Merge pull request #453 from MannLabs/develop

v0.4.6

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.4.5
+current_version = 0.4.6
 commit = True
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?

MANIFEST.in

@@ -2,5 +2,7 @@ include settings.ini
 include LICENSE
 include CONTRIBUTING.md
 include README.md
+include modifications.tsv
+include amino_acids.tsv
 recursive-include alphapept *
 recursive-exclude * __pycache__

README.md

@@ -6,6 +6,7 @@
 ![Quick Test](https://github.com/MannLabs/alphapept/workflows/Quick%20Test/badge.svg)
 ![Performance test](https://github.com/MannLabs/alphapept/workflows/Performance%20test/badge.svg)
 ![Windows Installer](https://github.com/MannLabs/alphapept/workflows/Windows%20Installer/badge.svg)
+[![launch - renku](https://renkulab.io/renku-badge.svg)](https://renkulab.io/projects/renku-stories/alphapept-gui-streamlit)
 
 [![DOI:10.1101/2021.07.23.453379](http://img.shields.io/badge/DOI-10.1101/2021.07.23.453379-B31B1B.svg)](https://www.biorxiv.org/content/10.1101/2021.07.23.453379v1)
 

alphapept/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.4.5"
+__version__ = "0.4.6"
 
 __requirements__ = {
     "": "requirements/requirements.txt",

alphapept/__version__.py

@@ -33,7 +33,7 @@
 AUTHOR_EMAIL = "straussmaximilian@gmail.com"
 COPYRIGHT = "Mann Labs"
 BRANCH = "master"
-VERSION_NO = "0.4.5"
+VERSION_NO = "0.4.6"
 MIN_PYTHON = "3.6"
 MAX_PYTHON = "4"
 AUDIENCE = "Developers"

alphapept/_nbdev.py

@@ -6,6 +6,30 @@
          "load_settings": "00_settings.ipynb",
          "load_settings_as_template": "00_settings.ipynb",
          "save_settings": "00_settings.ipynb",
+         "SETTINGS_TEMPLATE": "00_settings.ipynb",
+         "workflow": "00_settings.ipynb",
+         "general": "00_settings.ipynb",
+         "experiment": "00_settings.ipynb",
+         "raw": "00_settings.ipynb",
+         "fasta": "00_settings.ipynb",
+         "modfile_path": "10_constants.ipynb",
+         "mod_db": "00_settings.ipynb",
+         "mods": "00_settings.ipynb",
+         "mods_terminal": "00_settings.ipynb",
+         "mods_protein": "00_settings.ipynb",
+         "proteases": "00_settings.ipynb",
+         "features": "00_settings.ipynb",
+         "search": "00_settings.ipynb",
+         "score": "11_interface.ipynb",
+         "calibration": "00_settings.ipynb",
+         "matching": "00_settings.ipynb",
+         "quantification": "11_interface.ipynb",
+         "hash_file": "00_settings.ipynb",
+         "HOME": "00_settings.ipynb",
+         "AP_PATH": "00_settings.ipynb",
+         "DEFAULT_SETTINGS_PATH": "00_settings.ipynb",
+         "skip": "00_settings.ipynb",
+         "previous_md5": "00_settings.ipynb",
          "IsotopeDistribution": "01_chem.ipynb",
          "fast_add": "01_chem.ipynb",
          "numba_bin": "01_chem.ipynb",
@@ -145,7 +169,6 @@
          "add_column": "05_search.ipynb",
          "remove_column": "05_search.ipynb",
          "get_hits": "05_search.ipynb",
-         "score": "11_interface.ipynb",
          "LOSS_DICT": "05_search.ipynb",
          "LOSSES": "05_search.ipynb",
          "get_sequences": "05_search.ipynb",
@@ -218,6 +241,7 @@
          "match_datasets": "09_matching.ipynb",
          "AAs": "10_constants.ipynb",
          "get_mass_dict": "10_constants.ipynb",
+         "aafile_path": "10_constants.ipynb",
          "Isotope": "10_constants.ipynb",
          "isotopes": "10_constants.ipynb",
          "averagine_aa": "10_constants.ipynb",
@@ -238,7 +262,6 @@
          "protein_grouping": "11_interface.ipynb",
          "match": "11_interface.ipynb",
          "read_label_intensity": "11_interface.ipynb",
-         "quantification": "11_interface.ipynb",
          "export": "11_interface.ipynb",
          "run_complete_workflow": "11_interface.ipynb",
          "extract_median_unique": "11_interface.ipynb",

alphapept/constants.py

@@ -1,239 +1,19 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: nbs/10_constants.ipynb (unless otherwise specified).
 
