Quantitative mass spectrometry workflow. Currently supports proteomics experiments with complex experimental designs for DDA-LFQ, DDA-Isobaric and DIA-LFQ quantification.
Home Page: https://nf-co.re/quantms
License: MIT License
I run quantms v1.1.1 for a DDA LFQ experiment with the option "msstatslfq_removeFewMeasurements": false to keep features with 2 measurements but proteomicsLFQ removes them from the *_msstats_in.csv" file anyway. I want to keep features with 2 measurements since I have only 3 replicates per condition in this experiment.
The msstats.log (attached) says both that:
I tried to export the sdrf_openms_design_msstats_in_comparisons.csv into MSstats to ensure these features are not removed but noticed that proteomicsLFQ has already removed them.
data <- read.csv("proteomicslfq/sdrf_openms_design_msstats_in.csv", header = TRUE, sep = ',')
data <- data %>% filter(!grepl('CONTAMINANT_', ProteinName))
data_msstats <- OpenMStoMSstatsFormat(data, useUniquePeptide = TRUE, removeFewMeasurements=FALSE, removeProtein_with1Feature = FALSE, summaryforMultipleRows = max, use_log_file = FALSE)
data_msstats %>% group_by(PeptideSequence, PrecursorCharge) %>% summarise(RunCount = sum((Intensity))) %>% ungroup() %>% count(RunCount) %>% mutate(Frequency = n / sum(n))
RunCount n Frequency
1 3 106 0.0484
2 4 368 0.168
3 5 639 0.292
4 6 1077 0.492
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Nextflow version 22.10.6
Hardware Desktop
Executor local
Container engine: Docker
OS Linux
Version of nf-core/quantms 1.1.1
In the documentation we should explain when which score will be exported.
And maybe unify the scores in the sense that we should always perform an FDR calculation in PLFQ even if the cutoff is set to 1.0. (Currently calculation is omitted and actually 1.0 is fixed since max_psm_fdr does not seem to be passed to PLFQ).
This can be a bit confusing since then you might have some q-values from the per-file FDR calculation in there. Maybe we should also call the scores different then (e.g. per-file q-value, experiment-wide q-value).
Hello, I wanted to mention that this pipeline is missing the standard test configuration. It would be great to include a test profile since it is an important part of all nf-core pipelines and it would help users to test the pipeline before actually using it with their own data.
I then wanted to test the pipeline with two of the existing test profiles and there I encountered some errors.
nextflow run nf-core/quantms -profile test_dia,cfc --outdir results_diaThe test produced an output but I think in the msstates_comparisons.tsv the column names do not match with the actual column values. The column names might be in the wrong order. I attached the output tsv.
nextflow run nf-core/quantms -r 1.0 -profile test_lfq,cfc --outdir results_lfqThe test_lfq didn't run through and gave the following error report:
Thanks in advance :) !
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It runs LFQ:ID:DECOYDATABASE AND TMT:ID:DECOYDATABASE.
Why? @daichengxin
When SAGE is run after a failing execution, it give the following error:
Command wrapper:
ln: failed to create symbolic link '01524_A01_P015424_S00_N01_R1.mzML': File exists
Work dir:
/hps/nobackup/juan/pride/reanalysis/absolute-expression/tissues/PXD010154/work/51/e706bd1fd9ebdf269ed3c6d378a3c6
Tip: you can replicate the issue by changing to the process work dir and entering the command `bash .command.run
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There are a bunch of remaining tastks for post-release of 1.1.0:
nf-core bump-version 1.2.0dev?) #86This PR is inspired by: https://nf-co.re/docs/contributing/release_checklist#steps-to-release
Currently we are always converting to mzML although some tools allow reading raw files (e.g. DIANN and I think even Comet)
We only need to make sure that the mzml statistics module can also read raw files. There are Thermo raw parsing libraries for python out there.
Or we convert just for QC while DIANN is running and discard intermediate results.
Following advice from @jpfeuffer, I'm making the switch from the "nf-core/proteomicslfq" workflow to "nf-core/quantms" (dev version in both cases). I'm surprised to find that my "experimental_design.tsv" file that works for "proteomicslfq" doesn't work for "quantms". Contents of the file are included below.
If I name the file "experimental_design.tsv" and place it in the directory where I run the workflow, the file gets cleared (!) and I get the first error message (see below). The cause appears to be the sed command that attempts to change the file in-place.
If I give the file a different name (e.g. "experimental_design_test.tsv"), I get the second error message (see below).
$ nextflow run nf-core/quantms -r dev -profile docker --input experimental_design.tsv --database ../Human_reference_proteome_TD.fasta
[...]
Error executing process > 'NFCORE_QUANTMS:QUANTMS:CREATE_INPUT_CHANNEL:PREPROCESS_EXPDESIGN'
Caused by:
Process `NFCORE_QUANTMS:QUANTMS:CREATE_INPUT_CHANNEL:PREPROCESS_EXPDESIGN` terminated with an error exit status (1)
Command executed:
sed 's/.raw\t/.mzML\t/I' experimental_design.tsv > experimental_design.tsv
a=$(grep -n '^$' experimental_design.tsv | head -n1| awk -F":" '{print $1}'); sed -e ''"${a}"',$d' experimental_design.tsv > process_experimental_design.tsv
Command exit status:
1
Command output:
(empty)
$ nextflow run nf-core/quantms -r dev -profile docker --input experimental_design_test.tsv --database ../Human_reference_proteome_TD.fasta
[...]
