Holmes Lab

Computational Biology Research

Genome sequences are fundamental to many fields: a living history of molecular evolution, a reference frame for medicine, a substrate for bioengineering. Our UC Berkeley-based team makes the software that empowers genome scientists to decode, analyze, annotate, and design these sequences.

Our group is a mix of academics and research software engineers, working on intersecting projects. We develop new algorithms and models, gather data on natural and synthetic sequences, and develop code to visualize and explore data.

We use techniques from Bayesian statistics and deep learning through to modern web development. Recent papers describe software for annotating genomes collaboratively over the web, reconstructing ancestral viruses, and designing protein libraries for directed evolution.

What are the equations describing the dynamics of sequence evolution?

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What can single-molecule resolution tell us about the structure of genomes and populations?

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How can machine learning guide the design of genes, genomes, and microbial communities?

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Why shouldn’t bioinformatics be as easy, intuitive, and collaborative as the rest of the web?

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Bioinformatics

ALIFE 2020: The 2020 Conference on Artificial Life

PLoS Computational Biology

More Publications

Annabel Large

PhD Student, Bioengineering

Jordi Silvestre-Ryan

PhD Student, Bioengineering

Shihab Dider

Graduate Student, UC Berkeley

Ian Holmes

Professor, UC Berkeley

Robert Buels

Software Engineering Lead, UC Berkeley

Colin Diesh

Software Developer, University of California, Department of Bioengineering

Elliot Hershberg

Software Developer, University of California, Department of Bioengineering

Garrett Stevens

Software Developer, UC Berkeley

Peter Xie

Software Developer, University of California, Berkeley, Department of Bioengineering

Teresa De Jesus Martinez

Software Developer, University of California,Berkeley Department of Bioengineering

Full team

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