mannlabs / alphabase Goto Github PK

View Code? Open in Web Editor NEW

26.0 26.0 8.0 39.01 MB

Infrastructure of AlphaX ecosystem

Home Page: https://alphabase.readthedocs.io

License: Apache License 2.0

Python 24.23% Shell 0.30% Jupyter Notebook 75.25% HTML 0.06% Inno Setup 0.12% CSS 0.04%

infrastructure mass-spectrometry proteomics python

alphabase's People

Contributors

Stargazers

Watchers

Forkers

gsaxena888 jalew188 yangkl96 lucasmiranda42 vbrennsteiner sayan10-pixel

alphabase's Issues

Handling of non amino-acid FASTA characters

Describe the bug
Currently, non AA characters like X,J,B break the isotope calculation and might have undefined behaviour in other functions.
Where should we capture these precursors? during the FASTA digest?

To Reproduce
Steps to reproduce the behavior:

Download Isoform FASTA
Run fasta digest

Expected behavior
I think it would be best to drop them with a warning.

Logs

2024-01-15 18:54:41> Running IsotopeGenerator
  0%|          | 1/464 [00:05<39:16,  5.09s/it]/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613: RuntimeWarning: invalid value encountered in divide
  precursor_dist [/](https://file+.vscode-resource.vscode-cdn.net/)= np.sum(precursor_dist, axis=1, keepdims=True)
  8%|▊         | 35/464 [00:06<00:30, 14.08it/s]/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613: RuntimeWarning: invalid value encountered in divide
  precursor_dist [/](https://file+.vscode-resource.vscode-cdn.net/)= np.sum(precursor_dist, axis=1, keepdims=True)
 19%|█▊        | 86/464 [00:08<00:13, 27.12it/s]/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613: RuntimeWarning: invalid value encountered in divide
  precursor_dist [/](https://file+.vscode-resource.vscode-cdn.net/)= np.sum(precursor_dist, axis=1, keepdims=True)
 22%|██▏       | 101/464 [00:09<00:13, 27.31it/s]/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613: RuntimeWarning: invalid value encountered in divide
  precursor_dist [/](https://file+.vscode-resource.vscode-cdn.net/)= np.sum(precursor_dist, axis=1, keepdims=True)
 27%|██▋       | 126/464 [00:10<00:13, 25.06it/s]/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613: RuntimeWarning: invalid value encountered in divide
  precursor_dist [/](https://file+.vscode-resource.vscode-cdn.net/)= np.sum(precursor_dist, axis=1, keepdims=True)
[/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613](https://file+.vscode-resource.vscode-cdn.net/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613): RuntimeWarning: invalid value encountered in divide
  precursor_dist [/](https://file+.vscode-resource.vscode-cdn.net/)= np.sum(precursor_dist, axis=1, keepdims=True)
 35%|███▌      | 163/464 [00:12<00:18, 16.47it/s]/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613: RuntimeWarning: invalid value encountered in divide
  precursor_dist [/](https://file+.vscode-resource.vscode-cdn.net/)= np.sum(precursor_dist, axis=1, keepdims=True)
 46%|████▌     | 213/464 [00:14<00:11, 22.18it/s]/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613: RuntimeWarning: invalid value encountered in divide
  precursor_dist [/](https://file+.vscode-resource.vscode-cdn.net/)= np.sum(precursor_dist, axis=1, keepdims=True)
 62%|██████▏   | 288/464 [00:17<00:07, 24.15it/s]/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613: RuntimeWarning: invalid value encountered in divide
  precursor_dist [/](https://file+.vscode-resource.vscode-cdn.net/)= np.sum(precursor_dist, axis=1, keepdims=True)
 74%|███████▎  | 342/464 [00:20<00:06, 19.47it/s]/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613: RuntimeWarning: invalid value encountered in divide
  precursor_dist [/](https://file+.vscode-resource.vscode-cdn.net/)= np.sum(precursor_dist, axis=1, keepdims=True)
 93%|█████████▎| 432/464 [00:24<00:01, 19.63it/s]/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:613: RuntimeWarning: invalid value encountered in divide
  precursor_dist [/](https://file+.vscode-resource.vscode-cdn.net/)= np.sum(precursor_dist, axis=1, keepdims=True)
100%|██████████| 464/464 [00:26<00:00, 17.76it/s]

DiaNNDecoyLib decoy generation fails with Selenocystein

Describe the bug
Alphabase can't generate decooys when the sequence contains a selenocysteine.

To Reproduce
Steps to reproduce the behavior:

Download reviewed Fasta from Uniprot
Generate DIA-NN decoy lib as described in APD notebook

Expected behavior
Decoy sequences are appended.