Cannot invoke method contains() on null object
-- Check script '/home/hendrik.weisser/.nextflow/assets/nf-core/quantms/./workflows/../subworkflows/local/create_input_channel.nf' at line: 137 or see '.nextflow.log' file for more detailsexperimental_design.tsv:
Fraction_Group Fraction Spectra_Filepath Label Sample
1 1 /home/hendrik.weisser/Data/Proteomics/SST/mzML_files/20220223e_JR_SST_02.mzML 1 1
2 1 /home/hendrik.weisser/Data/Proteomics/SST/mzML_files/20220228a_JR_SST_02.mzML 1 2
3 1 /home/hendrik.weisser/Data/Proteomics/SST/mzML_files/20220301a_JR_SST_03.mzML 1 3
4 1 /home/hendrik.weisser/Data/Proteomics/SST/mzML_files/20220301a_JR_SST_05.mzML 1 4
Sample MSstats_Condition MSstats_BioReplicate
1 before 1
2 before 2
3 after 1
4 after 2
Nextflow version: 21.10.6
nf-core/quantms revision: 63dd266 [dev]
OS: Ubuntu 20.04.4 LTS
It would be great to have tests for all the quantms pipeline's Python code.
The automated tests would make the development much more enjoyable and have the benefit to speed up development.
There are a couple of considerations:
tests folder in ./bin/?Some example of tests for ./bin/diann_convert.py
"""Tests for test_diann_convert.py
"""
from diann_convert import calculate_coverage
def test_calculate_coverage_multiple_incomplete():
assert calculate_coverage("WATEROVERTHEDUCKSBACK", {"WATER", "DUCK"}) == 0.42857142857142855
def test_calculate_coverage_repeated_complete():
assert calculate_coverage("DUCKDUCKDUCK", {"DUCK"}) == 1.0
def test_calculate_coverage_incomplete():
assert calculate_coverage("WATEROVERTHEDUCK", {"DUCK"}) == 0.25
def test_calculate_coverage_overlap_partial_complete():
assert calculate_coverage("WATER", {"WAT", "TER"}) == 1.0
def test_calculate_coverage_overlap_partial_incomplete():
assert calculate_coverage("WATERGLASS", {"WAT", "TER"}) == 0.5NB: the tests require all dependencies of the scripts and hence can not be run on all infrastructure. e.g. there is no openms version for MacOS. So tests might have to be run in containers or some other solution would have to be found.
The commands in the documentation on nf-co.re and in this repository are wrong.
On the banner on the right-hand side of https://nf-co.re/quantms, it says this pipeline can be run with:
$ nextflow run nf-core/quantms -r 1.0 -profile test --outdir ../outdir
N E X T F L O W ~ version 22.10.4
Unknown configuration profile: 'test'
Did you mean one of these?
oistIn the README.md in this repository, it says it can be run with:
$ nextflow run nf-core/quantms -profile test,singularity --input project.sdrf.tsv --database protein.fasta --outdir ../outdir
N E X T F L O W ~ version 22.10.4
Project `nf-core/quantms` is currently stickied on revision: 1.0 -- you need to explicitly specify a revision with the option `-r` in order to use itPassing the -r makes it fail like the previous one.
Perhaps going forward the config should assign one of the test_* as test, which is what other workflows with multiple tests do.
To prevent breaking this pipeline in the near future, the nf-validation version should be pinned to version 1.1.3 like:
plugins {
id '[email protected]'
}
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I get the following error when running quant.ms using an sdrf.tsv file:
Error in setnames(x, value) :
Can't assign 4 names to a 0 column data.table
Calls: OpenMStoMSstatsFormat ... colnames<- -> names<- -> names<-.data.table -> setnames
Execution halted
Command error:
Loading required package: MSstats
Attaching package: ‘MSstats’
The following object is masked from ‘package:grDevices’:
savePlot
Loading required package: tibble
Loading required package: data.table
INFO [2023-09-01 12:20:06] ** Raw data from OpenMS imported successfully.
INFO [2023-09-01 12:20:06] ** Raw data from OpenMS cleaned successfully.
INFO [2023-09-01 12:20:06] ** Using annotation extracted from quantification data.
INFO [2023-09-01 12:20:06] ** Run labels were standardized to remove symbols such as '.' or '%'.
INFO [2023-09-01 12:20:06] ** The following options are used:
- Features will be defined by the columns: PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge
- Shared peptides will be removed.
- Proteins with a single feature will be removed.
- Features with less than 3 measurements across runs will be removed.
INFO [2023-09-01 12:20:06] ** Features with all missing measurements across runs are removed.
INFO [2023-09-01 12:20:06] ** Shared peptides are removed.
INFO [2023-09-01 12:20:07] ** Multiple measurements in a feature and a run are summarized by summaryforMultipleRows: max
INFO [2023-09-01 12:20:07] ** Features with one or two measurements across runs are removed.
INFO [2023-09-01 12:20:07] Proteins with a single feature are removed.
INFO [2023-09-01 12:20:07] ** Run annotation merged with quantification data.
INFO [2023-09-01 12:20:07] ** Multiple fractionations exist: 36 fractionations per MS replicate.
INFO [2023-09-01 12:20:27] ** Features with one or two measurements across runs are removed.
INFO [2023-09-01 12:20:27] ** Fractionation handled.
Error in setnames(x, value) :
Can't assign 4 names to a 0 column data.table
Calls: OpenMStoMSstatsFormat ... colnames<- -> names<- -> names<-.data.table -> setnames
Execution halted
Work dir:
/home/kcoetzer/nf/nf-core-quantms-1.1.1/workflow/work/76/86b9b6fe2a2b16cf1173bf95dc7275
Tip: you can try to figure out what's wrong by changing to the process work dir and showing the script file named .command.sh
Execution cancelled -- Finishing pending tasks before exit
NXF_VER=22.10.1 nextflow run main.nf --outdir /home/kcoetzer/nf/nf-core-quantms-1.1.1/ -profile singularity -params-file /home/kcoetzer/nfparams_quantms.json -resumePXD010154.sdrf_openms_design_msstats_in.csv
PXD010154.sdrf.csv
nfparams.txt
Hardware: local server
Container: singularity
Version of nf-core/quantms: 1.1.1
My plan is to use OpenMS' QCCalculator additionally in (almost) each step to create small mzQC files with additional summaries.
