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+ "P", + "V", + "E", + "D", + "N", + "E", + "K", + "D", + "L", + "V", + "K", + "L", + "A", + "K", + "R", + "L", + "K", + "K", + "E", + "K", + "V", + "N", + "V", + "D", + "I", + "I", + "N", + "F", + "G", + "E", + "E", + "E", + "V", + "N", + "T", + "E", + "K", + "L", + "T", + "A", + "F", + "V", + "N", + "T", + "L", + "N", + "G", + "K", + "D", + "G", + "T", + "G", + "S", + "H", + "L", + "V", + "T", + "V", + "P", + "P", + "G", + "P", + "S", + "L", + "A", + "D", + "A", + "L", + "I", + "S", + "S", + "P", + "I", + "L", + "A", + "G", + "E", + "G", + "G", + "A", + "M", + "L", + "G", + "L", + "G", + "A", + "S", + "D", + "F", + "E", + "F", + "G", + "V", + "D", + "P", + "S", + "A", + "D", + "P", + "E", + "L", + "A", + "L", + "A", + "L", + "R", + "V", + "S", + "M", + "E", + "E", + "Q", + "R", + "Q", + "R", + "Q", + "E", + "E", + "E", + "A", + "R", + "R", + "A", + "A", + "A", + "A", + "S", + "A", + "A", + "E", + "A", + "G", + "I", + "A", + "T", + "T", + "G", + "T", + "E", + "D", + "S", + "D", + "D", + "A", + "L", + "L", + "K", + "M", + "T", + "I", + "S", + "Q", + "Q", + "E", + "F", + "G", + "R", + "T", + "G", + "L", + "P", + "D", + "L", + "S", + "S", + "M", + "T", + "E", + "E", + "E", + "Q", + "I", + "A", + "Y", + "A", + "M", + "Q", + "M", + "S", + "L", + "Q", + "G", + "A", + "E", + "F", + "G", + "Q", + "A", + "E", + "S", + "A", + "D", + "I", + "D", + "A", + "S", + "S", + "A", + "M", + "D", + "T", + "S", + "E", + "P", + "A", + "K", + "E", + "E", + "D", + "D", + "Y", + "D", + "V", + "M", + "Q", + "D", + "P", + "E", + "F", + "L", + "Q", + "S", + "V", + "L", + "E", + "N", + "L", + "P", + "G", + "V", + "D", + "P", + "N", + "N", + "E", + "A", + "I", + "R", + "N", + "A", + "M", + "G", + "S", + "L", + "A", + "S", + "Q", + "A", + "T", + "K", + "D", + "G", + "K", + "K", + "D", + "K", + "K", + "E", + "E", + "D", + "K", + "K" + ], + "tickvals": [ + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 107, + 108, + 109, + 110, + 111, + 112, + 113, + 114, + 115, + 116, + 117, + 118, + 119, + 120, + 121, + 122, + 123, + 124, + 125, + 126, + 127, + 128, + 129, + 130, + 131, + 132, + 133, + 134, + 135, + 136, + 137, + 138, + 139, + 140, + 141, + 142, + 143, + 144, + 145, + 146, + 147, + 148, + 149, + 150, + 151, + 152, + 153, + 154, + 155, + 156, + 157, + 158, + 159, + 160, + 161, + 162, + 163, + 164, + 165, + 166, + 167, + 168, + 169, + 170, + 171, + 172, + 173, + 174, + 175, + 176, + 177, + 178, + 179, + 180, + 181, + 182, + 183, + 184, + 185, + 186, + 187, + 188, + 189, + 190, + 191, + 192, + 193, + 194, + 195, + 196, + 197, + 198, + 199, + 200, + 201, + 202, + 203, + 204, + 205, + 206, + 207, + 208, + 209, + 210, + 211, + 212, + 213, + 214, + 215, + 216, + 217, + 218, + 219, + 220, + 221, + 222, + 223, + 224, + 225, + 226, + 227, + 228, + 229, + 230, + 231, + 232, + 233, + 234, + 235, + 236, + 237, + 238, + 239, + 240, + 241, + 242, + 243, + 244, + 245, + 246, + 247, + 248, + 249, + 250, + 251, + 252, + 253, + 254, + 255, + 256, + 257, + 258, + 259, + 260, + 261, + 262, + 263, + 264, + 265, + 266, + 267, + 268, + 269, + 270, + 271, + 272, + 273, + 274, + 275, + 276, + 277, + 278, + 279, + 280, + 281, + 282, + 283, + 284, + 285, + 286, + 287, + 288, + 289, + 290, + 291, + 292, + 293, + 294, + 295, + 296, + 297, + 298, + 299, + 300, + 301, + 302, + 303, + 304, + 305, + 306, + 307, + 308, + 309, + 310, + 311, + 312, + 313, + 314, + 315, + 316, + 317, + 318, + 319, + 320, + 321, + 322, + 323, + 324, + 325, + 326, + 327, + 328, + 329, + 330, + 331, + 332, + 333, + 334, + 335, + 336, + 337, + 338, + 339, + 340, + 341, + 342, + 343, + 344, + 345, + 346, + 347, + 348, + 349, + 350, + 351, + 352, + 353, + 354, + 355, + 356, + 357, + 358, + 359, + 360, + 361, + 362, + 363, + 364, + 365, + 366, + 367, + 368, + 369, + 370, + 371, + 372, + 373, + 374, + 375, + 376, + 377 + ], + "title": { + "text": "protein sequence" + }, + "type": "linear", + "zeroline": false + }, + "xaxis2": { + "anchor": "y", + "matches": "x", + "range": [ + -10, + 387 + ], + "showgrid": false, + "side": "top", + "tickangle": 0, + "tickformat": ".d", + "tickmode": "auto", + "title": { + "text": "AA position" + }, + "type": "linear", + "zeroline": false + }, + "yaxis": { + "automargin": true, + "range": [ + -1, + 6.7 ], "showgrid": false, "showticklabels": true, @@ -36632,13 +80147,21 @@ "proteome
(9% coverage)", "Chain", "Domain", - "Secondary structure" + "Secondary structure", + "AlphaFold IDR", + "AlphaFold secondary structures", + "AlphaFold confidence", + "AlphaFold exposure" ], "tickvals": [ 0, 2, 2.5, - 3 + 3, + 4.5, + 5, + 5.5, + 6 ], "title": { "text": "" @@ -36648,9 +80171,9 @@ } }, "text/html": [ - "
+ + + + + + + +
+ +

