HWbash

Lecture on Perl, Lecture on command-line tools

• In this set of tasks, use command-line tools or one-liners in Perl, awk or sed. Do not write any scripts or programs.
• Each task can be split into several stages and intermediate files written to disk, but you can also use pipelines to reduce the number of temporary files.
• Your commands should work also for other input files with the same format (do not try to generalize them too much, but also do not use very specific properties of a particular input, such as the number of lines etc.)

Preparatory steps and submitting

# create a folder for this homework
mkdir bash
# move to the new folder
cd bash
# link input files to the current folder
ln -s /tasks/bash/known.fa /tasks/bash/yarLip.fa /tasks/bash/matches.tsv names.tsv .
# copy protocol to the current folder
cp -i /tasks/bash/protocol.txt .

• Now you can open protocol.txt in your favorite editor and start working
• Command ln created symbolic links to the input files, so you can use them under names such as known.fa rather than full paths such as /tasks/bash/known.fa.

When you are done, you can submit all required files as follows (substitute your username):

cp -ipv protocol.txt /submit/bash/your_username

# check what was submitted
ls -l /submit/bash/your_username

Introduction to tasks A-C

• In these tasks we will again process bioinformatics data. We have two files of sequences in the FASTA format. This time the sequences represent proteins, not DNA, and therefore they use 20 different letters representing different amino acids. Lines starting with '>' contain the identifier of a protein and potentially an additional description. This is followed by the sequence of this protein, which will not be needed in this task. This data comes from the Uniprot database.
• File /tasks/bash/yarLip.fa is a FASTA file with proteins from the yeast Yarrowia lipolytica. Each protein is identified in the FASTA file only by its identifier such as Q6CFX1.
• File /tasks/bash/known.fa is a FASTA file with proteins from several yeast species. Each identifier is followed by a description of the biological function of the protein.
• These two sets of proteins were compared by a bioinformatics tool called BLAST which finds proteins or their parts with similar sequences. The results of BLAST are in file /tasks/bash/matches.tsv. This file contains a section for each protein from yarLip.fa. This section starts with several comments, i.e. lines starting with # symbol.

This is followed by a table with the found matches in the TSV format, i.e., several values delimited by tab characters \t. We will be most interested in the first two columns representing the IDs proteins from yarLip.fa and from known.fa, respectively.

Task A (counting proteins)

Steps (1) and (2)

• Use files known.fa and yarLip.fa to find out how many proteins are in each. Each protein starts with a line starting with the > symbol, so it is sufficient to count those.
• Beware that > symbol means redirect in bash. Therefore you have to enclose it in single quotation marks '>' so that it is taken literally.
• For each file write a single command or a pipeline of several commands that will produce the number with the answer. Write the commands and the resulting protein counts to the appropriate sections of your protocol.

Step 3

• Create file known.tsv which contains sequence IDs and descriptions extracted from known.fa
• Leading > should be removed. Any text after OS= in the description should be also removed.
• This file should be sorted alphabetically.
• The file should end as follows:

tr|Q5AQ78|Q5AQ78_CANAL Uncharacterized protein 
tr|Q5AQ79|Q5AQ79_CANAL Potential SET3 histone deacetylase complex component 
tr|Q5AQ80|Q5AQ80_CANAL Potential SET3 histone deacetylase complex component 
tr|Q5AQ81|Q5AQ81_CANAL Uncharacterized protein 
tr|Q9P3E3|Q9P3E3_SCHPO NAD-dependent malic enzyme (Predicted), partial (Fragment) 

• Submit file known.tsv, write your command(s) to the protocol.

Task B (counting matches)

Step (1)

• From file matches.tsv extract pairs of similar proteins and store them in file pairs.txt.
• Each line of the file should contain a pair of protein IDs extracted from the first two columns of the matches.tsv file.
• These IDs should be separated by a single space and the file should be sorted alphabetically.
• Do not forget to omit lines with comments.
• Each pair from the input should be listed only once in the output.
• Commands grep, sort and uniq would be helpful. To select only some columns, you can use cut, awk or a perl one-liner.
• The file pairs.txt should have 66622 lines (command wc) and it should start as follows:

B5FVA8 sp|O13857|PLB2_SCHPO
B5FVA8 sp|P39105|PLB1_YEAST
B5FVA8 sp|P53541|SPO1_YEAST

• Submit file pairs.txt and write your commands to the protocol.

Step (2)

• Find out how many proteins from yarLip.fa have at least one similarity found in matches.tsv. This can be done by counting distinct values in the first column of your pairs.txt file from step (1).
• Write your answer and commands to the protocol. Compare this number with the total number of proteins from yarLip.fa found in Task A(2).
• We suggest commands cut/awk/perl, sort, uniq, wc
• The result of your commands should be an output consisting of a single number (and the end-of-line character).

