Difference between revisions of "Task alignments 2011"

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Most prediction methods are based on comparisons to related proteins. Therefore, the search for related sequences and the alignment to other proteins is a prerequisite for most of the analyses in this practical. Hence we will investigate the recall and alignment quality of different alignment methods.
 
Most prediction methods are based on comparisons to related proteins. Therefore, the search for related sequences and the alignment to other proteins is a prerequisite for most of the analyses in this practical. Hence we will investigate the recall and alignment quality of different alignment methods.
   
  +
== Theoretical background talks ==
 
The introductory talks shall give an overview of
 
The introductory talks shall give an overview of
 
* pairwise alignments and high-throuput profile searches (e.g. Fasta, Blast, PSI-Blast, HHsearch)
 
* pairwise alignments and high-throuput profile searches (e.g. Fasta, Blast, PSI-Blast, HHsearch)
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with special attention to advantages and limitations of theses methods.
 
with special attention to advantages and limitations of theses methods.
   
  +
== Sequence searches ==
 
Subsequently, for every native protein sequence for every disease the students shall employ different tools for database searching and multiple sequence alignment.
 
Subsequently, for every native protein sequence for every disease the students shall employ different tools for database searching and multiple sequence alignment.
 
The methods to employ (minimally) are:
 
The methods to employ (minimally) are:
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* check HSSP for "true positives" and see how many of these are found (-> recall)
 
* check HSSP for "true positives" and see how many of these are found (-> recall)
   
  +
== Multiple sequence alignments==
 
* Multiple sequence alignments of 20 sequences from your database search, including sequences from these ranges:
 
* Multiple sequence alignments of 20 sequences from your database search, including sequences from these ranges:
 
** 99 - 90% sequence identity
 
** 99 - 90% sequence identity
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*** use of 3D-Coffee
 
*** use of 3D-Coffee
   
Subsequently compare the alignments:
+
Comparison of the alignments:
 
* How many conserved columns?
 
* How many conserved columns?
 
* Are functionally important residues conserved?
 
* Are functionally important residues conserved?

Revision as of 13:47, 3 May 2011

Most prediction methods are based on comparisons to related proteins. Therefore, the search for related sequences and the alignment to other proteins is a prerequisite for most of the analyses in this practical. Hence we will investigate the recall and alignment quality of different alignment methods.

Theoretical background talks

The introductory talks shall give an overview of

  • pairwise alignments and high-throuput profile searches (e.g. Fasta, Blast, PSI-Blast, HHsearch)
  • multiple alignments (e.g. ClustalW, Probcons, Mafft, Muscle, T-Coffee, Cobalt) and MSA editors (e.g. Jalview)

with special attention to advantages and limitations of theses methods.

Sequence searches

Subsequently, for every native protein sequence for every disease the students shall employ different tools for database searching and multiple sequence alignment. The methods to employ (minimally) are:

  • Searches of the non-redundant sequence database:
    • Fasta
    • Blast
    • PSI-Blast using standard parameters with all combinations of
      • 3 iterations
      • 5 iterations
      • default E-value cutoff (0.005)
      • E-value cutoff 10E-6;
    • HHsearch

For evaluating the differences of the search methods:

  • compare the result lists (how much overlap, distribution of %identity and score)
  • check HSSP for "true positives" and see how many of these are found (-> recall)

Multiple sequence alignments

  • Multiple sequence alignments of 20 sequences from your database search, including sequences from these ranges:
    • 99 - 90% sequence identity
    • 89 - 60% sequence identity
    • 59 - 40% sequence identity
    • 39 - 20% sequence identity

Ideally there should be 5 sequences from each range with at least one pdb-structure in each range. The alignment methods to use are:

    • Cobalt
    • ClustalW
    • Muscle
    • T-Coffee with
      • default parameters ("t_coffee your_sequences.fasta)
      • use of 3D-Coffee

Comparison of the alignments:

  • How many conserved columns?
  • Are functionally important residues conserved?
  • How many gaps?
  • Are there gaps in secondary structure elements?