Fundamentals of Sequence Analysis, 1998-1999
Problem set 3:  Databases and Database searching

If you get stuck, refer to the OpenVMS and GCG resources in the 
class home page.

 S.F. Altschul, W. Gish, W. Miller, E.W. Myers, and D.J. Lipman (1990)
  Basic Local Alignment Search Tool, J. Mol. Biol. 215:403-410

 S.F. Altschul, T.L. Madden, A.A. Schaffer, J. Zhang, Z. Zhang, and
  D.J. Lipman (1997) Gapped BLAST and PSI-BLAST: a new generation
  of protein database search programs. Nuc. Acids Res 25:3389-3402

 M. Gribskov, M. Homyak, J. Edenfield, D. Eisenberg (1988)
  Profile scanning for 3-dimensional structural patterns in protein     
  sequences. CABIOS  4(1):61-66

 W.R. Pearson and D.J. Lipman (1988) Improved Tools for biological
  sequence comparison, PNAS 85:2444-2448

 W.R. Pearson (1995) Comparison of methods for searching protein
   sequence databases. Prot. Science 4:1145-1160

 W.R. Pearson (1998) Empirical Statistical Estimates for Sequence 
  Similarity Searches. J. Mol. Biol. 276:71-84

Problem group 1.  Databases

Here is the recent history of the Genbank database:

Release    Date     Base Pairs   Entries

   106   Apr 98     1502542306    2209232
   107   Jun 98     1622041465    2355928
   108   Aug 98     1797137713    2532359
   109   Oct 98     2008761784    2837897
   110   Dec 98     2162067871    3043729

1.  Assuming that this growth rate is steady, estimate the size of the
next Genbank release (111), in base pairs.

Problem group 2.  Sequence database searching with query sequences

For this exercise use PEPCORRUPT to create these files derived from
Pir1:A1HU (which is 353 amino acids in length)

                    Substitutions   Indels  Average subs/residue
  A1HU_150.pep      525             0       1.5
  A1HU_175.pep      613             0       1.75
BLAST each of these against the Swiss Protein database using the default
settings. FASTA each of these against the local SwissProtein database
using a wordsize of 1, with the standard matrix (Dayhoff) and the Blosum62

This will take a while, so run it in a batch queue using, which contains:

$ blast_program="blastp"
$ blast_datalib="swissprot"
$ blast_descriptions=50
$ blast_alignments=0
$ blast2 a1hu_150.pep
$ blast2 a1hu_175.pep
$! Use Pearson's FASTA, not the GCG one
$ fasta3   "-QO" a1hu_150_fasta_2.out   -b 20 -d 1 a1hu_150.pep "c"
$ delete .;*
$ fasta3   "-QO" a1hu_175_fasta_2.out   -b 20 -d 1 a1hu_175.pep "c"
$ delete .;*
$ fasta3   "-QO" a1hu_150_fasta_1.out   -b 20 -d 1 a1hu_150.pep "c" 1
$ delete .;*
$ fasta3   "-QO" a1hu_175_fasta_1.out   -b 20 -d 1 a1hu_175.pep "c" 1
$ delete .;*

Use the following command to start the job only AFTER you verify
that you did, in fact, create the two .PEP files!!!

$ submit/log/noprint class:db_search_test

It may take a while for these calculations to complete - use 

   $ show entry 

to see what is still running.

2A.  Summarize the results.

A search through the EST database divisions found these six high
scoring regions: 

Gb_Est1:H25698  H25698 yl54g08.r1 Homo sapiens cDNA clone 162110 5'...  638.0
Gb_Est1:H39741  H39741 yo53d04.r1 Homo sapiens cDNA clone 181639 5'...  638.0
Gb_Est2:T28687  T28687 EST51971 Homo sapiens cDNA 5' end similar to...  635.0
Gb_Est1:H45310  H45310 yo65b01.r1 Homo sapiens cDNA clone 182761 5'...  624.0
Gb_Est1:H39698  H39698 yo52e05.r1 Homo sapiens cDNA clone 181568 5'...  613.0
Gb_Est2:R83490  R83490 yp15b06.r1 Homo sapiens cDNA clone 187475 5'...  613.0

Using A1HU as a query, search through these six EST sequence files with

  $ framesearch/infile1=pir1:a1hu/infile2=@class:est6.fil-

2B. How many of the six contain at least one frameshift?  What does that
suggest to you about EST sequences?

Problem group 3.  Patterns, Profiles, and  Databases

The PROSITE database is available in both pattern form (search with MOTIFS)
and profile form (search with PROFILESCAN).  Run both on the PIR1:A1HU
sequence and you will find a hit only against the "ig_mhc" motif, this will
be in the output .motifs and .scan files - you will need to search the 
latter for ig_mhc to find the hit.

3A.  Compare the results of the two methods.

3B.  Use FINDPATTERNS to search the SwissProtein database for
     the Ig_Mhc pattern:               (F,Y)xCx(V,A)xH
     Then use PROFILESEARCH with profiledir:ig_mhc.prf to search the same 
     database.  Use /BATCH on both.  What do you see?

Problem group 4.  Finding database entries by documentation

4A.  Use Entrez at to search the Protein databases for:
     Organism:    Drosophila
     All Fields:  Immunoglobulin
     All Fields:  Neural

    How many entries do you find?

4B.  Repeat the search on one of the SRS servers.  How many entries 
     did you find?