Those mzQC files should contain only stuff that cannot be read from the final mzTab.
This would also allow skipping the copying of the input mzMLs to the pmultiqc step since it just needs to read the already summarized data in the mzQC.
Please list places and metrics that we need to extract in the comments @ypriverol @timosachsenberg
MzMLs (run QCCalculator during mzML Indexing step?):
idXMLs (per Search engines):
idXMLs (after Perc/IDPEP):
idXMLs (after consensusID):
idXMLs (after filtering):
idXMLs (after inference):
features:
consensus features:
I think that this centralization would make sense so one does not have to check the issues in two repos. Here's a link to the GitHub documentation describing how to:
Hi,
I would like to install quantms on Kubernetes service on AWS. Are there any instructions on how can I get started with this if it is possble? I do not find any documentation on how to install all the components in AWS env or on any cloud.
@drpatelh reported this in Slack
https://nfcore.slack.com/archives/C02Q3FL29PD/p1661255290163269
Hola! Just noticed you have a bug in the dev code where you are trying to load the same config twice:
Lines 291 to 293 in 6ffb0c9
Lines 337 to 338 in 6ffb0c9
You should keep the one defined later in the config.
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Especially the ones from the openms/subfolder. So they can be reused for other pipelines.
They should be mostly nf-core compatible already but might require some tests and minor adaptions.
Currently untested and probably not working. See AWS issues
Error executing process > 'NFCORE_QUANTMS:QUANTMS:LFQ:ID:DATABASESEARCHENGINES:SEARCHENGINEMSGF (BSA2_F2)'
Caused by:
Process NFCORE_QUANTMS:QUANTMS:LFQ:ID:DATABASESEARCHENGINES:SEARCHENGINEMSGF (BSA2_F2) terminated with an error exit status (1)
Command executed:
MSGFPlusAdapter
-protocol automatic
-in BSA2_F2.mzML
-out BSA2_F2_msgf.idXML
-threads 2
-java_memory 6144 \
-database "18Protein_SoCe_Tr_detergents_trace_target_decoy.fasta"
-instrument high_res
-matches_per_spec 1
-min_precursor_charge 2
-max_precursor_charge 4
-min_peptide_length 6
-max_peptide_length 40
-max_missed_cleavages 2
-isotope_error_range 0,1
-enzyme "Trypsin/P"
-tryptic fully
-precursor_mass_tolerance 5
-precursor_error_units ppm
-fixed_modifications 'Carbamidomethyl (C)'
-variable_modifications 'Oxidation (M)'
-max_mods 3
-PeptideIndexing:IL_equivalent
-PeptideIndexing:unmatched_action warn
-debug 0
2>&1 | tee BSA2_F2_msgf.log
cat <<-END_VERSIONS > versions.yml
"NFCORE_QUANTMS:QUANTMS:LFQ:ID:DATABASESEARCHENGINES:SEARCHENGINEMSGF":
MSGFPlusAdapter: $(MSGFPlusAdapter 2>&1 | grep -E '^Version(.)' | sed 's/Version: //g' | cut -d ' ' -f 1)
msgf_plus: $(msgf_plus 2>&1 | grep -E '^MS-GF+ Release.')
END_VERSIONS
Command exit status:
1
Command output:
Input file 'MSGFPlus.jar' could not be found (by searching on PATH). Either provide a full filepath or fix your PATH environment!
Error: File not found (the file 'MSGFPlus.jar' could not be found)
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I was trying to run the test data set using test_full_data.config and got an error saying there was a missing required column in SDRF file. It needs a characteristics[biological replicate] column.
I posted in the nf-core/test-data slack channel and they suggested I make the bug report here first.
Error message:
The following columns are mandatory and not present in the SDRF: characteristics[biological replicate] -- ERROR
The column characteristics[biological replicate] is not present in the SDRF -- ERROR
There were validation errors!
### Relevant files
_No response_
### System information
_No response_
Hardcoding the version information like seen below bears the potential to be inconsistent with the actual sdrf-pipelines version
parse_sdrf --version
Is now the way to get the version in future releases of sdrf-pipelines.
Not a huge problem, but still worth fixing in my opinion: If an invalid value is passed to the "search_engines" parameter (e.g. "mgsf" instead of "msgf"), the workflow will run and "complete successfully", but no searches or subsequent steps will be performed.
Aborting with an informative error message (e.g. "Error: invalid value for parameter 'search_engines': mgsf") would be more useful in such a case.
It may be worth checking that all parameters that require specific values are validated to avoid unexpected results.
