Hi, I have used Mayu integrated in TPP to analyze my data.
The log file of Mayu showed that there is no decoy proteins found, and the
log file is in the attachment.
I generated my database for searching with other tools than the "
reverse_fasta.pl" tool in the Mayu, and set the parameter "Tag (prefix) for
decoy proteins" as "_REVERSED".
I am wondering that the decoy proteins in my database fasta is
tagged(Suffix) "_REVERSED", has this any effect on Mayu performance?
Any suggestion will be welcomed.
Thanks.
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" C:\Inetpub\tpp-bin\Mayu.pl 1.07
initial DS cutoff: 0
status fdr: 0.01
INPUT:
using xml parser: 0
-A pepxml input: c:/Inetpub/wwwroot/ISB/data/noiRT/TZ.interact.ipro.pep.xml
-B table input:
-C search database:
c:/Inetpub/wwwroot/ISB/data/database/Uniprot_MOUSE_iRT_20130626_concatenated_target_decoy.fasta
-D minimal peptide length: 0
TARGET-DECOY OPTIONS:
-E decoy id prefix: _REVERSED
-F target to decoy ratio: 1
-G maximal PSM FDR for analysis: 0.01
-H PSM FDR steps: 11
PROTEIN FDR CALCULATION:
-I number of missed cleavages: 2
-J minimal peptide mass: 400
-K maximal peptide mass: 6000
-L number of protein size bins: 10
-N id set selection type: 0
all data and mFDR range (default)
don't correct identical sequences: 0
use equidist protein binning: 0
POST ANALYSIS:
run R analysis: 0
OUTPUT:
file name base: c:/Inetpub/wwwroot/ISB/data/noiRT/Mayu
print mFDR file: 1
print protein size bin file: 0
print protein feature file: 0
print input pepxml to csv: 0
print cumulative input to stdout: 0
print status: 0
------------------------------------
protein size
------------------------------------
parsing
c:/Inetpub/wwwroot/ISB/data/database/Uniprot_MOUSE_iRT_20130626_concatenated_target_decoy.fasta...
checking decoy ids for mirror target ids... ok
62 total identical sequences, 32 sequences corrected to ''
101960 protein groups...
estimating protein sizes...
getting bins for the proteins...
copying target entries to decoy entries...
------------------------------------
input PSM (mFDR)
------------------------------------
Found IprophetProbability. Using this instead of PeptideProphetProbability.
Caveat: Do not mix iprophet and peptideprophet files, there is no check!
TZ.interact.ipro.pep.xml parsed
0.00000% of PSM removed (length < 0 aa)
non FDR filtered target: 332199 PSM, 71473 peptides, 24469 proteins
non FDR filtered decoy: 0 PSM, 0 peptides, 0 proteins
0.00000% of PSM removed (mFDR <= 0.01)
target: 332199 PSM, 71473 peptides, 24469 proteins at 0.01 FDR
decoy: 0 PSM, 0 peptides, 0 proteins at 0.01 FDR
cumulative input data:
target: 332199 PSM, 71473 peptides, 24469 proteins at 0.01 FDR
decoy: 0 PSM, 0 peptides, 0 proteins at 0.01 FDR
------------------------------------
protein identification FDR (protFDR)
------------------------------------
registered error models:
PSMFDR, PeptideIdFDR, ProteinIdFDR, LocalProteinIdFDR
DATA SELECTION SCHEME:
complete data set -> set of mFDR
36 total runs, looping through id sets...
------------------------------------
printing files
------------------------------------
main output files:
c:/Inetpub/wwwroot/ISB/data/noiRT/Mayu_main_1.07.csv,
c:/Inetpub/wwwroot/ISB/data/noiRT/Mayu_main_1.07.txt
321 seconds run time"