Logs
fasta_lib.append_decoy_sequence() fails with: 'substring not found'

Example

for seq in decoy_lib.precursor_df.sequence:
    try:
        (lambda x:
            x[0]+decoy_lib.mutated_AAs[decoy_lib.raw_AAs.index(x[1])]+
            x[2:-2]+decoy_lib.mutated_AAs[decoy_lib.raw_AAs.index(x[-2])]+x[-1])(seq)
    except Exception as e:
        print(seq)
        #print(e)

SUCCHCR
KUEUPSN
GPSSGGUG
RGPSSGGUG
FUIFSSSLK
AEENITESCQUR
SGLDPTVTGCUG
SGASILQAGCUG
SSGLDITQKGCUG
RSGLDPTVTGCUG
GUGCKVPQEALLK
FUIFSSSLKFVPK
RSGASILQAGCUG
SUCCHCRHLIFEK
LYAGAILEVCGUK
KLYAGAILEVCGUK
LANGGEGMEEATVVIEHCTSUR
LPPAAUQISQQLIPTEASASUR
EKLANGGEGMEEATVVIEHCTSUR
LPPAAUQISQQLIPTEASASURUK
ENLPSLCSUQGLRAEENITESCQUR
AEENITESCQURLPPAAUQISQQLIPTEASASUR

LOESS Regression fails with few datapoints

There is still an issue if there fewer datapoints than n_kernels * kernel_size. Will change this in the future.

Originally posted by @GeorgWa in #41 (comment)

SpecLib function to count number of fragments per precursor

Allow Fixed Terminal Modifications

At the moment SpecLibFasta.add_modifications() fails to include any terminal modifications.

Last fragment missing in create_fragment_mz_dataframe_by_sort_precursor

When calculating the fragments for a precursor, the last fragment is missing:

Note that b7 (782.35672) and y7 is missing (800.36728)

Include dataframe validation schema

modloss_importance in modification.tsv

Were the modloss_importance values derived from somewhere or just arbitrary? I noticed they were only set to non-zero values for phospho and glygly. Does this mean that unless manually changed in modification.tsv to be 1, b/y ions with modlosses will be ignored?

Thanks,
Kevin

missing proteins and genes in alphabase.spectral_library.reader.SWATHLibraryReader

Use multiprocessing for decoy generation

At the moment decoy generation is not using multiprocessing by default.
A batched approach could significantly decrease runtime.

calc_precursor_mz leads to unexpected behavior

Hi Feng, I observed the following bug when using AB to create multiplexed DIA libraries. It's technically not a bug but more of a incompatible behavior between functions which might confuse users. Let me know what you think, I'm happy to make a PR.

Describe the bug
When calling calc_precursor_mz on a spectral library, clip_by_precursor_mz_ is always invoked, which messes up the precursor fragment mapping.

To Reproduce

Create a library with a preecursor just below the upper mz limit:

precursor_df = pd.DataFrame([
    {'sequence': 'AGHCEWQMKPERGWWWWWPPWWWWGGGGAGGAG', 'mods': 'Dimethyl@Any N-term;Dimethyl@K', 'mod_sites': '0;8', 'charge': 2},
    {'sequence': 'AGHCEWQMKPE', 'mods': 'Dimethyl@Any N-term;Dimethyl@K', 'mod_sites': '0;8', 'charge': 3},
    {'sequence': 'AGHCEWQMKPERGWWWWWPPWWWWGGGGAGGAGG', 'mods': '', 'mod_sites': '', 'charge': 2},
])

spec_lib = SpecLibBase(
    ['b_z1','b_z2','y_z1','y_z2'],
    decoy='pseudo_reverse',
    precursor_mz_max=2000,
)
spec_lib._precursor_df = precursor_df
spec_lib.calc_precursor_mz()
spec_lib.calc_fragment_mz_df()

spec_lib.precursor_df

sequence	mods	mod_sites	charge	nAA	precursor_mz	frag_start_idx	frag_stop_idx
AGHCEWQMKPE	Dimethyl@Any N-term;Dimethyl@K	0;8	3	11	457.877652	0	10
AGHCEWQMKPERGWWWWWPPWWWWGGGGAGGAG	Dimethyl@Any N-term;Dimethyl@K	0;8	2	33	1997.896037	10	42
AGHCEWQMKPERGWWWWWPPWWWWGGGGAGGAGG			2	34	1998.375468	42	75

Modify the sequence or modification which will change the m/z of some precursors, recalculate the precursor mz:

spec_lib._precursor_df['mods'] = spec_lib._precursor_df['mods'].str.replace('Dimethyl', 'Dimethyl:2H(6)13C(2)')
spec_lib.calc_precursor_mz()

sequence	mods	mod_sites	charge	nAA	precursor_mz	frag_start_idx	frag_stop_idx
AGHCEWQMKPE	Dimethyl:2H(6)13C(2)@Any N-term;Dimethyl:2H(6)...	0;8	3	11	463.240566	0	10
AGHCEWQMKPERGWWWWWPPWWWWGGGGAGGAGG			2	34	1998.375468	42	75

recalculate the fragment masses:
spec_lib.calc_fragment_mz_df()