-__all__ = ['AAs', 'mass_dict', 'get_mass_dict', 'Isotope', 'spec', 'isotopes', 'averagine_aa', 'averagine_avg',
-           'protease_dict', 'loss_dict', 'LABEL', 'label_dict']
+__all__ = ['AAs', 'get_mass_dict', 'modfile_path', 'aafile_path', 'mass_dict', 'Isotope', 'spec', 'isotopes',
+           'averagine_aa', 'averagine_avg', 'protease_dict', 'loss_dict', 'LABEL', 'label_dict']
 
 # Cell
 AAs = set('ACDEFGHIKLMNPQRSTUVWY')
 
 # Cell
+
 from numba import types
 from numba.typed import Dict
-
-mass_dict = Dict.empty(key_type=types.unicode_type, value_type=types.float64)
-
-mass_dict["A"] = 71.0371138
-mass_dict["C"] = 103.0091845
-mass_dict["D"] = 115.0269431
-mass_dict["E"] = 129.0425931
-mass_dict["F"] = 147.0684139
-mass_dict["G"] = 57.02146373
-mass_dict["H"] = 137.0589119
-mass_dict["I"] = 113.084064
-mass_dict["K"] = 128.094963
-mass_dict["L"] = 113.084064
-mass_dict["M"] = 131.0404846
-mass_dict["N"] = 114.0429275
-mass_dict["P"] = 97.05276386
-mass_dict["Q"] = 128.0585775
-mass_dict["R"] = 156.101111
-mass_dict["S"] = 87.03202843
-mass_dict["T"] = 101.0476785
-mass_dict["U"] = 150.9536333957
-mass_dict["V"] = 99.06841392
-mass_dict["W"] = 186.079313
-mass_dict["Y"] = 163.0633286
-mass_dict["cC"] = 160.03064823
-mass_dict["oxM"] = 147.03539923000002
-mass_dict["aA"] = 113.04767849000001
-mass_dict["aC"] = 145.01974919
-mass_dict["aD"] = 157.03750779
-mass_dict["aE"] = 171.05315779
-mass_dict["aF"] = 189.07897859
-mass_dict["aG"] = 99.03202842
-mass_dict["aH"] = 179.06947659
-mass_dict["aI"] = 155.09462869
-mass_dict["aK"] = 170.10552769
-mass_dict["aL"] = 155.09462869
-mass_dict["aM"] = 173.05104929
-mass_dict["aN"] = 156.05349219000001
-mass_dict["aP"] = 139.06332855
-mass_dict["aQ"] = 170.06914219
-mass_dict["aR"] = 198.11167569
-mass_dict["aS"] = 129.04259312
-mass_dict["aT"] = 143.05824319
-mass_dict["aU"] = 192.9641980857
-mass_dict["aV"] = 141.07897861
-mass_dict["aW"] = 228.08987769
-mass_dict["aY"] = 205.07389329
-mass_dict["amA"] = 70.053098207
-mass_dict["amC"] = 102.02516890700001
-mass_dict["amD"] = 114.042927507
-mass_dict["amE"] = 128.058577507
-mass_dict["amF"] = 146.084398307
-mass_dict["amG"] = 56.037448137
-mass_dict["amH"] = 136.074896307
-mass_dict["amI"] = 112.100048407
-mass_dict["amK"] = 127.11094740700001
-mass_dict["amL"] = 112.100048407
-mass_dict["amM"] = 130.056469007
-mass_dict["amN"] = 113.05891190700001
-mass_dict["amP"] = 96.068748267
-mass_dict["amQ"] = 127.07456190700002
-mass_dict["amR"] = 155.117095407
-mass_dict["amS"] = 86.048012837
-mass_dict["amT"] = 100.06366290700001
-mass_dict["amU"] = 149.9696178027
-mass_dict["amV"] = 98.084398327
-mass_dict["amW"] = 185.095297407
-mass_dict["amY"] = 162.079313007
-mass_dict["pS"] = 166.99835935
-mass_dict["pT"] = 181.01400942
-mass_dict["pY"] = 243.02965952
-mass_dict["deamN"] = 115.026943093
-mass_dict["deamQ"] = 129.04259309300002
-mass_dict["cmC"] = 85.9826354
-mass_dict["pgE"] = 111.03202841000001
-mass_dict["pgQ"] = 111.03202840000002
-mass_dict["tmt0A"] = 295.1895917
-mass_dict["tmt0C"] = 327.1616624
-mass_dict["tmt0D"] = 339.179421
-mass_dict["tmt0E"] = 353.195071
-mass_dict["tmt0F"] = 371.2208918
-mass_dict["tmt0G"] = 281.17394163
-mass_dict["tmt0H"] = 361.2113898
-mass_dict["tmt0I"] = 337.2365419
-mass_dict["tmt0K"] = 352.2474409
-mass_dict["tmt0L"] = 337.2365419
-mass_dict["tmt0M"] = 355.1929625
-mass_dict["tmt0N"] = 338.1954054
-mass_dict["tmt0P"] = 321.20524176000004
-mass_dict["tmt0Q"] = 352.2110554
-mass_dict["tmt0R"] = 380.2535889
-mass_dict["tmt0S"] = 311.18450633
-mass_dict["tmt0T"] = 325.2001564
-mass_dict["tmt0U"] = 375.1061112957
-mass_dict["tmt0V"] = 323.22089182
-mass_dict["tmt0W"] = 410.2317909
-mass_dict["tmt0Y"] = 387.2158065
-mass_dict["tmt2A"] = 296.1929466
-mass_dict["tmt2C"] = 328.16501730000004
-mass_dict["tmt2D"] = 340.1827759
-mass_dict["tmt2E"] = 354.1984259
-mass_dict["tmt2F"] = 372.2242467
-mass_dict["tmt2G"] = 282.17729653000004
-mass_dict["tmt2H"] = 362.2147447
-mass_dict["tmt2I"] = 338.2398968
-mass_dict["tmt2K"] = 353.2507958
-mass_dict["tmt2L"] = 338.2398968
