ProDBWiki/DeveloperDocumentation/reduceSearches: Difference between revisions
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== Idea == | == Idea == | ||
To get good results with a mascot search, you choose many different search parameters. For example more than one database, 0 - 5 or more missed cleavage sites, a wide spectra for the peptide tolerance and small steps (50- | To get good results with a mascot search, you choose many different search parameters. For example more than one database, 0 - 5 or more missed cleavage sites, a wide spectra for the peptide tolerance and small steps (50-100ppm in 10ppm steps). With every parameter more, the number of searches increases. Rapidly you have 100 or more searches for one scan. And if you want to analyse more than one scan, you can wait very long for your results. | ||
If you choose the ''Reduce Searches'', you can decrease the number of searches. This function tries which parameters give the best results and minimizes the searches. At first it only iterates over the missed cleavage sites and saves the best one. Then it iterates over the peptide tolerance (the missed cleavage site is always the best one) and saves the best combination from missed cleavage site and peptide tolerance. For all other searches these parameters are used and only the remaining parameters are iterated. By the use of this tool, you can reduce the number of searches to less than 10. | If you choose the ''Reduce Searches'', you can decrease the number of searches. This function tries which parameters give the best results and minimizes the searches. At first it only iterates over the missed cleavage sites and saves the best one. Then it iterates over the peptide tolerance (the missed cleavage site is always the best one) and saves the best combination from missed cleavage site and peptide tolerance. For all other searches these parameters are used and only the remaining parameters are iterated. By the use of this tool, you can reduce the number of searches to less than 10. | ||
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== What Did I Do == | == What Did I Do == | ||
I add two functions to ''msanalysis.cgi'': '''create_searchp_reduce''' and '''reduce_searches'''. Both functions are called from msanalysis.cgi, after checking if ''Reduce Searches'' was selected in the search from. The first function ''create_searchp_reduce'' sorts the searchParameters in a special way, so that they can be used for the second function ''reduce_searches''. All searchParams are stored in the hash %sortSearchDB, this hash has the different selected databases as keys and is returned. %sortSearchDB contains hashes (%sortSearchP) which have the different peptide tolerances as keys. If the key from %sortSearchDB is database A, the hash %sortSearchP contains only search parameters with database A as attribute. %sortSearchP also contains hashes (%sortSearchC) which have different missed cleavage site as keys. %sortSearchC contains only an array (@sortC) with all search parameters with the same database (key from %sortSearchDB), same peptide tolerance (key from %sortSearchP) and same missed cleavage site (key from %sortSearchC). The search parameters are sorted in the order: DB -> PeptidToleranz -> MissedCleavageSites. | |||
For example: You have database DB A and DB B, peptidetolerance 50, 75, 100ppm and missed cleavage sites 1, 2, 3 and different fixed and variable modifications. Than you get the following hash %sortSearchDB (SP = search parameter, DB = database, P = peptide tolerance, C = missed cleavage): | |||
<pre><nowiki> | |||
line Key contains Key contains Key contains contains | |||
1 sortSearchDB: DB A sortSearchP: 50 sortSearchC: 1 @sortC: SP with: DB A, P 50, C 1, and different fixed and variable modifications | |||
2 2 @sortC: SP with: DB A, P 50, C 2, and different fixed and variable modifications | |||
3 3 @sortC: SP with: DB A, P 50, C 3, and different fixed and variable modifications | |||
4 75 sortSearchC: 1 @sortC: SP with: DB A, P 75, C 1, and different fixed and variable modifications | |||
5 2 @sortC: SP with: DB A, P 75, C 2, and different fixed and variable modifications | |||
6 3 @sortC: SP with: DB A, P 75, C 3, and different fixed and variable modifications | |||
7 100 sortSearchC: 1 @sortC: SP with: DB A, P 100, C 1, and different fixed and variable modifications | |||
8 2 @sortC: SP with: DB A, P 100, C 2, and different fixed and variable modifications | |||
9 3 @sortC: SP with: DB A, P 100, C 3, and different fixed and variable modifications | |||
10 DB B sortSearchP: 50 sortSearchC: 1 @sortC: SP with: DB B, P 50, C 1, and different fixed and variable modifications | |||
11 2 @sortC: SP with: DB B, P 50, C 2, and different fixed and variable modifications | |||
12 3 @sortC: SP with: DB B, P 50, C 3, and different fixed and variable modifications | |||
13 75 sortSearchC: 1 @sortC: SP with: DB B, P 75, C 1, and different fixed and variable modifications | |||
14 2 @sortC: SP with: DB B, P 75, C 2, and different fixed and variable modifications | |||
15 3 @sortC: SP with: DB B, P 75, C 3, and different fixed and variable modifications | |||
16 100 sortSearchC: 1 @sortC: SP with: DB B, P 100, C 1, and different fixed and variable modifications | |||
17 2 @sortC: SP with: DB B, P 100, C 2, and different fixed and variable modifications | |||
18 3 @sortC: SP with: DB B, P 100, C 3, and different fixed and variable modifications | |||
</nowiki></pre> | |||
The second function ''reduce_searches'' gets the hash from the previous function as parameter. And is responsible for recducing the number of searches. First the best Score for a missed cleavage site is searched, therefore only the search parameters are used which have the same attributes and only the number of missed cleavage sites is iterated, the score for the searches are compared and the cleavage site Cbest with the best score is saved (for example: as search parameters are used the first three rows from the example above (DB A, P 50, C is iterated). After that, the best Peptid Tolerance Pbest is selected with the same procedure, only, that they use the best missed cleavage site and don't iterate that parameter anymore (for example: the best C is 2, now we use the lines 5 and 8, if one of the scores is bigger than the score from Cbest, the peptide tolerance from that search is set as Pbest otherwise we take the peptide tolerance from Cbest (line 2)). Than the different databases are iterated with the Cbest and Pbest (for example: Pbest 75 we search with the parameters from line 14). At last all other attributes are iterated, but they always have the best cleavage site (Cbest) and the best tolerance (Pbest) as attributes. | |||
Author: Nicole de la Chaux | Author: Nicole de la Chaux | ||
Latest revision as of 07:17, 26 October 2011
Reduce Searches
Idea
To get good results with a mascot search, you choose many different search parameters. For example more than one database, 0 - 5 or more missed cleavage sites, a wide spectra for the peptide tolerance and small steps (50-100ppm in 10ppm steps). With every parameter more, the number of searches increases. Rapidly you have 100 or more searches for one scan. And if you want to analyse more than one scan, you can wait very long for your results.
