Computational Biology Facility

The Computational Biology Facility (CBF) is a team of highly experienced and qualified data scientists, bioinformaticians and software developers. We work as partners, collaborators or service providers supporting data-driven biological and clinical research, offering tailor-made, gold standard solutions across a wide range of bioinformatics, statistics, and functional interpretation of data.

Click here to access the full Computational Biology Facility website.

What you'll receive from our facility

• Support from project conception to publication or application in all areas related to data science, protein bioinformatics and software development applied to biological, biotechnological or clinical aspects
• Access to cutting-edge expertise in the analysis of all types of life sciences data including the fast-moving spheres of single cell and geospatial RNAseq
• Full academic supervision and a highly specialised team of technologists dedicated to the project
• Support in experimental design and power calculations for your proposed studies
• Project management in all projects and support with FAIR data sharing practices
• Provision of high-quality publishable data visualisations and outputs
• Development opportunities – with a full suite of courses to support a learning path in data analysis from beginner to expert, using examples from life sciences and clinical research.

More information about our team expertise can be found here.

Who can use our facilities

• University of Liverpool academic staff
• Researchers from other universities/ research institutions
• Industrial research partners.

What the Computational Biology Facility can be used for

• Omics analyses with broad expertise in transcriptomics, methylation, proteomics (including PTM and cell signalling analyses) and metabolomics
• Multi-omics integration
• Spatial omics particularly in Nanostring GeoMx platform analyses
• Single cell omics analyses
• In-silico biomarker discovery applying machine learning methods
• Drug repositioning
• Systems biology approaches including building networks and predicting regulators
• In-silico Protein structure modelling including AlphaFold2
• Protein function prediction
• Expertise in machine learning, the use of large language models and natural language processing
• Software development and database management
• R, statistics and bioinformatics teaching.

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