$ nextflow run nf-core/quantms -r dev -profile docker --input ../experimental_design_mzML.tsv --database ../Human_reference_proteome_TD.fasta --search_engines mgsf --outdir results_test --labelling_type "label free sample" --acquisition_method dda
[...]
executor > local (13)
[a6/835132] process > NFCORE_QUANTMS:QUANTMS:INPUT_CHECK:SAMPLESHEET_CHECK [100%] 1 of 1 ✔
[c3/d7052e] process > NFCORE_QUANTMS:QUANTMS:CREATE_INPUT_CHANNEL:PREPROCESS_EXPDESIGN [100%] 1 of 1 ✔
[- ] process > NFCORE_QUANTMS:QUANTMS:FILE_PREPARATION:THERMORAWFILEPARSER -
[f9/21a528] process > NFCORE_QUANTMS:QUANTMS:FILE_PREPARATION:MZMLINDEXING (20220301a_JR_SST_05) [100%] 10 of 10 ✔
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:ID:PSMRESCORING:EXTRACTPSMFEATURES -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:ID:PSMRESCORING:PERCOLATOR -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:ID:PSMFDRCONTROL:IDSCORESWITCHER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:ID:PSMFDRCONTROL:IDFILTER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:FEATUREMAPPER:ISOBARICANALYZER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:FEATUREMAPPER:IDMAPPER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:FILEMERGE -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:PROTEININFERENCE:PROTEININFERENCER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:PROTEININFERENCE:IDFILTER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:PROTEINQUANT:IDCONFLICTRESOLVER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:PROTEINQUANT:PROTEINQUANTIFIER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:PROTEINQUANT:MSSTATSCONVERTER -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:ID:PSMRESCORING:EXTRACTPSMFEATURES -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:ID:PSMRESCORING:PERCOLATOR -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:ID:PSMFDRCONTROL:IDSCORESWITCHER -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:ID:PSMFDRCONTROL:IDFILTER -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:PROTEOMICSLFQ -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:MSSTATS -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:DIANNCFG -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:LIBRARYGENERATION -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:DIANNSEARCH -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:DIANNCONVERT -
[c4/6c40ab] process > NFCORE_QUANTMS:QUANTMS:CUSTOM_DUMPSOFTWAREVERSIONS (1) [100%] 1 of 1 ✔
[- ] process > NFCORE_QUANTMS:QUANTMS:SUMMARYPIPELINE -
-[nf-core/quantms] Pipeline completed successfully-No response
Nextflow version: 21.10.6
nf-core/quantms revision: 1193c6e [dev]
OS: Ubuntu 20.04.4 LTS
Hello! I was hoping we could discuss why the lookup of a modification is using the 'NT' name when an unimod accession is available. It sincerely feels like the human readable name should be a fallback or at least checked for consistency when validating the input SDRF.
LMK what you think!
Mod specified as:
NT=Carbamidomethylation; MT=Fixed; TA=C; AC=Unimod:4
I am assuming right now that it should have been specified as its PSI name:
NT=Carbamidomethyl; MT=Fixed; TA=C; AC=Unimod:4 BUT I believe it should not be used anyway, since the unimod accession is available.
(Should this value be validated in sdrf_pipelines ?)
LMK if we could improve the documentation on the pipeline to know what fields/sub-fields actually matter for the execution.
Error trace at the NFCORE_QUANTMS:QUANTMS:DIA:DIANNCONVERT stage:
Error executing process > 'NFCORE_QUANTMS:QUANTMS:DIA:DIANNCONVERT (input_files.sdrf)'
Caused by:
Essential container in task exited
Command executed:
diann_convert.py convert \
--folder ./ \
--exp_design input_files.sdrf_openms_design.tsv \
--diann_version ./version/versions.yml \
--dia_params "0.02;Da;10;ppm;Trypsin;Carbamidomethylation (C);Oxidation (M)" \
--charge 4 \
--missed_cleavages 2 \
--qvalue_threshold 0.01 \
2>&1 | tee convert_report.log
cat <<-END_VERSIONS > versions.yml
"NFCORE_QUANTMS:QUANTMS:DIA:DIANNCONVERT":
pyopenms: $(pip show pyopenms | grep "Version" | awk -F ': ' '{print $2}')
END_VERSIONS
Command exit status:
1
line 422, in MTD_mod_info
mod_obj = mods_db.getModification(mod)
File "pyopenms/pyopenms_2.pyx", line 9057, in pyopenms.pyopenms_2.ModificationsDB.getModification
File "pyopenms/pyopenms_2.pyx", line 9016, in pyopenms.pyopenms_2.ModificationsDB._getModification_1
RuntimeError: the value 'Carbamidomethylation (C)' was used but is not valid; Retrieving the modification failed. It is not available for the residue '' and term specificity 'none'.
const OpenMS::ResidueModification* OpenMS::ModificationsDB::searchModificationsFast(const OpenMS::String&, bool&, const OpenMS::String&, OpenMS::ResidueModification::TermSpecificity) constModification not found: Carbamidomethylation (C)
Warning: OPENMS_DATA_PATH environment variable already exists. pyOpenMS will use it (/usr/local/share/OpenMS/) to locate data in the OpenMS share folder (e.g., the unimod database), instead of the default (/usr/local/lib/python3.10/site-packages/pyopenms/share/OpenMS).
1nxf-scratch-dir ip-172-31-26-118.eu-west-1.compute.internal:/tmp/nxf.XXXXSM8v5U
Input file 'MSGFPlus.jar' could not be found (by searching on PATH). Either provide a full filepath or fix your PATH environment!
Error: File not found (the file 'MSGFPlus.jar
Hi, thanks for the all useful docs and work so far!
I am currently trying to run DIA data through quantms. We are primarily interested in Phosphoproteomics but I would like to run the data through DIA-NN to see how feasible our outputs are for use. From the flowchart in the homepage, it appears that in DIA-LFQ mode the pipeline should not run Comet or percolator and instead go straight into DIA-NN after mzML indexing.
However, with my run the thermorawparser completes successfully and yet ProteomicsLFQ amongst other steps such as Percolator is carried out. It always errors out at the NFCORE_QUANTMS:QUANTMS:LFQ:PROTEOMICSLFQ with an error no proteins remaining after FDR message. I am new to proteomics (and Nextflow!) but from the docs I can see that it states the comet search engine and these steps are not appropriate for DIA data? Could this be the cause of the error?
Based on the argument parameters, I do not think my command line is triggering any DDA-LFQ jobs but perhaps it is something in the SDRF?
