SILACtor relies on 3rd party software, but don't worry, you probably already have it in your lab. If not, it can be found online for free. Here is the list of software you will need:
Step #1: Process your data
Step #2: Set up SILACtor
SILACtor can be configured for any SILAC-labeled amino acids in your experiment. Use the S tags to indicate the expected
mass shifts. You can specify more than one mass shift to accomodate multiple labeled amino acids, or multiple labels per peptide.
Below is an example for a typical Lys-6, Arg-8 data set, and includes combinations of KK, RR, and KR resulting from missed cleavage:
S 6.0201324 S 8.0142036 S 12.0402648 S 14.034336 S 16.0284072
A list of peptide sequences are needed to provide identification of observed SILAC features from the precursor data. There are two
ways to import the data, with the recommended way being to provide an AMT database appropriate for your data acquisition. To do so, use
the DBA tag:
The AMT database must be tab-delimited text with the following columns:
"protein" can be any character string designation for your proteins up to 32 characters. "peptide" is a peptide sequence up to 64 characters. "charge" is the integer charge state of the peptide identification. "mass" is the monoisotopic, zero charge mass value in Daltons for the identified peptide. "firstRT" is the lower limit of retention time (in minutes) over which the peptide is observed. "lastRT" is the upper limit of retention time (in minutes) over which the peptide is observed.
Alternatively, you can have SILACtor build an AMT database for you (currently Mascot users only). Export your Mascot data to a comma
separated value file (.CSV) and provide that file, plus the original raw file with the DB tag:
DB ..\080410_HeLa_KT_0hrs_01.csv ..\080410_HeLa_KT_0hrs_01.RAW DB ..\080410_HeLa_KT_0hrs_02.csv ..\080410_HeLa_KT_0hrs_02.RAW DB ..\080410_HeLa_KT_1hrs_01.csv ..\080410_HeLa_KT_1hrs_01.RAW DB ..\080410_HeLa_KT_1hrs_02.csv ..\080410_HeLa_KT_1hrs_02.RAW
Parameters that alter the behavior of SILACtor are specified with the P tag. There are five parameters to specify, each with a specific
name. Here are examples of each:
P RTime 10.0 120.0 P DBMinFileCount 6 P DBMinEventCount 1 P DBRTTolerance 10.0 P CorrThreshold 0.90
RTime limits the analysis of SILACtor to just the chromatographic regions specified (in minutes). In the example above, only data obtained between 10 and 120 minutes is used to exclude the loading and column equilibration phases of the data acquisition.
DBMinFileCount is only used if building your own AMT library with the DB tags. Here, the parameter requires the peptides to be observed in 6 of the files that go into building the database.
DBMinEventCount is only used if building your own AMT library with the DB tags. Here, the parameter requires the peptides to be observed just once in each the database search result file to be considered valid.
DBRTTolerance is only used if building your own AMT library with the DB tags. This parameter removes bad peptide IDs, defined as those whose lower and upper retention time boundaries exceed a logical limit. Here, the limit is 10 minutes.
CorrThreshold is the minimum correlation required between any two peptide extracted ion chromatograms to be considered a light-heavy pair if they differ by the expected mass. The use of a correlation reduces the liklihood of spurrious pairings between different peptide signals by requiring both extracted ion chromatograms to have a similar elution profile as judged using Pearson's correlation coefficient. (Setting this to 0, effectively turns off the validation).
Precursor information is included in the analysis using the F tags. Valid files are the Hardklör results files. Each file must be assigned a number.
No two files can be assigned the same number. You must also specify an output file for the SILAC results and a time label for timepoint analysis (arbitrary units).
F 1 ..\080410_HeLa_KT_0hrs_01.hk KT-0hrs-01.txt 0.0 F 2 ..\080410_HeLa_KT_0hrs_02.hk KT-0hrs-02.txt 0.0 F 3 ..\080410_HeLa_KT_1hrs_01.hk KT-1hrs-01.txt 1.0 F 4 ..\080410_HeLa_KT_1hrs_02.hk KT-1hrs-02.txt 1.0
In the sample above, four files are analyzed, two replicates from timepoint 0, and two replicates from time point 1.
Replicate timepoints should be combined into a single set for more robust data analysis. This is done using the R tag. Each replicate analysis must be assigned
a number, and these numbers must be different than the F line numbers. Also, indicate an output file for the combined results, an output file for a targeted mass list,
and the file numbers from the F tags that you wish to combine (tab-delimited):
R 5 KT-0hrs.txt KTlist-0hr.txt 1 2 R 6 KT-1hrs.txt KTlist-1hr.txt 3 4
In the example above, we combined the two replicates at time point 0 (indicated with the F tag numbers 1 and 2) and output the results to KT-0hrs.txt. Also a mass and time targeted inclusion list was generated for follow up analysis of the most interesting peptides that did not receive a sequence ID. This replicate analysis was assigned the number 5. Likewise, a replicate analysis for time point 1 was performed and assinged the number 6. To generate a timepoint analysis, the T tag is used. It is also assinged a number that was not used previously in the F or R lines. An output file must be specified and indicate the file numbers or replicate numbers to be included (tab-delimited). Note that you can use file numbers from the F lines or replicate numbers from the R lines if you have replicates:
T 7 KT-timepoints.txt 5 6
In the above example, the replicates at time point 0 (number 5) and time point 1 (number 6) are used to make a time point analysis that is output to KT-timepoints.txt.
Step #3: Running SILACtor
where silactorConf.txt can be whatever you name your configuration file.
SILACtor will run, outputting messages to your console indicating its progress. Depending on the size of your experiment, typical run times are anywhere from 5 minutes to an hour.