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Created Feb 19, 2020
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tdc stderr, target psms, decoy psms
PSMId score q-value posterior_error_prob peptide proteinIds
Percolator version 3.02.0, Build Date Feb 2 2018 16:57:41
Copyright (c) 2006-9 University of Washington. All rights reserved.
Written by Lukas Käll (lukall@u.washington.edu) in the
Department of Genome Sciences at the University of Washington.
Issued command:
percolator -Y -P -decoy_ -m target.tdc.txt -M decoy.tdc.txt -s -t 0.05 make-pin.pin
Started Thu Feb 20 00:32:27 2020
Hyperparameters: selectionFdr=0.01, Cpos=0, Cneg=0, maxNiter=10
Reading tab-delimited input from datafile make-pin.pin
Features:
lnrSp deltLCn deltCn XCorr Sp IonFrac PepLen Charge1 enzN enzC enzInt lnNumDSP dM absdM
Found 210 PSMs
Separate target and decoy search inputs detected, using target-decoy competition on Percolator scores.
Train/test set contains 105 positives and 105 negatives, size ratio=1 and pi0=1
Selecting Cpos by cross-validation.
Selecting Cneg by cross-validation.
Split 1: Selected feature 12 as initial direction. Could separate 70 training set positives with q<0.01 in that direction.
Split 2: Selected feature 12 as initial direction. Could separate 70 training set positives with q<0.01 in that direction.
Split 3: Selected feature 12 as initial direction. Could separate 70 training set positives with q<0.01 in that direction.
Found 105 test set positives with q<0.05 in initial direction
Reading in data and feature calculation took 0.003579 cpu seconds or 0 seconds wall clock time.
---Training with Cpos selected by cross validation, Cneg selected by cross validation, initial_fdr=0.01, fdr=0.01
Iteration 1: Estimated 105 PSMs with q<0.05
Iteration 2: Estimated 105 PSMs with q<0.05
Iteration 3: Estimated 105 PSMs with q<0.05
Iteration 4: Estimated 105 PSMs with q<0.05
Iteration 5: Estimated 105 PSMs with q<0.05
Iteration 6: Estimated 105 PSMs with q<0.05
Iteration 7: Estimated 105 PSMs with q<0.05
Iteration 8: Estimated 105 PSMs with q<0.05
Iteration 9: Estimated 105 PSMs with q<0.05
Iteration 10: Estimated 105 PSMs with q<0.05
Learned normalized SVM weights for the 3 cross-validation splits:
Split1 Split2 Split3 FeatureName
0.0111 0.0047 0.0033 lnrSp
0.0205 0.0049 0.0053 deltLCn
-0.0021 -0.0012 0.0001 deltCn
0.0001 0.0040 -0.0008 XCorr
0.0000 0.0000 0.0000 Sp
0.0000 0.0000 0.0000 IonFrac
0.0038 0.0026 0.0037 PepLen
0.0000 0.0000 0.0000 Charge1
0.0000 0.0000 0.0000 enzN
0.0000 0.0000 0.0000 enzC
-0.0030 0.0002 0.0009 enzInt
-0.9588 -0.9616 -0.9617 lnNumDSP
-0.0008 -0.0027 -0.0026 dM
-0.0022 -0.0030 -0.0033 absdM
-0.0291 -0.0297 -0.0298 m0
Found 105 test set PSMs with q<0.05.
Selected best-scoring PSM per scan+expMass (target-decoy competition): 21 target PSMs and 0 decoy PSMs.
Tossing out "redundant" PSMs keeping only the best scoring PSM for each unique peptide.
Calculating q values.
Final list yields 0 target peptides with q<0.05.
Calculating posterior error probabilities (PEPs).
Processing took 0.1709 cpu seconds or 0 seconds wall clock time.