In addition: WARN: There's no process matching config selector: MULTIQC -- Did you mean: PMULTIQC?
Thanks in advance!
Kind regards,
James
nextflow run nf-core/quantms -r 1.2.0 --input '/home/james.burgess/projects/JB240122_MaxQuant_MSQuant_Benchmarking/MaxQuant_MSquant_Linux_Benchmarking/quantms/JB230130_quantms_input.sdrf.tsv' --root_folder '/ibm/hpcfs1/tmp/JB230123_DATA/DATA/raw' --database '/ibm/hpcfs1/tmp/JB230123_DATA/DATA/MaxDIA/fasta/JB230123_quantms_database.fasta' --outdir '/ibm/hpcfs1/tmp/JB230123_DATA/DATA/JB230123_quantms_proc' --acquisition_method 'dia' --max_cpus 60 --max_memory 150GB -profile singularity -resume
[8e/b7eb83] process > NFCORE_QUANTMS:QUANTMS:INPUT_CHECK:SAMPLESHEET_CHECK (JB230130_quantms_input.sdrf.tsv) [ 0%] 0 of 1
[- ] process > NFCORE_QUANTMS:QUANTMS:CREATE_INPUT_CHANNEL:SDRFPARSING -
[- ] process > NFCORE_QUANTMS:QUANTMS:FILE_PREPARATION:DECOMPRESS -
[- ] process > NFCORE_QUANTMS:QUANTMS:FILE_PREPARATION:MZMLINDEXING -
[- ] process > NFCORE_QUANTMS:QUANTMS:FILE_PREPARATION:THERMORAWFILEPARSER -
[- ] process > NFCORE_QUANTMS:QUANTMS:FILE_PREPARATION:MZMLSTATISTICS -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:ID:DATABASESEARCHENGINES:SEARCHENGINECOMET -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:ID:PSMRESCORING:EXTRACTPSMFEATURES -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:ID:PSMRESCORING:PERCOLATOR -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:ID:PSMFDRCONTROL:IDSCORESWITCHER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:ID:PSMFDRCONTROL:IDFILTER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:FEATUREMAPPER:ISOBARICANALYZER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:FEATUREMAPPER:IDMAPPER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:FILEMERGE -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:PROTEININFERENCE:PROTEININFERENCER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:PROTEININFERENCE:IDFILTER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:PROTEINQUANT:IDCONFLICTRESOLVER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:PROTEINQUANT:PROTEINQUANTIFIER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:PROTEINQUANT:MSSTATSCONVERTER -
[- ] process > NFCORE_QUANTMS:QUANTMS:TMT:MSSTATSTMT -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:ID:DATABASESEARCHENGINES:SEARCHENGINECOMET -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:ID:PSMRESCORING:EXTRACTPSMFEATURES -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:ID:PSMRESCORING:PERCOLATOR -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:ID:PSMFDRCONTROL:IDSCORESWITCHER -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:ID:PSMFDRCONTROL:IDFILTER -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:PROTEOMICSLFQ -
[- ] process > NFCORE_QUANTMS:QUANTMS:LFQ:MSSTATS -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:DIANNCFG -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:SILICOLIBRARYGENERATION -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:DIANN_PRELIMINARY_ANALYSIS -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:ASSEMBLE_EMPIRICAL_LIBRARY -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:INDIVIDUAL_FINAL_ANALYSIS -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:DIANNSUMMARY -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:DIANNCONVERT -
[- ] process > NFCORE_QUANTMS:QUANTMS:DIA:MSSTATS -
[- ] process > NFCORE_QUANTMS:QUANTMS:CUSTOM_DUMPSOFTWAREVERSIONS -
[- ] process > NFCORE_QUANTMS:QUANTMS:SUMMARYPIPELINE -
WARN: There's no process matching config selector: MULTIQC -- Did you mean: PMULTIQC?Nextflow version: 23.10.1.5891
Hardware: HPC
Executor: local
Container engine: Singularity, Conda
OS: Linux 4.18.0-513.5.1.el8_9.x86_64
Version of nf-core/quantms: 1.2.0
we removed the distinction of UTIL_ and TOPP_ tool in OpenMS 3.1.
This means we need to update documentation links in quantms to reflect those changes.
Replacing "UTIL_" with "TOPP_" should suffice.
No response
No response
No response
We need to upgrade the singularity and docker images to use the latest comet version 2023.
There are a bunch of remaining tasks for post-release of 1.1.1:
nf-core bump-version 1.2.0dev?) #100This PR is inspired by: https://nf-co.re/docs/contributing/release_checklist#steps-to-release
Im getting an odd error when trying to run 40 samples through the pipeline on AWS batch.
Everything proceeds normally until the MZMLINDEXING step when the head node crashes, with the error java error “Failed to acquire stream chunk”.
