Nanguiano Week 3

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The Genetic Code, by Computer

Connect to the my.cs.lmu.edu workstation as shown in class and do the following exercises from there.

For this exercise, I performed the following series of commands to prepare for the assignment.

  • Log in to the server 
ssh my.cs.lmu.edu -l nanguia1
  • Create a folder for this class
mkdir biodb
  • Move into the biodb folder
cd biodb
  • Create a file with a sample DNA sequence in it
cat >"sequence_file.txt" 
agcggtatac
  • Make a directory for this week
mkdir week3
  • Move the DNA sequence file into the folder
mv sequence_file.txt week3/
  • Go into Dondi's directory
cd ~dondi/xmlpipedb/data
  • Copy the genetic-code.sed file into my week 3 directory
cp genetic-code.sed ~nanguia1/biodb/week3
  • Copy the Match utlity into my week 3 directory
cp xmlpipedb-match-1.1.1.jar ~nanguia1/biodb/week3
  • Copy the file containing the P falciparum DNA sequence into my directory
cp 493.P_falciparum.xml ~nanguia1/biodb/week3
  • Copy the file containing the DNA from Chromosome 19 into my directory
cp hs_ref_GRCh37_chr19.fa ~nanguia1/biodb/week3
  • Return to my directory
cd ~nanguia1/biodb/week3

Complement of a Strand

Write a sequence of piped text processing commands that, when given a nucleotide sequence, returns its complementary strand.

On a sequence_file.txt file containing the sequence "agcggtatac", the command and output was as follows: 

cat sequence_file.txt | sed "y/atgc/tacg/"
tcgccatatg

Reading Frames

Write 6 sets of text processing commands that, when given a nucleotide sequence, returns the resulting amino acid sequence, one for each possible reading frame for the nucleotide sequence. You should have 6 different sets of commands, one for each possible reading frame.

I experienced considerable difficulty getting to this work at first. After some trial and error, and with the help of Dondi, I finally realized that my problems were coming from the fact that I was not converting t to u. After performing the conversion, my commands worked just as expected.

On a sequence_file.txt containing the sequence "agcggtatac", the command and output was as follows:

+1 

cat sequence_file.txt | sed "s/.../& /g" | sed "s/t/u/g" | sed -f genetic-code.sed | sed "s/ //g" | sed "s/[acgu]//g"
SGI

+2 

cat sequence_file.txt | sed "s/^.//g" | sed "s/.../& /g" | sed "s/t/u/g" | sed -f genetic-code.sed | sed "s/ //g" | sed "s/[acgu]//g"
AVY

+3 

cat sequence_file.txt | sed "s/^..//g" | sed "s/.../& /g" | sed "s/t/u/g" | sed -f genetic-code.sed | sed "s/ //g" | sed "s/[acgu]//g"
RY

The remaining three were divided onto two lines on this wiki because they could not fit onto one without causing graphical bugs. The actual command was written without newlines.

-1 

cat sequence_file.txt | sed "y/acgt/tgca/" | rev | sed "s/.../& /g" | sed "s/t/u/g" | sed -f genetic-code.sed | 
sed "s/ //g" | sed "s/[acgu]//g"
VYR

-2 

cat sequence_file.txt | sed "y/acgt/tgca/" | rev | sed "s/^.//g" | sed "s/.../& /g" | sed "s/t/u/g" | sed -f genetic-code.sed | 
sed "s/ //g" | sed "s/[acgu]//g"
YTA

-3 

cat sequence_file.txt | sed "y/acgt/tgca/" | rev | sed "s/^..//g" | sed "s/.../& /g" | sed "s/t/u/g" | sed -f genetic-code.sed | 
sed "s/ //g" | sed "s/[acgu]//g"
IP

Check Your Work

Utilizing the ExPASy Translate Tool, I inputted my sample dna sequence, "agcggtatac". The result was as follows:

NAW3TranslationTest.png

XMLPipeDB Match Practice

For your convenience, the XMLPipeDB Match Utility (xmlpipedb-match-1.1.1.jar) has been installed in the ~dondi/xmlpipedb/data directory alongside the other practice files. Use this utility to answer the following questions:

Note: I used this wiki page to learn about the match utility.

  1. What Match command tallies the occurrences of the pattern GO:000[567] in the 493.P_falciparum.xml file?
    • java -jar xmlpipedb-match-1.1.1.jar GO:000[567] < 493.P_falciparum.xml
    • How many unique matches are there?
      • 3
    • How many times does each unique match appear?
      • GO:007 : 113
      • GO:006 : 1100
      • GO:008 : 1371
  2. Try to find one such occurrence “in situ” within that file. Look at the neighboring content around that occurrence.
    • One example was: <dbReference type="GO" id="GO:0005622">
    • Describe how you did this.
      • grep "GO:000[567]" 493.P_falciparum.xml | more
    • Based on where you find this occurrence, what kind of information does this pattern represent?
      • Based on where I found it, this pattern shows the gene ontology ID of a particular gene in the database.
  3. What Match command tallies the occurrences of the pattern \"Yu.*\" in the 493.P_falciparum.xml file?
    • java -jar xmlpipedb-match-1.1.1.jar \"Yu.*\" < 493.P_falciparum.xml
    • How many unique matches are there?
      • 3
    • How many times does each unique match appear?
      • "Yu b." : 1
      • "Yu k." : 228
      • "Yu m." : 1
    • What information do you think this pattern represents?
      • I believe this pattern represents a name.
      • This was confirmed by running the command grep "Yu.*" 493.P_falciparum.xml
  4. Use Match to count the occurrences of the pattern ATG in the hs_ref_GRCh37_chr19.fa file (this may take a while). Then, use grep and wc to do the same thing.
    • What answer does Match give you?
      • java -jar xmlpipedb-match-1.1.1.jar ATG < hs_ref_GRCh37_chr19.fa
      • Total unique matches: 1
      • Number of matches: 830101
    • What answer does grep + wc give you?
      • grep "ATG" hs_ref_GRCh37_chr19.fa | wc
      • Lines: 502410
      • Words: 502410
      • Characters: 35671048
    • Explain why the counts are different. (Hint: Make sure you understand what exactly is being counted by each approach.)
      • Match is searching for exactly those three letters and the number of times that they appear throughout the entire file. So ATG appears 830,101 times in the file. Grep, on the other hand, searches for the lines that contain that pattern ATG. ATG appears at least once on 502,410 lines, and because each of the lines it appeared in had no spaces, it counts each line as a word. This results in 502,410 words. The number of characters that consist of those 502,410 lines is 35,671,048.
      • To illustrate that this is the case, I ran a few experiments. Running grep -v "ATG" hs_ref_GRCh37_chr19.fa | wc returns this output:
        • Lines: 299182
        • Words: 299244
        • Characters: 21242050
      • Running wc hs_ref_GRCh37_chr19.fa returns the following output:
        • Lines: 801592
        • Words: 801654
        • Characters: 56913098
      • Adding up the results of the grep "ATG" hs_ref_GRCh37_chr19.fa | wc and grep -v "ATG" hs_ref_GRCh37_chr19.fa | wc gives the following result, illustrating that my theory on what grep is counting was correct:
        • Lines: 801492
        • Words: 801654
        • Characters: 56913098

Links

Nicole Anguiano
BIOL 367, Fall 2015

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