If you choose the Reduce Searches, you can decrease the number of searches. This function tries which parameters give the best results and minimizes the searches. At first it only iterates over the missed cleavage sites and saves the best one. Then it iterates over the peptide tolerance (the missed cleavage site is always the best one) and saves the best combination from missed cleavage site and peptide tolerance. For all other searches these parameters are used and only the remaining parameters are iterated. By the use of this tool, you can reduce the number of searches to less than 10.
How to use
To use this feature select the field Reduce Searches from the dropdown menu Search for mixture. All other parameters can be chosen as normal. Click on Search to submit the search. After all searches are finished, you can click on Results to get the results. Be aware that not all searches are generated only a few with the best parameters (so you can see only the results for these).
What Did I Do
I add two functions to msanalysis.cgi: create_searchp_reduce and reduce_searches. Both functions are called from msanalysis.cgi, after checking if Reduce Searches was selected in the search from. The first function create_searchp_reduce sorts the searchParameters in a special way, so that they can be used for the second function reduce_searches. All searchParams are stored in the hash %sortSearchDB, this hash has the different selected databases as keys and is returned. %sortSearchDB contains hashes (%sortSearchP) which have the different peptide tolerances as keys. If the key from %sortSearchDB is database A, the hash %sortSearchP contains only search parameters with database A as attribute. %sortSearchP also contains hashes (%sortSearchC) which have different missed cleavage site as keys. %sortSearchC contains only an array (@sortC) with all search parameters with the same database (key from %sortSearchDB), same peptide tolerance (key from %sortSearchP) and same missed cleavage site (key from %sortSearchC). The search parameters are sorted in the order: DB -> PeptidToleranz -> MissedCleavageSites. For example: You have database DB A and DB B, peptidetolerance 50, 75, 100ppm and missed cleavage sites 1, 2, 3 and different fixed and variable modifications. Than you get the following hash %sortSearchDB (SP = search parameter, DB = database, P = peptide tolerance, C = missed cleavage):
line Key contains Key contains Key contains contains 1 sortSearchDB: DB A sortSearchP: 50 sortSearchC: 1 @sortC: SP with: DB A, P 50, C 1, and different fixed and variable modifications 2 2 @sortC: SP with: DB A, P 50, C 2, and different fixed and variable modifications 3 3 @sortC: SP with: DB A, P 50, C 3, and different fixed and variable modifications 4 75 sortSearchC: 1 @sortC: SP with: DB A, P 75, C 1, and different fixed and variable modifications 5 2 @sortC: SP with: DB A, P 75, C 2, and different fixed and variable modifications 6 3 @sortC: SP with: DB A, P 75, C 3, and different fixed and variable modifications 7 100 sortSearchC: 1 @sortC: SP with: DB A, P 100, C 1, and different fixed and variable modifications 8 2 @sortC: SP with: DB A, P 100, C 2, and different fixed and variable modifications 9 3 @sortC: SP with: DB A, P 100, C 3, and different fixed and variable modifications 10 DB B sortSearchP: 50 sortSearchC: 1 @sortC: SP with: DB B, P 50, C 1, and different fixed and variable modifications 11 2 @sortC: SP with: DB B, P 50, C 2, and different fixed and variable modifications 12 3 @sortC: SP with: DB B, P 50, C 3, and different fixed and variable modifications 13 75 sortSearchC: 1 @sortC: SP with: DB B, P 75, C 1, and different fixed and variable modifications 14 2 @sortC: SP with: DB B, P 75, C 2, and different fixed and variable modifications 15 3 @sortC: SP with: DB B, P 75, C 3, and different fixed and variable modifications 16 100 sortSearchC: 1 @sortC: SP with: DB B, P 100, C 1, and different fixed and variable modifications 17 2 @sortC: SP with: DB B, P 100, C 2, and different fixed and variable modifications 18 3 @sortC: SP with: DB B, P 100, C 3, and different fixed and variable modifications
The second function reduce_searches gets the hash from the previous function as parameter. And is responsible for recducing the number of searches. First the best Score for a missed cleavage site is searched, therefore only the search parameters are used which have the same attributes and only the number of missed cleavage sites is iterated, the score for the searches are compared and the cleavage site Cbest with the best score is saved (for example: as search parameters are used the first three rows from the example above (DB A, P 50, C is iterated). After that, the best Peptid Tolerance Pbest is selected with the same procedure, only, that they use the best missed cleavage site and don't iterate that parameter anymore (for example: the best C is 2, now we use the lines 5 and 8, if one of the scores is bigger than the score from Cbest, the peptide tolerance from that search is set as Pbest otherwise we take the peptide tolerance from Cbest (line 2)). Than the different databases are iterated with the Cbest and Pbest (for example: Pbest 75 we search with the parameters from line 14). At last all other attributes are iterated, but they always have the best cleavage site (Cbest) and the best tolerance (Pbest) as attributes.
Author: Nicole de la Chaux