-mass_dict["tmt2M"] = 356.1963174
-mass_dict["tmt2N"] = 339.1987603
-mass_dict["tmt2P"] = 322.20859666
-mass_dict["tmt2Q"] = 353.21441030000005
-mass_dict["tmt2R"] = 381.25694380000004
-mass_dict["tmt2S"] = 312.18786123
-mass_dict["tmt2T"] = 326.2035113
-mass_dict["tmt2U"] = 376.1094661957
-mass_dict["tmt2V"] = 324.22424672
-mass_dict["tmt2W"] = 411.23514580000005
-mass_dict["tmt2Y"] = 388.2191614
-mass_dict["tmt6A"] = 300.200046
-mass_dict["tmt6C"] = 332.1721167
-mass_dict["tmt6D"] = 344.1898753
-mass_dict["tmt6E"] = 358.2055253
-mass_dict["tmt6F"] = 376.2313461
-mass_dict["tmt6G"] = 286.18439593
-mass_dict["tmt6H"] = 366.2218441
-mass_dict["tmt6I"] = 342.2469962
-mass_dict["tmt6K"] = 357.2578952
-mass_dict["tmt6L"] = 342.2469962
-mass_dict["tmt6M"] = 360.2034168
-mass_dict["tmt6N"] = 343.2058597
-mass_dict["tmt6P"] = 326.21569606
-mass_dict["tmt6Q"] = 357.2215097
-mass_dict["tmt6R"] = 385.2640432
-mass_dict["tmt6S"] = 316.19496062999997
-mass_dict["tmt6T"] = 330.2106107
-mass_dict["tmt6U"] = 380.11656559569997
-mass_dict["tmt6V"] = 328.23134612
-mass_dict["tmt6W"] = 415.2422452
-mass_dict["tmt6Y"] = 392.2262608
-mass_dict["itraq4KA"] = 215.13952419999998
-mass_dict["itraq4KC"] = 247.1115949
-mass_dict["itraq4KD"] = 259.1293535
-mass_dict["itraq4KE"] = 273.14500350000003
-mass_dict["itraq4KF"] = 291.1708243
-mass_dict["itraq4KG"] = 201.12387413
-mass_dict["itraq4KH"] = 281.1613223
-mass_dict["itraq4KI"] = 257.1864744
-mass_dict["itraq4KK"] = 272.1973734
-mass_dict["itraq4KL"] = 257.1864744
-mass_dict["itraq4KM"] = 275.142895
-mass_dict["itraq4KN"] = 258.1453379
-mass_dict["itraq4KP"] = 241.15517426
-mass_dict["itraq4KQ"] = 272.1609879
-mass_dict["itraq4KR"] = 300.2035214
-mass_dict["itraq4KS"] = 231.13443883
-mass_dict["itraq4KT"] = 245.15008890000001
-mass_dict["itraq4KU"] = 295.0560437957
-mass_dict["itraq4KV"] = 243.17082432
-mass_dict["itraq4KW"] = 330.1817234
-mass_dict["itraq4KY"] = 307.16573900000003
-mass_dict["itraq4K"] = 272.1973734
-mass_dict["itraq4Y"] = 307.16573900000003
-mass_dict["itraq8KA"] = 375.2393133
-mass_dict["itraq8KC"] = 407.211384
-mass_dict["itraq8KD"] = 419.2291426
-mass_dict["itraq8KE"] = 433.2447926
-mass_dict["itraq8KF"] = 451.2706134
-mass_dict["itraq8KG"] = 361.22366323
-mass_dict["itraq8KH"] = 441.2611114
-mass_dict["itraq8KI"] = 417.2862635
-mass_dict["itraq8KK"] = 432.2971625
-mass_dict["itraq8KL"] = 417.2862635
-mass_dict["itraq8KM"] = 435.2426841
-mass_dict["itraq8KN"] = 418.245127
-mass_dict["itraq8KP"] = 401.25496336000003
-mass_dict["itraq8KQ"] = 432.260777
-mass_dict["itraq8KR"] = 460.3033105
-mass_dict["itraq8KS"] = 391.23422793
-mass_dict["itraq8KT"] = 405.249878
-mass_dict["itraq8KU"] = 455.1558328957
-mass_dict["itraq8KV"] = 403.27061342
-mass_dict["itraq8KW"] = 490.2815125
-mass_dict["itraq8KY"] = 467.2655281
-mass_dict["itraq8K"] = 432.2971625
-mass_dict["itraq8Y"] = 467.2655281
-mass_dict["eA"] = 337.1209813
-mass_dict["eC"] = 369.093052
-mass_dict["eD"] = 381.1108106
-mass_dict["eE"] = 395.1264606
-mass_dict["eF"] = 413.1522814
-mass_dict["eG"] = 323.10533123
-mass_dict["eH"] = 403.1427794
-mass_dict["eI"] = 379.1679315
-mass_dict["eK"] = 394.1788305
-mass_dict["eL"] = 379.1679315
-mass_dict["eM"] = 397.1243521
-mass_dict["eN"] = 380.126795
-mass_dict["eP"] = 363.13663136
-mass_dict["eQ"] = 394.142445
-mass_dict["eR"] = 422.1849785
-mass_dict["eS"] = 353.11589592999997
-mass_dict["eT"] = 367.131546
-mass_dict["eU"] = 417.03750089569996
-mass_dict["eV"] = 365.15228142
-mass_dict["eW"] = 452.1631805
-mass_dict["eY"] = 429.1471961
-mass_dict["arg10R"] = 166.10938057776002
-mass_dict["arg6R"] = 162.121241
-mass_dict["lys8K"] = 136.10916278888
-mass_dict["Electron"] = 0.00054857990907
-mass_dict["Proton"] = 1.00727646687
-mass_dict["Hydrogen"] = 1.00782503223
-mass_dict["C13"] = 13.003354835
-mass_dict["Oxygen"] = 15.994914619
-mass_dict["OH"] = 17.002739651229998
-mass_dict["H2O"] = 18.01056468346
-
-mass_dict["NH3"] = 17.03052
-mass_dict["delta_M"] = 1.00286864
-mass_dict["delta_S"] = 0.0109135
-
-# Cell
+import os
 