The log where the error happens:
| 2022-10-05T11:20:19.906-07:00 | [51/a3778a] Submitted process > NFCORE_QUANTMS:QUANTMS:FILE_PREPARATION:MZMLINDEXING (file_32)
| 2022-10-05T11:20:23.240-07:00 | Failed to acquire stream chunk
| 2022-10-05T11:20:23.240-07:00 | -- Check script '/root/.nextflow/assets/[users_name]/nf-quantms/./workflows/../subworkflows/local/file_preparation.nf' at line: 32 or see '.nextflow.log' file for more details
| 2022-10-05T11:20:23.263-07:00 | -�[0;35m[nf-core/quantms]�[0;31m Pipeline completed with errors�[0m-
| 2022-10-05T11:20:23.267-07:00 | WARN: Killing running tasks (39)
| 2022-10-05T11:20:23.469-07:00CopyWARN: Unable to get file attributes file: s3://[users_bucket]/versions.yml -- Cause: com.amazonaws.SdkClientException: Failed to sanitize XML document destined for handler class com.amazonaws.services.s3.model.transform.XmlResponsesSaxParser$ListBucketHandler | WARN: Unable to get file attributes file: s3://[users_bucket]/_nextflow/runs/39/df0b79873b070c19eddd53c33b8288/versions.yml -- Cause: com.amazonaws.SdkClientException: Failed to sanitize XML document destined for handler class com.amazonaws.services.s3.model.transform.XmlResponsesSaxParser$ListBucketHandler
| 2022-10-05T11:20:27.115-07:00 | Failed to acquire stream chunk
| 2022-10-05T11:20:39.624-07:00 | === Running Cleanup ===
The code refenced:
quantms/subworkflows/local/file_preparation.nf
Lines 30 to 34 in a1bf7d4
The head node seems to crash at a different file number if I change the amount of memory I assign to the head node. Is all the data passing through the head node somewhere? I've never had this problem with any of my NGS pipelines, they use more and larger files, so I am a little confused at this crash.
No response
The log file where it crashes: (it is dated different, but this is the same error that always shows)
Oct-10 23:03:07.744 [Actor Thread 15] ERROR nextflow.extension.DataflowHelper - @unknown
java.io.IOException: Failed to acquire stream chunk
at com.upplication.s3fs.ng.FutureInputStream.nextBuffer(FutureInputStream.java:78)
at com.upplication.s3fs.ng.FutureInputStream.read(FutureInputStream.java:63)
at java.base/sun.nio.cs.StreamDecoder.readBytes(StreamDecoder.java:270)
at java.base/sun.nio.cs.StreamDecoder.implRead(StreamDecoder.java:313)
at java.base/sun.nio.cs.StreamDecoder.read(StreamDecoder.java:188)
at java.base/java.io.InputStreamReader.read(InputStreamReader.java:177)
at java.base/java.io.BufferedReader.fill(BufferedReader.java:162)
at java.base/java.io.BufferedReader.readLine(BufferedReader.java:329)
at java.base/java.io.BufferedReader.readLine(BufferedReader.java:396)
at java_io_BufferedReader$readLine.call(Unknown Source)
at Script_d4bc0d6a$_runScript_closure1$_closure2$_closure5$_closure8$_closure9$_closure11.doCall(Script_d4bc0d6a:32)
at jdk.internal.reflect.GeneratedMethodAccessor276.invoke(Unknown Source)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:568)
at org.codehaus.groovy.reflection.CachedMethod.invoke(CachedMethod.java:107)
at groovy.lang.MetaMethod.doMethodInvoke(MetaMethod.java:323)
at org.codehaus.groovy.runtime.metaclass.ClosureMetaClass.invokeMethod(ClosureMetaClass.java:274)
at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1035)
at groovy.lang.Closure.call(Closure.java:412)
at groovy.lang.Closure.call(Closure.java:428)
at org.codehaus.groovy.runtime.DefaultGroovyMethods.upto(DefaultGroovyMethods.java:16406)
at org.codehaus.groovy.runtime.dgm$875.doMethodInvoke(Unknown Source)
at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1268)
at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1035)
at groovy.runtime.metaclass.NumberDelegatingMetaClass.invokeMethod(NumberDelegatingMetaClass.java:60)
at org.codehaus.groovy.runtime.callsite.PojoMetaClassSite.call(PojoMetaClassSite.java:44)
at org.codehaus.groovy.runtime.callsite.AbstractCallSite.call(AbstractCallSite.java:148)
at Script_d4bc0d6a$_runScript_closure1$_closure2$_closure5$_closure8$_closure9.doCall(Script_d4bc0d6a:31)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:568)
at org.codehaus.groovy.reflection.CachedMethod.invoke(CachedMethod.java:107)
at groovy.lang.MetaMethod.doMethodInvoke(MetaMethod.java:323)
at org.codehaus.groovy.runtime.metaclass.ClosureMetaClass.invokeMethod(ClosureMetaClass.java:274)
at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1035)
at groovy.lang.Closure.call(Closure.java:412)
at groovy.lang.Closure.call(Closure.java:428)
at org.codehaus.groovy.runtime.IOGroovyMethods.withReader(IOGroovyMethods.java:1160)
at org.apache.groovy.nio.extensions.NioExtensions.withReader(NioExtensions.java:1434)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:568)
at org.codehaus.groovy.runtime.metaclass.ReflectionMetaMethod.invoke(ReflectionMetaMethod.java:54)
at org.codehaus.groovy.runtime.metaclass.NewInstanceMetaMethod.invoke(NewInstanceMetaMethod.java:54)
at groovy.lang.MetaMethod.doMethodInvoke(MetaMethod.java:323)
at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1268)
at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1035)
at groovy.runtime.metaclass.NextflowDelegatingMetaClass.invokeMethod(NextflowDelegatingMetaClass.java:66)
at org.codehaus.groovy.runtime.callsite.PojoMetaClassSite.call(PojoMetaClassSite.java:44)
at org.codehaus.groovy.runtime.callsite.AbstractCallSite.call(AbstractCallSite.java:139)
at Script_d4bc0d6a$_runScript_closure1$_closure2$_closure5$_closure8.doCall(Script_d4bc0d6a:30)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:568)
at org.codehaus.groovy.reflection.CachedMethod.invoke(CachedMethod.java:107)
