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Thomas edited this page Jun 4, 2024 · 10 revisions

This is the wiki for the Neurogenomics labs in the Department of Brain Sciences at Imperial College London run by Dr Nathan Skene. We use cell-type specific genomic datasets to investigate the mechanisms underlying brain disorders. We are interested in asking simple questions about complex traits: which cells they act through, at which life stage they are active, and what processes are affected within each cell type. We use and develop novel experimental and computational methods to tackle these questions.

For more information about lab, follow the links on this wiki, visit the lab website and check out the lab GitHub.

New starters

If you are new to the lab then you should read the to-do list for new starters.

If you will be doing experimental work, please see the list of required courses and forms for new experimental staff.

Whether you are a computational researcher new to biology, or a biologist new to computational research, we suggest you take a look at our recommended reading list. We don't expect you to read all these overnight but you are expected to read widely and deeply. Do not just read papers: read books. Read within what you percieve your field to be, and outside your field. Read history and philosophy. Read biographies of other scientists. Science is a marathon not a sprint, and if you see the full context of your work it makes it more fun.

Once you've settled in a bit, you should familiarise yourself with the labs position on reproducible bioinformatics: why we believe in it and and how we aim to act on this.

You might want to look into possible training opportunities. Take a look at this page of useful info so you know what's there when you need it!

Commercialisation

The lab believes in the value of developing commercialisable research. We strongly advise you to consider how your research and career can lead to benefits for the rest of humanity. One of the best ways you can do this is to start a company and leverage venture capital. We've got some notes on doing this here and we are always happy to chat about it.

Useful info for computational researchers

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