+ + +
+ +
+ + + + + """ + + html_template = html_template.replace('your_file.cif',protein+'_'+data_name+'_mod.cif') + + if coloring == 'AlphaFold confidence': + color = 'af' + elif coloring == 'AlphaFold exposure': + color = 'exposure' + elif coloring == 'MS peptides': + color = 'aa' + elif coloring == 'MS modified peptides': + color = 'aa_ptm' + elif coloring == 'MS PTMs': + color = 'ptm' + elif coloring == 'AlphaFold IDR': + color = 'idr' + elif coloring == 'MS PTMs on AlphaFold IDR': + color = 'idr_ptm' + elif coloring == 'AlphaFold secondary structures': + color = 'structure_group' + else: + color = 'af' + + html = html_template.replace('globalThis.color_choice = your_choice',f"globalThis.color_choice = '{color}'") + + return html + +# Cell +def get_ms_concensus(ms_list): + unique_ms_list = set(ms_list) + if len(unique_ms_list)==1: + out = unique_ms_list + else: + out = [i for i in unique_ms_list if i != 'detected'] + + out = ';'.join(out) + + return out + +# Cell +import alphamap +def plot_3d_structure(df: pd.DataFrame or list, + name: str, + protein: str, + fasta: fasta, + selected_coloring: str, + dashboard: bool = False, + download_folder: str = tempfile.gettempdir()): + + """ + Function to generate the sequence plot. + + Args: + df (pd.DataFrame/list): Single dataframe or list of dataframes containing the datasets to plot. + name (str): Single string containing the name of the MS dataset in df. + protein (str): Uniprot protein accession. + fasta (fasta): Fasta file imported by pyteomics 'fasta.IndexedUniProt'. + selected_coloring (str): Coloring to show. + dashboard (bool, optional): Flag if the function is called from the dashboard. Default is 'False'. + + Returns: + go.Figure: 3D plot. + + """ + + if isinstance(df, pd.DataFrame): + df_plot = get_plot_data(protein=protein, + df = df, + fasta = fasta) + elif isinstance(df, list): + df_plot = [get_plot_data(protein=protein, + df = d, + fasta = fasta) for d in df] + df_plot = pd.concat(df_plot) + + if df_plot is None: + MS_annotation = pd.DataFrame({'position' : [], 'MS data': []}) + else: + MS_annotation = extract_annotation(df_plot) + MS_annotation = MS_annotation.groupby('position')['MS data'].apply(list).reset_index(name='MS data') + MS_annotation['MS data'] = MS_annotation['MS data'].apply(lambda x: get_ms_concensus(x)) + + cif_available = manipulate_cif(protein = protein, + MS_data = MS_annotation, + download_folder = download_folder) + + BASE_PATH = os.path.dirname(alphamap.__file__) + js_path = os.path.join(BASE_PATH, 'js') + cif_path = download_folder + + + import shutil + new_file = os.path.join(cif_path, protein+'_'+name+'_mod.cif') + try: + os.remove(new_file) + except: + pass + shutil.copyfile(src = os.path.join(cif_path, protein+'_mod.cif'), + dst = new_file) + + if cif_available: + mod_html = adjust_html(protein=protein, + coloring=selected_coloring, + data_name=name) + else: + mod_html = f""" + +
No AlphaFold structure available for {protein}.
+ """ + + + + return mod_html, js_path, cif_path + # Cell from .pdflib import * diff --git a/docs/SequencePlot.html b/docs/SequencePlot.html index bf54d06..9c0af21 100644 --- a/docs/SequencePlot.html +++ b/docs/SequencePlot.html @@ -26,6 +26,13 @@
+
+ {% endraw %} + + {% raw %} + +
+
{% endraw %} @@ -46,7 +53,7 @@

