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To do list for new starters
Send me your username so I can give you access to the computing cluster and shared project spaces
Join the UKDRI slack channel once you have a DRI login, and sign up to the #lab-neurogenomics channel. If you do not have a DRI login (e.g. you are a masters student), then badget me to add you as a single channel guest, otherwise you'll miss a lot of lab info.
Send me a profile photo and a short bio paragraph to go on the lab website. Send these to Tom Roberts ([email protected]).
Get slack installed and ensure you've got access to lab-skene, and lab-neurogenomics. Email Nathan if there's any issue.
Signup to present at the next available lab meeting (ensure it's after you've had a couple of months in the lab at least): https://docs.google.com/spreadsheets/d/1z77H6UniPF1fkcf5XXO985LEDilTMhm3mSpZXzK3GSA/edit#gid=0
Create a Github account if you don't already have one: send me your username so I can add you to the group's project space (https://github.com/neurogenomics). Try to do all your work within a Github repository. Regardless of whether you will be working in the wet or dry lab, we expect you to use github for project management. This video explains how we do so (https://www.youtube.com/watch?v=zTNDo4PGTds&t=1s), please watch and take into consideration.
Join the lab's mailing list: https://groups.google.com/forum/#!forum/neurogenomics-lab
Read about our reason's for supporting open science and what you can do to support it.
Take a look at the Useful Info page. There may be something there you want to know!
If you are new to computing/programming and will be working here on bioinformatics related projects then register as soon as possible for:
- the Imperial's High Performance Computing course
- if Imperial has any software carpentry courses running soon, sign up for those
- look into whether any software carpentry courses are happening nearby soon
If you are experienced with programming, learn about containerisation with docker/singularity and writing workflows with nextflow/WDL/snakemake. If you have never made an R package, try making a simple test one and then installing it from github. If you're never used packrat/Renv, try using them.
If you are new to working with single cell, then try out the Broad Institute single cell tutorial or bioconductor's tutorial.
If you're new to using Git/Github then have a look through these course materials.
You should go through tutorials relating to the lab's core methods:
- EWCE: https://github.com/NathanSkene/EWCE/
- LD Score Regression: https://github.com/bulik/ldsc/wiki/Heritability-and-Genetic-Correlation
- Note, the links to GWAS sumstats may now be broken, but these can still be obtained from here: https://www.med.unc.edu/pgc/download-results/
- MAGMA_Celltyping: https://github.com/NathanSkene/MAGMA_Celltyping
If you are new to statistics (or stats in R) then there are two fundamental methods that you want to get used to using:
- Generalised linear models: go through this tutorial and google if it's not clear
- Bootsrapping. Look into how EWCE works.
I recommend using Obsidian to keep track of your work / notes. Try it if you haven't before. Keep notes on papers and books you have read, datasets that might be useful, computer errors etc in here. Here is a short article discussing how to use Obsidian. Have a read about Zettelkasten method's of note taking.
In the Uren building we use LabCup to manage equipment that's available. More expensive equipment that's available through the UKDRI is available on the UKDRI portal.
If you are working in the wetlab then you will be expected to maintain notes on Labstep.
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- Useful Info
- To do list for new starters
- Recommended Reading
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Computing
- Our Private Cloud System
- Cloud Computing
- Docker
- Creating a Bioconductor package
- PBS example scripts for the Imperial HPC
- HPC Issues list
- Nextflow
- Analysing TIP-seq data with the nf-core/cutandrun pipeline
- Shared tools on Imperial HPC
- VSCode
- Working with Google Cloud Platform
- Retrieving raw sequence data from the SRA
- Submitting read data to the European Nucleotide Archive
- R markdown
- Lab software
- Genetics
- Reproducibility
- The Lab Website
- Experimental
- Lab resources
- Administrative stuff