PSMId score q-value posterior_error_prob peptide proteinIds
target_0_20_1_2 0 0.111111 6.30512e-16 K.Q[-16.04]QQRPGLSSK.V AAK49468.1
target_0_8_1_1 0 0.111111 6.30512e-16 R.RGQPWVLTR.Y AAK49467.1
target_0_12_1_5 -0.000516181 0.111111 6.30512e-16 R.N[0.98]GASYFAAAQ[-16.04]K.R ALJ92831.1
target_0_4_1_2 -0.000764098 0.111111 6.30512e-16 K.RGPQ[-16.04]ETVADR.R AAK49465.1
target_0_1_1_2 -0.00106162 0.111111 6.30512e-16 K.LVQGLLAVTAK.Q AAK49470.1
target_0_17_1_3 -0.00262305 0.111111 6.30512e-16 K.KAAKIAAAGLAK.N AAK49479.1
target_0_16_1_1 -0.00272363 0.111111 6.30512e-16 K.EES[79.97]LEDLAK.G AAK49472.1
target_0_13_1_5 -0.00334685 0.111111 6.30512e-16 K.RN[0.98]T[79.97]EAVGKR.- QEG99559.1
target_0_9_1_4 -0.016052 0.111111 6.30512e-16 K.VAGTGDN[0.98]Q[-16.04]HTK.I AAK49470.1
PSMId score q-value posterior_error_prob peptide proteinIds
target_0_20_1_2 0 0.047619 6.30512e-16 K.Q[-16.04]QQRPGLSSK.V AAK49468.1
target_0_8_1_1 0 0.047619 6.30512e-16 R.RGQPWVLTR.Y AAK49467.1
target_0_2_1_1 0 0.047619 6.30512e-16 R.RGQPWVLTR.Y AAK49467.1
target_0_12_1_5 -0.000516181 0.047619 6.30512e-16 R.N[0.98]GASYFAAAQ[-16.04]K.R ALJ92831.1
target_0_4_1_2 -0.000764098 0.047619 6.30512e-16 K.RGPQ[-16.04]ETVADR.R AAK49465.1
target_0_7_1_1 -0.000910821 0.047619 6.30512e-16 R.RGQPWVLTR.Y AAK49467.1
target_0_11_1_1 -0.000961745 0.047619 6.30512e-16 R.RGQPWVLTR.Y AAK49467.1
target_0_1_1_2 -0.00106162 0.047619 6.30512e-16 K.LVQGLLAVTAK.Q AAK49470.1
target_0_10_1_1 -0.00106718 0.047619 6.30512e-16 R.RGQPWVLTR.Y AAK49467.1
target_0_14_1_1 -0.00123665 0.047619 6.30512e-16 R.RGQPWVLTR.Y AAK49467.1
target_0_17_1_3 -0.00262305 0.047619 6.30512e-16 K.KAAKIAAAGLAK.N AAK49479.1
target_0_16_1_1 -0.00272363 0.047619 6.30512e-16 K.EES[79.97]LEDLAK.G AAK49472.1
target_0_21_1_5 -0.00298902 0.047619 6.30512e-16 K.LVQGLLAVTAK.Q AAK49470.1
target_0_13_1_5 -0.00334685 0.047619 6.30512e-16 K.RN[0.98]T[79.97]EAVGKR.- QEG99559.1
target_0_3_1_1 -0.00422443 0.047619 6.30512e-16 K.EES[79.97]LEDLAK.G AAK49472.1
target_0_18_1_1 -0.00467679 0.047619 6.30512e-16 K.EES[79.97]LEDLAK.G AAK49472.1
target_0_15_1_2 -0.00559309 0.047619 6.30512e-16 K.EES[79.97]LEDLAK.G AAK49472.1
target_0_5_1_1 -0.0103525 0.047619 6.30512e-16 R.RGQPWVLTR.Y AAK49467.1
target_0_6_1_1 -0.0107748 0.047619 6.30512e-16 K.RN[0.98]T[79.97]EAVGKR.- QEG99559.1
target_0_19_1_1 -0.011423 0.047619 6.30512e-16 R.RGQPWVLTR.Y AAK49467.1
target_0_9_1_4 -0.016052 0.047619 6.30512e-16 K.VAGTGDN[0.98]Q[-16.04]HTK.I AAK49470.1
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