at groovy.lang.MetaMethod.doMethodInvoke(MetaMethod.java:323)
at org.codehaus.groovy.runtime.metaclass.ClosureMetaClass.invokeMethod(ClosureMetaClass.java:274)
at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1035)
at groovy.lang.Closure.call(Closure.java:412)
at groovy.lang.Closure.call(Closure.java:428)
at nextflow.extension.BranchOp.doNext(BranchOp.groovy:55)
at jdk.internal.reflect.GeneratedMethodAccessor258.invoke(Unknown Source)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:568)
at org.codehaus.groovy.reflection.CachedMethod.invoke(CachedMethod.java:107)
at groovy.lang.MetaMethod.doMethodInvoke(MetaMethod.java:323)
at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1268)
at groovy.lang.MetaClassImpl.invokeMethodClosure(MetaClassImpl.java:1048)
at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1142)
at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1035)
at groovy.lang.Closure.call(Closure.java:412)
at groovy.lang.Closure.call(Closure.java:428)
at groovy.lang.Closure$call.call(Unknown Source)
at nextflow.extension.DataflowHelper$_subscribeImpl_closure2.doCall(DataflowHelper.groovy:285)
at jdk.internal.reflect.GeneratedMethodAccessor202.invoke(Unknown Source)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:568)
at org.codehaus.groovy.reflection.CachedMethod.invoke(CachedMethod.java:107)
at groovy.lang.MetaMethod.doMethodInvoke(MetaMethod.java:323)
at org.codehaus.groovy.runtime.metaclass.ClosureMetaClass.invokeMethod(ClosureMetaClass.java:274)
at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1035)
at groovy.lang.Closure.call(Closure.java:412)
at groovyx.gpars.dataflow.operator.DataflowOperatorActor.startTask(DataflowOperatorActor.java:120)
at groovyx.gpars.dataflow.operator.DataflowOperatorActor.onMessage(DataflowOperatorActor.java:108)
at groovyx.gpars.actor.impl.SDAClosure$1.call(SDAClosure.java:43)
at groovyx.gpars.actor.AbstractLoopingActor.runEnhancedWithoutRepliesOnMessages(AbstractLoopingActor.java:293)
at groovyx.gpars.actor.AbstractLoopingActor.access$400(AbstractLoopingActor.java:30)
at groovyx.gpars.actor.AbstractLoopingActor$1.handleMessage(AbstractLoopingActor.java:93)
at groovyx.gpars.util.AsyncMessagingCore.run(AsyncMessagingCore.java:132)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
at java.base/java.lang.Thread.run(Thread.java:833)
Caused by: java.util.concurrent.ExecutionException: java.lang.OutOfMemoryError: Cannot reserve 10,485,760 bytes of direct buffer memory (allocated: 1070363393, limit: 1,073,741,824)
at java.base/java.util.concurrent.FutureTask.report(FutureTask.java:122)
at java.base/java.util.concurrent.FutureTask.get(FutureTask.java:191)
at com.upplication.s3fs.ng.FutureInputStream.nextBuffer(FutureInputStream.java:75)
... 94 common frames omitted
Caused by: java.lang.OutOfMemoryError: Cannot reserve 10485760 bytes of direct buffer memory (allocated: 1070363393, limit: 1073741824)
at java.base/java.nio.Bits.reserveMemory(Bits.java:178)
at java.base/java.nio.DirectByteBuffer.<init>(DirectByteBuffer.java:121)
at java.base/java.nio.ByteBuffer.allocateDirect(ByteBuffer.java:332)
at com.upplication.s3fs.ng.ChunkBuffer.<init>(ChunkBuffer.java:41)
at com.upplication.s3fs.ng.ChunkBufferFactory.create(ChunkBufferFactory.java:65)
at com.upplication.s3fs.ng.S3ParallelDownload.doDownload(S3ParallelDownload.java:136)
at com.upplication.s3fs.ng.S3ParallelDownload.lambda$safeDownload$1(S3ParallelDownload.java:127)
at dev.failsafe.Functions.lambda$toCtxSupplier$11(Functions.java:236)
at dev.failsafe.Functions.lambda$get$0(Functions.java:46)
at dev.failsafe.internal.RetryPolicyExecutor.lambda$apply$0(RetryPolicyExecutor.java:75)
at dev.failsafe.SyncExecutionImpl.executeSync(SyncExecutionImpl.java:176)
at dev.failsafe.FailsafeExecutor.call(FailsafeExecutor.java:437)
at dev.failsafe.FailsafeExecutor.get(FailsafeExecutor.java:115)
at com.upplication.s3fs.ng.S3ParallelDownload.safeDownload(S3ParallelDownload.java:127)
at com.upplication.s3fs.ng.FutureIterator.lambda$init$0(FutureIterator.java:59)
at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
... 3 common frames omitted
Oct-10 23:03:07.773 [Actor Thread 15] DEBUG nextflow.Session - Session aborted -- Cause: Failed to acquire stream chunk
Nextflow version (eg. 22.04.5)
Hardware AWS
Executor awsbatch
Container engine: default
OS AWSLinux
Version of nf-core/quantms v1.1dev
No response
No response
No response
In the DDA/label-free workflow (perhaps in others as well), it would be useful to get an output of the identification results at the end of the search/inference/filtering pipeline, but before quantification. This would help for QC-purposes and to get a fuller understanding of the data on the ID level.
Currently the latest "ID-only" output is in the "idfilter" directory, containing results after database search, rescoring, (consensus ID, if applicable) and filtering according to "psm_pep_fdr_cutoff" - but before protein inference and protein-level filtering ("protein_level_fdr_cutoff"). Later outputs (after ProteomicsLFQ) only contain results for quantified IDs - omitting those that were identified, but never quantified. (Is that correct?)
Thank you to all the developers for their hard work! Recently, I have been learning the basics of OpenMS, and I have been using the data PXD015828 from PRIDE as a practice dataset. During this process, I have encountered the following issues. I referenced quantms（https://github.com/nf-core/quantms/tree/master） to build a set of scripts, and I encountered three issues during testing.
