Daniel

Git submodules

9. February 2020 Daniel Comments 0 Comment

Today I noticed for the first time the concept of submodules in git. While cloning a repository from GitHub I noticed that one folder in it remained empty. After having a closer look, I noticed a reference to another repository tree like this:

Here, the folder htslib is actually from a tree in a different repository. After I cloned the repository like this (I forked it before):

git clone https://github.com/fischuu/SE-MEI.git

the folder htslib remained empty. That is because files from submodules are not fetched by default. This needs to be done separately by first initializing the submodules (first, cd into the cloned repository)

git submodule init

and then update the files from it

git submodule update

After these steps, the repository should be complete. However, instead of initializing the submodule separately, there is also a shortcut to fetch them all in one step by adding an additional parameter to the cloning like this:

git clone --recurse-submodules https://github.com/fischuu/SE-MEI.git

More details to git submodules can be found here.

Take a random sample of size k from paired-end FASTQ

10. December 2019 Daniel Comments 0 Comment

Today I wrote a bash script that creates a random subset of a paired-end FASTQ file pair. It requires the names of the two FASTQ-files as input and also the amount of reads that the sample should have.

The script is mainly based on this Blog post. This is a rather rough code and it could be more user-friendly and allow for more options, but in its current form, it does what I need it to do.

#!/bin/bash

round() {
    printf "%.2f" "$1"
}

file1=$1
file2=$2
sample=$3
# Input test
 if ! [[ $sample =~ ^-?[0-9]+([.][0-9]+)?$ ]]; then 
>&2 echo "$sample is not a number"; exit 1; 
fi
  
extension1="${file1##*.}"
extension2="${file2##*.}"
filename1="${file1%.*}"
filename2="${file2%.*}"

fn1=$filename1"_"$sample".fastq"
fn2=$filename2"_"$sample".fastq"

if [ $extension1 == "gz" ]; then
  gunzip $file1;
  file1=$filename1;
  filename1="${file1%.*}"
  fn1=$filename1"_"$sample".fastq"
fi
if [ $extension2 == "gz" ]; then
  gunzip $file2;
  file2=$filename2;
  filename2="${file2%.*}"
  fn2=$filename2"_"$sample".fastq"
fi

lines=$(wc -l < $file1)
echo $lines
echo $sample

if (( $(awk 'BEGIN {print ("'$sample'" <= 1)}') )); then
  sample=$(awk 'BEGIN {printf("%.0f", "'$sample'" * "'$lines'")}')
fi

echo $sample

paste $file1 $file1 | \
awk '{ printf("%s",$0); n++; if(n%4==0) { printf("\n");} else { printf("\t");} }' | \
awk -v k=$sample 'BEGIN{srand(systime() + PROCINFO["pid"]); }{ s=x++<k?x- 1:int(rand()*x);
                  if(s<k)R[s]=$0}END{for(i in R)print R[i]}' | \
awk -F"\t" -v file1=$fn1 -v file2=$fn2 '{print $1"\n"$3"\n"$5"\n"$7 > file1;\
                                         print $2"\n"$4"\n"$6"\n"$8 > file2}'
                                         
if [ $extension1 == "gz" ]; then
  gzip $fn1;
  gzip $file1;
fi
if [ $extension2 == "gz" ]; then
  gzip $fn2;
  gzip $file2;
fi

Adding an external HDD to fstab

3. October 2019 Daniel Comments 0 Comment

In order to permanently add an external HDD, the best way is to first identfy the UUID and the corresponding file system by typing

sudo blkid

with that information one can edit the fstab

sudo vim /etc/fstab

and enter then there a line with the format

UUID=<UUID> \tab <mountPoint> \tab <filesystem> \tab <options> \tab 0 \tab 1

Here, the values for <UUID> and <filesytem> we get from the blkid command, the mount point is ‘free choice’ and as option, I choose e.g. errors=remount-ro

Once the fstab is populated like this, just try to mount the disc by typing

sudo mount -a

Cancel all slurm jobs larger job ID X

30. September 2019 Daniel Comments 0 Comment

Sometimes it happens that we have running a whole bunch of slurm jobs from different projects, some of them are running already for days, while others are just fired – and then we noticed, damn, the 100 jobs that I just fired are wrong and they need to be canceled. Unfortunately, there is no slurm command that can do that, it requires some kind of scripting to do that.

The following script takes as an input a slurm job ID and cancels all jobs larger than that (that belong to the logged in user…).

#!/bin/bash

declare -a jobs=()

if [ -z "$1" ] ; then
    echo "Minimum Job Number argument is required.  Run as '$0 jobnum'"
    exit 1
fi

minjobnum="$1"

myself="$(id -u -n)"

for j in $(squeue --user="$myself" --noheader --format='%i') ; do
  if [ "$j" -gt "$minjobnum" ] ; then
    jobs+=($j)
  fi
done

scancel "${jobs[@]}"

If you store this e.g as killLarger.sh in your PATH somewhere, you can just use it from anywhere and cancel slurm jobs that are larger than this ID.

RumenPredict

27. September 2019 Daniel Comments 0 Comment

Predicting appropriate GHG mitigation strategies based on modelling variables that contribute to ruminant environmental impact.

Objective:

Ruminant production is responsible for ~ 9% of anthropogenic CO₂ emission and 37% of CH₄ emissions. Release of methane results in 6-12% less energy being available to the animal. Ruminants also contribute towards NO₂ within the environment, a persistent gas in the atmosphere which has 296 times more warming potential than CO₂. RumenPredict brings together members of the international Rumen Microbial Genomics network (led by IBERS, AU), of which the Hungate 1000 (focussed on sequencing 1000 rumen microbes) and the Rumen Census (focussed on evaluating effects of diet, host genetics and geographical location on the rumen microbiome) are projects within.

RumenPredict brings together key members of the RMG network to generate the necessary data to link rumen microbiome information to host genetics and phenotype and develop feed based mitigation strategies. This will enhance innovative capacity and allow integration of new knowledge with that previously generated to devise geographic and animal-specific solutions to reduce the environmental impact of livestock ruminants. The project members have access to recent data/tools resulting from an array of projects, and RumenPredict will build upon and enhance the integration of knowledge generated from these projects whilst providing innovation through further testing and validation of key hypotheses resulting from the previously obtained data. RumenPredict will provide a platform for predicting how host genetics, feed additives or microbiome may affect emission phenotypes and develop genetic/diet/prediction technologies further for implementation to improve nitrogen use efficiency whilst decreasing environmental impact of ruminants.