8000 segmentation fault in 3.06 · Issue #352 · percolator/percolator · GitHub
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timosachsenberg opened this issue Apr 20, 2023 · 7 comments 8000
Open

segmentation fault in 3.06 #352

timosachsenberg opened this issue Apr 20, 2023 · 7 comments

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@timosachsenberg
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timosachsenberg commented Apr 20, 2023 •
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Hi,
might be a user problem ;) but I get a segmentation fault while parsing this file: https://pastebin.com/02JyPmx5
Anything obvious that seems wrong?
Best,
Timo
@timosachsenberg timosachsenberg mentioned this issue Apr 27, 2023
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@lazear
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lazear commented Apr 27, 2023

I can reproduce this as well. I have attached lldb backtrace below

(lldb) thread backtrace
* thread #1, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x0)
  * frame #0: 0x00007ff81f5c6f56 libc++.1.dylib`std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >::basic_string(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&) + 4
    frame #1: 0x000000010006f501 percolator`TabFileValidator::LongestCommonSubsequence(std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > >) + 49
    frame #2: 0x000000010006fccd percolator`TabFileValidator::findDecoyPrefix(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, int, int) + 1117
    frame #3: 0x000000010006f044 percolator`TabFileValidator::detectDecoyPrefix(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >) + 132
    frame #4: 0x00000001000700b1 percolator`TabFileValidator::validateTabFiles(std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > >, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >*) + 97
    frame #5: 0x0000000100054b75 percolator`Caller::parseOptions(int, char**) + 39829
    frame #6: 0x0000000100003026 percolator`main + 54
    frame #7: 0x000000020057852e dyld`start + 462

@lazear
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lazear commented May 1, 2023

Unfortunately I have another bug to add here:

$ percolator -Y -Q perpxml.pepXML PRM/results.sage.pin
Protein decoy-preix used is rev_
All files have been read
Percolator version 3.06.0, Build Date May 11 2022 12:40:18
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 -Q perpxml.pepXML PRM/results.sage.pin
Started Mon May  1 11:57:57 2023
Hyperparameters: selectionFdr=0.01, Cpos=0, Cneg=0, maxNiter=10
Reading tab-delimited input from datafile PRM/results.sage.pin
Features:
rank z=2 z=3 z=4 z=5 z=6 z=other peptide_len missed_cleavages isotope_error ln(precursor_ppm) fragment_ppm ln(hyperscore) ln(delta_next) ln(delta_best) aligned_rt predicted_rt sqrt(delta_rt_model) matched_peaks longest_b longest_y longest_y_pct ln(matched_intensity_pct) scored_candidates ln(-poisson)
Reading line 1000000
Reading line 2000000
Reading line 3000000
Found 3813731 PSMs
Separate target and decoy search inputs detected, using target-decoy competition on Percolator scores.
Train/test set contains 2223402 positives and 1590329 negatives, size ratio=1.39808 and pi0=1
Selecting Cpos by cross-validation.
Selecting Cneg by cross-validation.
Split 1:        Selected feature 22 as initial direction. Could separate 185212 training set positives with q<0.01 in that direction.
Split 2:        Selected feature 22 as initial direction. Could separate 198115 training set positives with q<0.01 in that direction.
Split 3:        Selected feature 22 as initial direction. Could separate 196641 training set positives with q<0.01 in that direction.
Found 285004 test set positives with q<0.01 in initial direction
Reading in data and feature calculation took 79.7216 cpu seconds or 51 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 425222 PSMs with q<0.01
Iteration 2:    Estimated 467885 PSMs with q<0.01
Iteration 3:    Estimated 484517 PSMs with q<0.01
Iteration 4:    Estimated 490633 PSMs with q<0.01
Iteration 5:    Estimated 493783 PSMs with q<0.01
Iteration 6:    Estimated 495191 PSMs with q<0.01
Iteration 7:    Estimated 495905 PSMs with q<0.01
Iteration 8:    Estimated 496281 PSMs with q<0.01
Iteration 9:    Estimated 496420 PSMs with q<0.01
Iteration 10:   Estimated 496666 PSMs with q<0.01
Learned normalized SVM weights for the 3 cross-validation splits:
 Split1  Split2  Split3 FeatureName
 0.2429  0.2564  0.2521 rank
 0.6460  0.6395  0.6571 z=2
 0.1325  0.1371  0.1497 z=3
-0.6172 -0.6168 -0.6422 z=4
 0.0000  0.0000  0.0000 z=5
 0.0000  0.0000  0.0000 z=6
 0.0000  0.0000  0.0000 z=other
-0.3459 -0.3690 -0.3922 peptide_len
-0.4797 -0.4987 -0.5061 missed_cleavages
 0.0000  0.0000  0.0000 isotope_error
 0.0351  0.0597  0.0522 ln(precursor_ppm)
-0.6293 -0.6032 -0.6215 fragment_ppm
-0.4705 -0.4672 -0.4627 ln(hyperscore)
 0.3950  0.3722  0.3864 ln(delta_next)
 0.0000  0.0000  0.0000 ln(delta_best)
 0.1986  0.2010  0.1983 aligned_rt
 0.0191  0.0256 -0.0082 predicted_rt
-1.2531 -1.2226 -1.2402 sqrt(delta_rt_model)
-0.5627 -0.4961 -0.5292 matched_peaks
-0.1750 -0.1811 -0.1716 longest_b
 1.5856  1.5353  1.6293 longest_y
-0.2934 -0.2819 -0.3335 longest_y_pct
-0.0412 -0.0117 -0.0078 ln(matched_intensity_pct)
-0.2784 -0.2921 -0.2818 scored_candidates
 2.1085  2.0524  2.0562 ln(-poisson)
-4.6583 -4.5782 -4.7188 m0
Found 496250 test set PSMs with q<0.01.
Selected best-scoring PSM per scan+expMass (target-decoy competition): 186531 target PSMs and 87530 decoy PSMs.
Exception caught: basic_string::substr: __pos (which is 18446744073709551614) > this->size() (which is 1)

@MatthewThe
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Looks like the first problem could be caused by the fact that there are no decoy PSMs. I will create better error handling for this.

The second problem might be related, but could you share a minimum breaking example so we can do some tests?

@timosachsenberg
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timosachsenberg commented May 2, 2023 •
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Not 100% sure, but I think I had decoy PSMs in the larger file... @lazear did you reproduce this with a file that contains decoys?

Update: I ran it successfully with proper target/decoy information. So maybe my problem was indeed only related to missing decoys.

@lazear
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lazear commented May 2, 2023

First problem I reproduced with the example Timo provided.

Second example has decoys - let me try and generate an example on data I can share.

@lazear lazear mentioned this issue Dec 4, 2023
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@jordancurrie
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sage_test.zip

Adding an example here with decoys from the issue @lazear mentioned above.

@jordancurrie
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sage_test.zip

Adding an example here with decoys from the issue @lazear mentioned above.

Never mind! Was using an old version of crux percolator, so probably not relevant here after all.

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