• Removed all motifs where both similar drugs bind to the target.
• 30,231 motifs were originally identified, now 27,499 (-2,732).

TypeB

• Removed motifs where the drug binds to both targets.
• 22,864 motifs were orignally identified, now 19,185 (-3,679).

TypeC

• Removed motifs where protein(1) inv_in disease(2) or protein(2) inv_in disease(1).
• 70,866 motifs were orignally identified in the dataset, now 70,715 (-151).
• Tried: Remove motifs where the target connects to protein(2), this did not take into account that some links are has_similar_sequence/structure and not is_a. 200 (-70,515).
• Removed motifs with target is_a protein2, made no difference to total.

Ondex

• Selecting a node with a certain id in Ondex?
• Too hard when there is 500k nodes
• Search doesn’t work
• Ondex Console: how to use this??? -> help is unavailable surprisingly.

This high number is due to the multiple disease involved with Protein(1) and multiple disease involved with Protein(2). Each of these will be seperate motif to the count.

• Do not count motifs where Protein(2) is known to interact with Disease(1)//Protein(1) amd Disease(2) (Not Interested) — Only cut the results by ~200 70,510
• Remove motifs where Target interacts with Protein(1) and Protein(2). — Cut the result to 200 motifs.
• Only 200, as I only checked if Target was a connected to Protein(2), but there are more than just “is_a” that can connect these nodes such as “h_s_s”.

log

Exported the datasets ready for using with software (motif finder) that searches for motifs that are overrepresented. (Hypergeometric tests: 1day work). Cytoscape has a plugin.

• ib_2010_data.1.xml.gz –> .dot (50mb)
• ib_2010_data.1.xml.gz –> graph htm??? (cant remeber but it was 450mb)

Motif Type C

Trying to define another motif, struggling… similar targets?

B

Plan

Results/Code/Other program (June-July 2010)

4 Weeks

• Week 1 – Code/Motif Finder (i.e. Motif A,B,C… x,y,z instances)
• Week 2 – Java/Score/FoundExamples
• Week 3 – Score
• Week 4 – Score

Paper (July-August 2010)

• Intro
• Background
• Methods

- Definitions of S.M.
- Searching
- Scoring

• Results
  • TypeA
  • TypeB
  • TypeC
• Discussion
  • Future work
• Abstract

Module Webpage

• Removed deprecated stuff
• Added some comments

Also fixed the name servers.

Example output from Java (cutoff dataset):

Code was ran on the updated dataset:

• There were 70,866 instances of this motif definition in the dataset.
• However, this doesn’t address the issue of the OMIM database. So many of these will not have true “Diseases”. This would probably be best addressed when trying to score this type, if its even possible to do this.

• Drug 1 – Chlorpromazine – ID:120943
• Drug 2 – Trimeprazine – ID:120836
• Target – Histamine H1 Receptor – ID:715

I then traversed the output of the java code in Ondex Mini, and sucessfully found the motif:

Definition of a more complex example

• Started an implementation of a more complex motif i.e. look for similar proteins.
• Developed it using the chart created on a previous post.
• Seems to be working okay, although there are some issues with the Disease output and when to increment the motif counter/add to the motif Set.

It would be a good idea to cross check this with Chlorpromazine, but I need to get Linux up and running properly in a dual-boot due to memory issues in Windows (also not being able to print concept names is a problem).

Wrote a toString() method for a nicer output for testing purposes:

Drug1 <--> Drug2 --> Target

I wanted the Concepts name for example Chlorpromazine:

Chlorpromazine <--> Trimeprazine --> Histamine H1 Receptor

However , the ondex API is not working for printing names? – so using getId()’s for now.

Output

Here is some current output of the basic semantic motif definition.

Differences

This new implementation is different to the previously developed code by Cockell, S.J. for the 2010 paper. However, in the cutoff_data.xml.gz 157 motifs were identified, the same as the using the other method. However, there is a difference when using the code on the larger dataset ib2010_data.xml.gz. Previously there were 26,693 motifs identified compared with 29,633 using the new implementation, a difference of 2,940.

Note

It should also be reminded that it was discovered in the proper dataset:

• Drugs seem to have the ConceptClass “Comp:Compound“.
• Rather than the ConceptClass “Compound“.
• There are 70 of these, none of which seem to bind to a target.
• There are over 8k concepts of the former, so I will use this.

Potential solution (in progress) to this:-

Code appears to be working, traverses and prints out the information of the nodes (although not each indiviudal motif).