（OpenMS 3.1 installed from bioconda）
The position of the consensusID step is inconsistent between the figure (https://github.com/nf-core/quantms/blob/master/docs/images/quantms_metro.png) and the source code. In the code, it is placed after rescoring, while in the figure, it is placed before rescoring. Does this inconsistency affect the experimental results? Which approach is better? Is it preferable to have consistency?
I faced Error in ProteomicsLFQ ( submitted to OpenMS), And I found the progress is omit in quantms, I want to put it back. I tried to add PeptideIndexer before ConsensusID, it it right?
I faced error when I use IDScoreSwitcher, I just skipped it Is it allowed to skip the IDScoreSwitcher?
Errorlog:
IDScoreSwitcher -in work/20170510_Joshi_9119/consensusID.idXML -out work/20170510_Joshi_9119/idscoreswitcher.idXML -new_score_orientation higher_better -threads 48 -debug 0
Error: Unexpected internal error (Meta value '' not found for peptide hit with sequence 'SETNDS(Phospho)S(Phospho)S(Phospho)GS(Phospho)QS(Phospho)HQDASAASSAPPR', charge 2, score 1.0)
And I also faced some openms error report, I submit in OpenMS, Thank you for your assistance. I hope to contribute to this program.
Right now, we are using our small test data. Which is kind of enough to show the capabilities but the results do not make much sense. Would be nice if you e.g. see reasonable proteins in the volcano plots of msstats or things like that.
We can then write stuff in the documentation on how to interpret results etc
I saw you added the Zenodo ID, howeer it's better to use the 'global' one that points to th altest version (so you don't have to update it all the time):
Cite all versions? You can cite all versions by using the DOI 10.5281/zenodo.7754148. This DOI represents all versions, and will always resolve to the latest one. Read more. (that's yours)
Also you need to update the doi in the manifest section of the nextflow.config
And can we remove it?
It certainly should not be part of the modules since it does not seem to be nf-core specific.
If you want to keep it, it should go into a config file.
I think this is used for the nf-tower Datasets feature.
For now, I think it is a json schema like the input params (nextflow.config) but probably it would be nice to think about a PEP specification in the longterm for SDRF in general.
Example of the JSON schema in RNAseq pipeline: https://github.com/nf-core/rnaseq/blob/master/assets/schema_input.json
PEP specification: http://pep.databio.org/en/latest/
E.g. for MSGF+ we can first create the index for the database and then pass it to every ID step. Might need some customization in the OpenMS adapters.
i have some error in when set reference_channel for my tmt run. i use tmt131N as my reference and run --reference_channel 131N. it report that:
ERROR: Validation of pipeline parameters failed!
See stagein directive in the module
I think we have the basic features ready in this pipeline.
Unfortunately most bug reports that we receive are from people struggling with SDRF or getting errors in the middle of the pipeline because of erroneous SDRFs.
I think we should significantly extend and improve both the checks and error messages in multiple steps, starting with SDRF parsing before focusing on even more complicated designs such as TMT with MBR. We can add erroneous SDRFs to the sdrf-pipelines repository and make sure that reasonable error messages are given. We can add several validation levels: for openms, for quantms, for quantms with MSstats (or downstream statistics), with each level requiring more information. We maybe should even invest in a basic UI component to more easily complete an SDRF in a way that is suitable for this pipeline. We can build on lesSDRF.
Other steps that would benefit from error handling are ID and filtering steps, where we could double-check that ID files are not empty.
Maintainers should also make sure to forward bad error handling to the upstream tools such as OpenMS.
Hi all,
as the title says, I was running the LFQ test pipeline, dev, and it failed due to a missing singularity container which couldn't be downloaded, ghcr.io-openms-openms-executables-latest.img. Maybe relevant, the test pipeline ran successfully with `-r 1.0
Cheers.
`nextflow run nf-core/quantms -r 1.0 -profile singularity,test_lfq --outdir test_pipeline`
Error executing process > 'NFCORE_QUANTMS:QUANTMS:LFQ:PROTEOMICSLFQ (BSA_design_urls_openms_design)'
Caused by:
Failed to pull singularity image
command: singularity pull --name ftp.pride.ebi.ac.uk-pride-resources-tools-ghcr.io-openms-openms-executables-latest.img.img.pulling.1672748804878 https://ftp.pride.ebi.ac.uk/pride/resources/tools/ghcr.io-openms-openms-executables-latest.img > /dev/null
status : 255
message:
INFO: Downloading network image
FATAL: the requested image was not foundNo response
Just a To-do.
New version of sdrf-pipelines version 0.0.23 has been released. We should move the pipeline to that version.
Im having some trouble testing v1.1 with my data. I am getting a crash when it hits DIANNCONVERT.
Looking into it, diann_convert.py is passed --dia_params "20;ppm;10;ppm;Trypsin;;Oxidation (M)", so
Lines 205 to 215 in a1bf7d4
mods_db.getModification(mod) says there is no mod for "".
Diving into that, I found my seed.sdrf_config has blanks for FixedModifications. This is probably because my original input SDRF file did not have any column for it. Is that the expected result if i don’t have any FixedModifications in my SDRF file or should it put null ?
One possible solution is just to change L215 to check for "null" or "", but I wonder if a better solution is to change it upstream where seed.sdrf_config is generated as it probably isn't supposed to have blanks in it?
I can't share my original seed file, but the bug should be reproducible if you delete the comment[modification parameters] columns from nf-core/test-datasets/quantms/testdata-aws/dia_full/PXD004684.sdrf.tsv
No response
No response
Nextflow version (eg. 22.04.5)
Hardware AWS
Executor awsbatch
Container engine: default
OS AWSLinux
Version of nf-core/quantms v1.1dev
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