File "/Users/georgwallmann/miniconda3/envs/alpha/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3378, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "/var/folders/lc/9594t94d5b5_gn0y04w1jh980000gn/T/ipykernel_6587/3999293303.py", line 1, in <module>
    spec_lib.calc_fragment_mz_df()
  File "/Users/georgwallmann/Documents/git/alphabase/alphabase/spectral_library/base.py", line 361, in calc_fragment_mz_df
    )
  File "/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/fragment.py", line 818, in create_fragment_mz_dataframe
  File "/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/fragment.py", line 876, in create_fragment_mz_dataframe
    '''
  File "/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/fragment.py", line 435, in mask_fragments_for_charge_greater_than_precursor_charge
    elif frag_type == 'y':
  File "/Users/georgwallmann/miniconda3/envs/alpha/lib/python3.9/site-packages/pandas/core/indexing.py", line 815, in __setitem__
    indexer = self._get_setitem_indexer(key)
  File "/Users/georgwallmann/miniconda3/envs/alpha/lib/python3.9/site-packages/pandas/core/indexing.py", line 698, in _get_setitem_indexer
    return self._convert_tuple(key)
  File "/Users/georgwallmann/miniconda3/envs/alpha/lib/python3.9/site-packages/pandas/core/indexing.py", line 897, in _convert_tuple
    keyidx = [self._convert_to_indexer(k, axis=i) for i, k in enumerate(key)]
  File "/Users/georgwallmann/miniconda3/envs/alpha/lib/python3.9/site-packages/pandas/core/indexing.py", line 897, in <listcomp>
    keyidx = [self._convert_to_indexer(k, axis=i) for i, k in enumerate(key)]
  File "/Users/georgwallmann/miniconda3/envs/alpha/lib/python3.9/site-packages/pandas/core/indexing.py", line 1394, in _convert_to_indexer
    key = check_bool_indexer(labels, key)
  File "/Users/georgwallmann/miniconda3/envs/alpha/lib/python3.9/site-packages/pandas/core/indexing.py", line 2567, in check_bool_indexer
    return check_array_indexer(index, result)
  File "/Users/georgwallmann/miniconda3/envs/alpha/lib/python3.9/site-packages/pandas/core/indexers/utils.py", line 553, in check_array_indexer
    raise IndexError(
IndexError: Boolean index has wrong length: 43 instead of 75

Expected behavior
calc_fragment_mz_df requires a continous frag_start_idx, frag_stop_idx, which is not compatible with the way clip_by_precursor_mz_ works. It is also not immediatley clear thatclip_by_precursor_mz_ has been called and precursors were removed.

Possible Solutions

don't call clip_by_precursor_mz_ by default within calc_precursor_mz
issue a warning: n precursors were remove because they were outside the mz limits
call spec_lib.remove_unused_fragments() right after clip_by_precursor_mz_

Adjust isotope distribution if first isotope isn't the monoisotopic intensity

In certain cases the array returned from dist, mono = isotope_dist.calc_formula_distribution() will start before the monoisotopic mass. In these cases, it needs to betrimmed or tracked.

alphabase.peptide.precursor.calc_precursor_isotope_intensity() needs to be adapted to handle this case.

Handle two columns with same name in dataframe validation

SpecLib function to filter fragments by number of allowed fragments

msgfplus rescoring support or AlphaPeptDeep format example

I'd like to use the output of the msgfplus search engine (which I can convert to percolator format) as an input toAlphaPeptDeep's rescoring module. However, I don't think that either msgfplus output or percolator output is supported? Thoughts?

(One idea I had was to write a converter from msgfplus (or percolator) to the "AlphaPeptDeep" format, but I couldn't find an example file for AlphaPeptDeep and documentation for what fields are required so that I could create such a converter. Thoughts?)

Update isotope calculation

Update the isotope calculation for spectral libraries to allow for dense and sparse isotope creation.
Both should be possible by calling a class method like create_dense_isotopes, create_sparse_isotopes.

Costumizing mz/intensity dtypes

Define MZ_DTYPE/RELATIVE_INTENSITY_DTYPE in a const file.

mmh3 installation error

Discussed in #78

^{Originally posted by jalew188 January 18, 2023}
As mmh3 depends on compiling system (e.g. Visual Studio in Windows) as its core is written in C/C++, it is better to write our own hash method using numba to replace mmh3.

See pypa/pip#8657

Include testcase for unexpected modifications in the library reader

Using `calc_precursor_isotope_intensity()` instead of `calc_precursor_isotope()` for default isotope processing

TODO after fixing #120 for the next release