Step (3)

• For each protein in the first column of pairs.txt file count how many times it occurs in the file. The result should be a file named frequency.txt with pairs protein ID, count separated by space, sorted from the proteins with the highest to the lowest count.
• To check you answer, look at lines 49 and 50 of the file as follows head -n 50 frequency.txt | tail -n 2
• You should get:

Q6CG49 247
Q6C078 245

• Submit file frequency.txt, write your commands to the protocol. Also write to the protocol what is the highest and lowest count in the second column of your file.
• Note: these counts say for proteins from yarLip.fa how many proteins from known.fa are similar to them. The highest count is actually caused by setting in the BLAST algorithm producing the file matches.tsv. Proteins with zero matches are not listed in your file but their number could be deduced from your results in step (2) and Task A(2).

Task C (joining information)

• Get the first (strongest) match for each query from matches.tsv.
• This can be done by printing the lines that are not comments but follow a comment line starting with #.
• In a Perl one-liner, you can create a state variable which will remember if the previous line was a comment and based on that you decide if you print the current line.
• Instead of using Perl, you can play with grep. Option -A 1 prints the matching lines as well as one line after each match
• Print only the first two columns separated by tab (name of query, name of target), sort the file by the second column.
• Store the result in file best.tsv. The file should start as follows:

Q6CBS2  sp|B5BP46|YP52_SCHPO
Q6C8R4  sp|B5BP48|YP54_SCHPO
Q6CG80  sp|B5BP48|YP54_SCHPO
Q6CH56  sp|B5BP48|YP54_SCHPO

• Submit file best.tsv with the result

Step 2:

• Create file known.tsv which contains sequence names extracted from known.fa with leading > removed
• This file should be sorted alphabetically.
• The file should start as follows (lines are trimmed below):

sp|A0A023PXA5|YA19A_YEAST Putative uncharacterized protein YAL019W-A OS=Saccharomyces...
sp|A0A023PXB0|YA019_YEAST Putative uncharacterized protein YAR019W-A OS=Saccharomyces...

• Submit file known.tsv

Step 3:

• For each Y.lipolytica protein, we have found similar proteins from other yeasts
• Now we want to extract for each protein in Y.lipolytica its closest match among all known proteins and see what is its function. This will give a clue about the potential function of the Y.lipolytica protein.

• Use command join to join the files best.tsv and known.tsv so that each line of best.tsv is extended with the text describing the corresponding target in known.tsv
• Use option -1 2 to use the second column of best.tsv as a key for joining
• The output of join may look as follows:

sp|B5BP46|YP52_SCHPO Q6CBS2 Putative glutathione S-transferase C1183.02 OS=Schizosaccharomyces...
sp|B5BP48|YP54_SCHPO Q6C8R4 Putative alpha-ketoglutarate-dependent sulfonate dioxygenase OS=...

• Further reformat the output so that the query name goes first (e.g. Q6CBS2), followed by target name (e.g. sp|B5BP46|YP52_SCHPO), followed by the rest of the text, but remove all text after OS=
• Sort by query name, store as best.txt
• The output should start as follows:

B5FVA8  tr|Q5A7D5|Q5A7D5_CANAL  Lysophospholipase
B5FVB0  sp|O74810|UBC1_SCHPO    Ubiquitin-conjugating enzyme E2 1
B5FVB1  sp|O13877|RPAB5_SCHPO   DNA-directed RNA polymerases I, II, and III subunit RPABC5

• Submit file best.txt

Note:

• Not all Y.lipolytica proteins are necessarily included in your final output (some proteins do not have blast match).
  • You can think how to find the list of such proteins, but this is not part of the task.
• Files best.txt and best.tsv should have the same number of lines.

Task D (passwords)

• The file /tasks/bash/names.txt contains data about several people, one per line.
• Each line consists of given name(s), surname and email separated by spaces.
• Each person can have multiple given names (at least 1), but exactly one surname and one email. Email is always of the form username@uniba.sk.
• The task is to generate file passwords.csv which contains a randomly generated password for each of these users
  • The output file has columns separated by commas ','
  • The first column contains username extracted from email address, the second column surname, the third column all given names and the fourth column the randomly generated password
• Submit file passwords.csv with the result of your commands.

Example line from input:

Pavol Orszagh Hviezdoslav hviezdoslav32@uniba.sk

Example line from output (password will differ):

hviezdoslav32,Hviezdoslav,Pavol Orszagh,3T3Pu3un

Hints:

• Passwords can be generated using pwgen (e.g. pwgen -N 10 -1 prints 10 passwords, one per line)
• We also recommend using perl, wc, paste (check option -d in paste)
• In Perl, function pop may be useful for manipulating @F and function join for connecting strings with a separator.