I am trying to run the following workflow
For the example dataset, the run completed successfully. But for the real dataset I get the error below:
OMSSA ran out of RAM because chunking was not used (that’s the default) or ‘chunk_size’ was too large (>30k).
ERROR OMSSAAdapter 0:11 Failing process stdout: [, Progress of ‘OMSSA search’:, Error: OMSSA problem! See above for OMSSA error. If this does not help, increase ‘debug’ level and run again., Note: This message can also be triggered if you run out of space in your tmp directory or (32bit OMSSA only) OMSSA ran out of RAM because chunking was not used (that’s the default) or ‘chunk_size’ was too large (>30k). Look above!, OMSSAAdapter took 12:16 m (wall), 01:28 m (CPU), 6.84 s (system), 01:21 m (user); Peak Memory Usage: 134 MB.]
ERROR OMSSAAdapter 0:11 Failing process stderr: ["…\src\algo\ms\omssa\omssacl.cpp", line 216: Fatal: COMSSA::Run() - Unable to open blast library C:\Users\deepa\Downloads\knime\ashcroft\uniprot.fasta with error:NCBI C++ Exception:, “…\src\objtools\blast\seqdb_reader\seqdbfile.cpp”, line 209: Error: ncbi::CSeqDBIdxFile::CSeqDBIdxFile() - Error: Not a valid version 4 database., ]
ERROR OMSSAAdapter 0:11 Return code: 9
ERROR OMSSAAdapter 0:11 Execute failed: Failed to execute node OMSSAAdapter
ERROR FeatureFinderCentroided 0:18 Failing process stdout: [Progress of ‘loading spectra list’:, , 0.13 % , 99.93 % , – done [took 49.64 s (CPU), 04:06 m (Wall)] – , Progress of ‘loading chromatogram list’:, , – done [took 0.02 s (CPU), 0.12 s (Wall)] – , Error: Unexpected internal error (Error: Profile data provided but centroided spectra expected. To enforce processing of the data set the -force flag.)]
ERROR FeatureFinderCentroided 0:18 Failing process stderr:
ERROR FeatureFinderCentroided 0:18 Return code: 8
So I changed the configuration settings in OMSSAAdpater, but I still get the same error
(I changed the default chunk_size from 0 to 10000 and 30000)
Could someone please suggest what’s the correct way to proceed?
I am using OpenMS plugin installed in Knime on Windows.
Thanks a lot for your time,