis and results interpretation.


Currently, the CBS team provide computational biology and bioinformatics support to the research groups at the Institute on genomics, epigenomics, transcriptomics, and highly multiplexed image analysis, including spatially resolved transcriptomic and proteomic data analysis. We utilise state-of-the-art computational tools, including machine learning and AI driven deep learning tools for spatially resolved cellular and subcellular multi-omics and multi-modal data analysis and integration.

We offer services on experimental design, data analysis and Institute-wide bioinformatics training on the following areas:

NGS, Proteomics and Imaging based data analysis

  • Bulk and Single-Cell Genomics and Transcriptomics
  • Multi-modal 10X Multiome (RNA+ATAC)
  • Variant Calling (WGS, WES, RNA-Seq)
  • Nanopore direct RNA sequencing analysis
  • Spatial multi-omics:
    • Spatial transcriptomics and proteomics (10X Visium/Xenium, NanoString GeoMx DSP/CosMx MSI, mIHC, mIF and CODEX)
  • Mass Spec Imaging (DESI, MALDI and SIMS)
  • Hyperion Image and CyTOF analysis
  • Hyperspectral image fusion and multi-modal data integration
  • Mass spectrometry-based protein and post-translation modification identification and quantification (DDA, DIA and PRM)

Other publicly available data sets:

  • TCGA/dbGap and data from published articles

Some examples of our analysis and publications:

Analysis of multi-modal scRNA-Seq, Valpione et al, EJC, 177 (2022) 164.
RNA Velocity: Imbalance of spliced and unspliced transcripts in scRNA-Seq.
Spatial Proteomics: CODEX analysis of Invasive Skin Cancer (Pedro et al.).
Spatial Transcriptomics: 10X Visium data showing single cell clustering and their spatial pattern within the tissue.
Identification of ROIs using histology images (left) and Integration of Hyperion and Visium data (right).

We encourage users interested in using our services to get in touch and discuss their requirements in advance. This allows us to assist them in experimental design and optimal parameter setting.

One-to-one training on data analysis can also be arranged.

For more information or to set up a request, please contact:

Team members

Sudhakar Sahoo

  • Head of Department

Robert Sellers

  • Principal Computational Biologist

Richard Reeves

  • Senior Computational Biologist

Adam Flinders

  • Senior Computational Biologist