Lab 5 - Multiple Alignment and Gene Prediction

Hand in: Note results (copy and paste where appropriate) and answer questions in your lab report, attach to an email to burhansd@canisius.edu

• Ex. 1
  Answer the following questions in your report, use the Internet or your book to help with the answers.
  
  1. What is the purpose of creating a multiple alignment of sequences?
  2. Would it make more sense in terms of biology to align nucleotide or protein sequences? Why?
  3. Protein alignments are usually scored using a special alignment matrix, often from the PAM or the BLOSUM families. Explain briefly what these matrices are and in particular why PAM250 and BLOSUM62 are the most commonly used such matrices.
• Ex. 2
  Bacteriocins are peptides or proteins, produced by a bacterium. Bacteriocins kill or inhibit other closely related bacteria.

  Below is shown the amino acid sequence for an `unknown' bacteriocin (carnobacteriocin A9b) from lactic acid bacteria, and five sequences for some `known' (published) bacteriocins. Note the X's which stand for `not determined'. The `unknown' bacteriocin kills Listeria monocytogenes, which is a human-pathogenic, food-borne bacterium. The illness listeriosis attacks people that have a weak immune system, as well as foetuses (by infecting the bearer of the foetus) and the mortality rate is approximately 25 per cent. In Denmark, approximately 40 people are affected by listeriosis per year.

  > carnobacteriocin A9b
  V N Y G N G V S X X K K X X
  > V1a piscicocin
  K Y Y G N G V S C N K N G C
  > V1b piscicocin
  A I S Y G N G V Y C N K E K C
  > bacteriocin B2
  V N Y G N G V S C S K T K C
  > leucocin A
  K Y Y G N G V H C T K S G C
  > mesentericin Y105
  K Y Y G N G V H C T K S G C
  

  Copy and paste these sequences given above into the ClustalW multiple alignment website http://www.ebi.ac.uk/clustalw/. Note that you should read about ClustalW so that you can understand your output. Make sure to view your output using JalView, the Java Applet graphical viewer, as well as to look at the other output files. What are the default parameters used for computing this alignment? How might they be changed, and what would you expect from changing them (discuss a couple of scenarios).

  The protein family has a name. What do you think it is? Find out by searching SwissProt using protein Blast (link to BLAST web site) on one of the known sequences. If you search the nr (non-redundant) database instead, you may need to increase the Expect value to 1000 or 10000 to make Blast less fastidious, otherwise you will get no hits. This is because the nr database is much larger than Swissprot, and the query sequences are very short.

• Ex. 3
  Carboxypeptidases (carboxypepsidases) are enzymes that cut the carboxyl terminus of peptides. In humans, they are secreted by the pancreas to aid digestion.

  The file carboxypep.fasta contains 18 sequences for carboxypepsidases from humans, cows, rats, pigs, etc. Download the file and align the sequences using ClustalW.

  How well conserved are the sequences? Are there any sequences that seem to be outliers (more distantly related)? Try removing the outliers by copying and editing the file and then redo the alignment. How does the new alignment compare to the original?

• Ex. 4
  T-Coffee is another multiple alignment program.

  The file Chloroperoxidase.fasta contains eight sequences for proteins that are chloroperoxidases, or related to chloroperoxidases.

  Use ClustalW to find a multiple alignment for these sequences. Because some sequences are short and others very long. What does the output indicate in terms of a common motif among the sequences? Are the different sequence lengths problematic?

  Now use T-COFFEE instead of ClustalW to make a multiple aligment of the same eight sequences. You can run T-COFFEE at T-COFFEE. Select the Advanced option under T-Coffee (first set of choices). Inspect the HTML version of the alignment. How does this ouput compare to the ClustalW output?

  Did either or both programs find the P[AS]YPSGHAT motif without problems? Explain your answer/include your data.

• Ex. 5
  Gene Prediction - chapter 6 covers this fairly extensively (latter part of ch 6), also check online for help in answering questions.
  1. Most statistical gene prediction programs require a set of parameters, estimated based on a training set of DNA sequences with genes clearly marked. What are the two major experimental methods used to reliably find a gene?
  2. A single nucleotide substitution at which position in a codon would most likely have the greatest impact on the function of the encoded protein: the first, the second, or the third? Why?
  3. Which of the following of point mutations would most likely have the greatest impact on the function of the encoded protein: a single nucleotide substitution mutation (i.e. A mutates to G) or a single nucleotide deletion (i.e. A is deleted from the sequence)? Why?
  4. Although the genetic code is universal, organisms usually have their own preference for codon usage. For example, the web site http://psyche.uthct.edu/shaun/SBlack/codonuse.html gives statistics on the codon usage of Escherichia coli. Your colleague has an EST fragment from E. coli with the following sequence: AAGUCAUUAUUUUCG.

     Assuming this is the coding strand, can you help her to identify the most likely translation frame?

  5. There are two general strategies for performing gene prediction: similarity based approaches and statistics-based approaches. Explain which genes are likely to be missed by the statistics-based approach and by the similarity-based approach.
  6. Sequence homology or similarity information is used in both the similarity based and the comparative genomics approaches for gene prediction. What is the difference between these two approaches?
  7. What is a pseudogene? Why gene prediction algorithms have difficulty to discern pseudogenes from true genes?
  8. Genscan is one of the best gene finding algorithms. The UCSC Genome Browser http://genome.ucsc.edu is a convenient graphic visualization tool for genome annotations. In this practical exercise you should use sequence from the human genome genomic_dna1.fa. Your task is to use the gene prediction program Genscan (Genscan) to find potential genes in this genomic sequence, and validate your findings against the annotations using the UCSC browser. Note that you will need to scroll down the Genscan results page to see all of the information, including the coding sequence from the DNA.
     1. Run Genscan. Go to the Genscan web site, submit your sequence, be sure to check "Print predicted coding sequences (-cds)" in the print options so that you can get the predicted cds, and wait for the result.
     2. Display the genomic regions on UCSC genome browser. Note that the chromosome number and region are in the given data file on the first line. You can display the predicted cds on the UCSC genome browser. In order to display the exon structure in the predicted cds, you will need to use the BLAT program by Jim Kent, which can quickly look for a sequence in the human genome and return the genomic regions with high similarity to your query sequence. Go to the UCSC genome browser home page, select BLAT and human genome, paste your predicted cds and submit. You will be brought to the actual display window of the genome browser where your sequence from BLAT search is displayed together with several other annotation tracks. You can play around the browser as it integrates a lot of information. Click on your sequence from BLAT search track to see the actual base-by-base display. Make sure you can recognize the signals (Start codon, Stop codon, splicing signals, etc.)
     3. Compare your predictions against the annotation for known genes and predictions by other gene-finding algorithms. Compare your predictions against the known genes track. If the known genes track is not shown, you can display it by using the drop down controls under the browser window. Analyze prediction result for your sequence:
        • What is the performance at the exon level? i.e., how many exons are predicted? How many are missed? How accurate are the predicted exon boundaries?
        • Genscan gives a probability score for each predicted exon. Are the probability values for exons predicted by Genscan informative?
        • What is the accuracy at the nucleotide level? i.e., how many nucleotides in the coding regions are correctly predicted? You can go to the browser home page, select Table Browser and select the knownGene table to extract the exact exon locations for the known genes.
        • What is the gene from the sequence? Did Genscan successfully predict this gene? How are the results compared to the Genscan gene track in the browser?

Some of the exercses have been taken and modified following websites, thanks to the original authors:
http://www.bioalgorithms.info/practical-problems.php
http://www.matfys.kvl.dk/bioinformatik/exercise-multiple.html