 #generates the mass dictionary from table
-def get_mass_dict(modfile:str="../modifications.tsv", aasfile: str="../amino_acids.tsv", verbose:bool=True):
+def get_mass_dict(modfile:str, aasfile: str, verbose:bool=True):
     """
     Function to create a mass dict based on tsv files.
     This is used to create the hardcoded dict in the constants notebook.
@@ -304,6 +84,16 @@ def get_mass_dict(modfile:str="../modifications.tsv", aasfile: str="../amino_aci
 
     return mass_dict
 
+try:
+    base = os.path.dirname(os.path.abspath(__file__)) #Cant do this in notebook
+except NameError:
+    base = os.path.join(os.pardir, 'alphapept')
+
+modfile_path = os.path.join(base, "modifications.tsv")
+aafile_path = os.path.join(base, "amino_acids.tsv")
+
+mass_dict = get_mass_dict(modfile=modfile_path, aasfile=aafile_path, verbose=False)
+
 # Cell
 import numpy as np
 from numba import int32, float32, float64, njit, types
@@ -381,6 +171,7 @@ def __init__(self, m0:int, dm:int, intensities:np.ndarray):
 protease_dict["hydroxylamine"] = "N(?=G)"
 protease_dict["iodosobenzoic acid"] = "W"
 protease_dict["lysc"] = "K"
+protease_dict["lysn"] = "N"
 protease_dict["ntcb"] = "\w(?=C)"
 protease_dict["pepsin ph1.3"] = "((?<=[^HKR][^P])[^R](?=[FL][^P]))|((?<=[^HKR][^P])[FL](?=\w[^P]))"
 protease_dict["pepsin ph2.0"] = "((?<=[^HKR][^P])[^R](?=[FLWY][^P]))|((?<=[^HKR][^P])[FLWY](?=\w[^P]))"