Command Line Introduction: Excercises

Excercise 1: Basic File Operations

This is a test.

Tasks:

1. Change to your home directory

2. Create a file named ‘test.txt’ (and check if it is there)

3. Create a directory named ‘tutorial’

4. Copy ‘test.txt’ into the directory ‘tutorial’

5. Delete ‘test.txt’ (in your home)

6. Change to ‘tutorial’ and rename ‘test.txt’ to ‘file.txt’ and verify

7. Remove the directory ‘tutorial’ and its contents

Solution:

cd
touch test.txt
ls -lrt
mkdir tutorial
cp test.txt tutorial/
rm test.txt
cd tutorial
mv test.txt file.txt
ls
rm file.txt
cd ..
rmdir tutorial

Excercise 2: Links

Tasks:

1. change to /mnt/

2. Create a directory with the name “linux_intro” and give it to the user ubuntu

3. Go back to your home directory

4. Create a soft link called ‘linux_intro’ to /mnt/linux_intro

Note: this cannot be done using normal permission. Use sudo for operating with root privileges

Solution:

cd /mnt
sudo mkdir linux_intro
sudo chown ubuntu:ubuntu linux_intro
cd
ln -s /mnt/linux_intro

Excercise 3: Display File Content

Before you can do the next excercise, you need to donwload the sequencing data:

cd ~/linux_intro
wget https://openstack.cebitec.uni-bielefeld.de:8080/swift/v1/linuxcourse/seqs.fasta

Tasks:

1. Use head and tail to inspect the file

2. Print the first and last entry of the fasta file to the command line

3. Browse the file using less, search for start codons

Solution:

head seqs.fasta
tail seqs.fasta 

head -n 2 seqs.fasta
tail -n 2 seqs.fasta 

less seqs.fasta

Excercise 4: Wildcards

For the next excercise, we will donwload more sequencing data:

wget https://openstack.cebitec.uni-bielefeld.de:8080/swift/v1/linuxcourse/linuxdata.tar.gz
tar -zxvf linuxdata.tar.gz

Tasks:

1. List all tools in /usr/local/bin/ starting with ‘blast’

2. List all tools in /usr/local/bin/ starting with ‘blast’ followed by one additional character

3. List all tools in /usr/local/bin/ starting with ‘a’ or ‘b’ and ending with ‘c’ or ‘d’

4. Copy all sequence files from the directory linuxdata to the linux_intro directory (except seqs.fasta)

Solution:

ls /usr/local/bin/blast* 

ls /usr/local/bin/blast? 

ls /usr/local/bin/[ab]*[cd] 

cd ~/linux_intro
cp ~/linuxdata/sequences* ~/linux_intro/
cp ~/linuxdata/sequences_?.fasta ~/linux_intro/
cp ~/linuxdata/sequences_[1-4].fasta ~/linux_intro/
cp ~/linuxdata/sequences_{1..4}.fasta ~/linux_intro/

Excercise 5: grep and wc

Tasks:

1. Copy the Araport11_genes.gff from the previously uncompressed ‘linuxdata.tar.gz’-archive into your linux_intro

2. Inspect the file using less

3. How many lines does the file contain?

4. How many entries are there for Chromosome 1?

5. Find all entries related to ‘Auxin’

6. Use the command “grep” to find a file inside the “linuxdata” directory that contains the words “Romeo and Juliet”

Solutoin:

cd ~/linux_intro
cp ~/linuxdata/Araport11_genes.gff .
less Araport11_genes.gff
wc -l Araport11_genes.gff
grep -c "^Chr1" Araport11_genes.gff 

grep Auxin Araport11_genes.gff 

grep -r "Romeo und Juliet" ~/linuxdata/

Excercise 6: Streams

Tasks:

1. Use cat and wildcards to combine all sequence-files into a new file “sequences.fasta”

2. Use head and tail to get the second sequence from sequences.fasta

3. Use grep to store the sequence headers of sequences.fasta in a file

4. Use grep, head and tail to store headers 11-20 in a file

5. Append the headers 41-50 to the same (!) file

6. Also store the first 50 headers in a separate file. Do this in one command by using “tee” !

7. Use grep and wc to find out the number of bases in sequences.fasta

Solutoin:

cat sequences_[1-4].fasta > sequences.fasta 

head -n 4 | tail -n 2 sequences.fasta 

grep ">" sequences.fasta > headers.txt
grep ">" sequences.fasta | head -n 20 | tail -n 10 > headers_2.txt
grep ">" sequences.fasta | head -n 50 | tail -n 10 >> headers_2.txt
grep ">" sequences.fasta | head -n 50 | tee headers50.txt | tail -n 10 >> headers_2.txt 

grep -v ">" sequences.fasta | wc

Excercise 7: Tabular Data

Tasks:

1. How many features (CDS/mRNA/UTR…) are there for each type?

Hint: features are in row 3, sort and uniq might be useful

2. Create the same statistic for each chromosome

Hint: cut can select multiple columns

3. How many genes with a ‘kinase’ annotation are there per chromosome?

Solution:

cut -f 3 Araport11_genes.gff | sort | uniq -c | grep -v "#" 

cut -f 1,3 Araport11_genes.gff | sort | uniq -c | grep -v "##" 

grep kinase Araport11_genes.gff | cut -f 1,3 | grep gene | cut -f 1 | sort | uniq -c