Question about de novo prediction of own datasets

Hello,

I wanted to try the model on my mgf files and I converted them with the provided script. The resulting .ipc file looks as follows:

Since I don't have any peptide sequences and want to predict new ones.

However when I am trying to run the model with this command:

!python -m instanovo.transformer.predict /content/consensus_spectra.ipc /content/checkpoints/instanovo_yeast.pt --denovo --output_path /content/

I am getting this error:

INFO:root:Initializing inference. INFO:root:Loading data from /content/consensus_spectra.ipc INFO:root:Data loaded, evaluating 100.0%, 10931 samples in total. INFO:root:Knapsack path missing or not specified, generating... INFO:root:Scaling masses. INFO:root:Initializing chart. INFO:root:Performing search. Traceback (most recent call last): File "/usr/lib/python3.10/multiprocessing/queues.py", line 244, in _feed obj = _ForkingPickler.dumps(obj) File "/usr/lib/python3.10/multiprocessing/reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) _pickle.PicklingError: Can't pickle <class 'exceptions.ColumnNotFoundError'>: import of module 'exceptions' failed Traceback (most recent call last): File "/usr/lib/python3.10/multiprocessing/queues.py", line 244, in _feed obj = _ForkingPickler.dumps(obj)

Is this because of the missing peptide sequence? And if so, how can I use the model to predict unidentified spectra?

Thanks for your time and help

instadeepai / InstaNovo

Question about de novo prediction of own datasets #26