Posts

Minitalk: Understanding gene regulation in complex disease with deep sequencing

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Today I gave a presentation on experiment design and use of ChIP-seq and MBD-seq to understand gene regulation. The target audience consisted of biomedical scientists with little background in genomics but were curious to incorporate deep sequencing into their studies.

Link to the slides HERE.


As always I love getting feedback - so leave your questions and comments below!

Shell aliases for bioinformatics

Using shell allows us to take advantage of some nice features to make our bioinformatics lives a little easier for things we do very frequently. In Ubuntu, the ~/.bashrc file is run as a new terminal window is opened to customise the shell. Here are a few of my favourite general shortcuts. Let me know your favourites in the comments section below!

#shorten ls forms
alias ll='ls -alF'
alias la='ls -A'
alias l='ls -CF'


#shorten file viewing
alias h='head'
alias t='tail'
alias n='nano -S'
#the -S option to nano makes scrolling smoother
alias nano='nano -S'

#easy update
alias update='sudo apt-get update && sudo apt-get upgrade -y'

#search through history
alias hgrep='history | grep'

#Get col headers of tab delim file
ch(){
cat $1 | tr '\t' '\n' | nl -n ln
}
export -f ch

#login with ssh where IP is constant (X is the IP address)
alias login1='ssh -Y username@X.X.X.X' #scp can be done as above

#login with …

Minitalk: on Excel Gene Name Errors

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It was great to visit the Monash Clayton Bioinformatics team led by David Powell today to introduce myself and speak about a topic very close to my heart!

Slides below:

Also let me know what you think of the new theme of the blog in the comments below. BTW Just realised this is my 100th post! Yay for me! Thanks for reading!

How NGS is transforming medicine

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Last month, I gave a talk at our departmental meeting, describing in general terms how high throughput sequencing technology was having real impacts in medicine and human health, as well as some emerging trends to watch out for in coming years.

Here's the link


Introducing the ENCODE Gene Set Hub

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TL;DR We curated a bunch of ENCODE data into gene sets that is super useful in pathway analysis (ie GSEA).
Link to gene sets and data: https://sourceforge.net/projects/encodegenesethub/
Poster presentation: DOI:10.13140/RG.2.2.34302.59208

Now for the longer version. Gene sets are wonderful resources. We use them to do pathway level analyses and identify trends in data that lead us to improved interpretation and new hypotheses. Most pathway analysis tools like GSEA allow us to use custom gene sets, this is really cool as you can start to generate gene sets based on your own profiling work and that of others.

There is huge value in curating experimental data into gene sets, as the MSigDB team have demonstrated. But overall, these data are under-shared. Even our group is guilty of not sharing the gene sets we've used in papers. There have been a few papers where we've used gene sets curated  from ENCODE transcription factor binding site (TFBS) data to understand which TFs were drivi…

2016 wrap-up

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What a rollercoaster. On the good side, we've seen advances in sequencing methods including improvements in Nanopore sequencing and single cell methods becoming more common. We also saw 10x genomics come to the party with an emulsion PCR approach that can be applied to produce synthetic long reads or single cell barcoding. There were major results from larger cohort studies exemplified by ExAC, which is revealing more about human genetic variation, while Blueprint and GTEx are revealing more about determinants of gene regulation. The #openaccess is growing rapidly in the bioinformatics community, along with the growth of preprint popularity which I hope is adopted more widely in the biomedical sciences.

On the not so good side, Illumina has made made no new instrument announcements nor any substantial updates to existing sequencing systems. There were a few papers (example) describing the methylation EPIC array announced in 2015. Prices for Illumina reagents continue to increase,…

MSigDB gene sets for mouse

I recently needed to convert MSigDB gene sets to mouse so I thought I would share.

GO.v5.2.symbols_mouse.gmt
kegg.v5.2.symbols_mouse.gmt
msigdb.v5.2.symbols_mouse.gmt
reactome.v5.2.symbols_mouse.gmt

Below is the code used to do the conversion. It requires an input GMT file of human gene symbols as well as a human-mouse orthology file. You can download the ortholog file here. As the name suggests, it is based on data downloaded from Ensembl Biomart version 87.

Running the program converts all human gmt files. It requres gnu parallel which can be easily installed on Ubuntu with "sudo apt-get install parallel"


#!/bin/bash

conv(){
line=$1
  NAME_DESC=`echo $line | cut -d ' ' -f-2`

  GENES=`echo $line | cut -d ' ' -f3- \
  | tr ' ' '\n' | sort -uk 1b,1 \
  | join -1 1 -2 1 - \
  <(cut -f3,5 mouse2hum_biomart_ens87.txt \
  | sed 1d | awk '$1!="" && $2!=""' \
  | sort -uk 1b,1) | cut -d ' ' -f2 \
  | sort -u…