Format uniprot annotation -

format_uniprot_annotation[source]

format_uniprot_annotation(uniprot_ann:DataFrame, uniprot_feature_dict:dict)

+

format_uniprot_annotation[source]

format_uniprot_annotation(uniprot_ann:DataFrame, uniprot_feature_dict:dict)

Function to format uniprot annotation for plotting.

Args: @@ -96,7 +103,7 @@

Format data for plotting -

get_plot_data[source]

get_plot_data(protein, df, fasta)

+

get_plot_data[source]

get_plot_data(protein, df, fasta)

Function to format experimental data for plotting.

Args: @@ -140,7 +147,7 @@

Function to plot a single dataset -

plot_single_peptide_traces[source]

plot_single_peptide_traces(df_plot, protein, fasta)

+

plot_single_peptide_traces[source]

plot_single_peptide_traces(df_plot, protein, fasta)

Function to plot single peptide trace.

Args: @@ -198,6 +205,13 @@

Plotting function for the
+
+ {% endraw %} + + {% raw %} + +
+
@@ -205,7 +219,122 @@

Plotting function for the
-

plot_peptide_traces[source]

plot_peptide_traces(df:DataFrame, name:str, protein:str, fasta:pyteomics.fasta, uniprot:DataFrame, selected_features:list, uniprot_feature_dict:dict, uniprot_color_dict:dict, selected_proteases:list=[], dashboard:bool=False, trace_colors:list=[])

+

get_quality_category[source]

get_quality_category(s)

+
+ +
+ +

+ +
+
+ +

+ {% endraw %} + + {% raw %} + +
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+
+ +
+ + +
+

get_exposure_category[source]

get_exposure_category(s)

+
+ +
+ +
+ +
+
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+
+ +
+ + +
+

get_alphafold_annotation[source]

get_alphafold_annotation(protein:str, selected_features:list, download_folder:str='/var/folders/zx/n29r0swn0hddt1qgy159sjrw0000gn/T')

+
+ +
+ +
+ +
+
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+ {% endraw %} + + {% raw %} + +
+
+ +
+
+
# af_annotation = get_alphafold_annotation(protein= "Q00266", 
+#                              selected_features= ["AlphaFold confidence",  
+#                                                    "AlphaFold exposure", 
+#                                                    "AlphaFold IDR", 
+#                                                    "AlphaFold secondary structures"], 
+#                              download_folder= '/Users/isabell/Downloads/') 
+
+ +
+
+
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+
+ +
+ + +
+

plot_peptide_traces[source]

plot_peptide_traces(df:DataFrame, name:str, protein:str, fasta:pyteomics.fasta, uniprot:DataFrame, selected_features:list, uniprot_feature_dict:dict, uniprot_color_dict:dict, selected_proteases:list=[], selected_alphafold_features:list=[], dashboard:bool=False, trace_colors:list=[], download_folder:str='/var/folders/zx/n29r0swn0hddt1qgy159sjrw0000gn/T')

Function to generate the sequence plot.

Args: @@ -237,6 +366,222 @@

plot_peptide_traces
+
+ {% endraw %} + +
+
+

3D visualization

+
+
+
+ {% raw %} + +
+ +
+
+ +
+ + +
+

extract_annotation[source]

extract_annotation(df:DataFrame)

+
+ +
+ +
+ +
+
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+
+ +
+ + +
+

manipulate_cif[source]

manipulate_cif(protein:str, MS_data:DataFrame, download_folder:str='/var/folders/zx/n29r0swn0hddt1qgy159sjrw0000gn/T')

+
+ +
+ +
+ +
+
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+
+ +
+ + +
+

adjust_html[source]

adjust_html(protein:str, coloring:str, data_name:str)

+
+ +
+ +
+ +
+
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+
+ +
+ + +
+

get_ms_concensus[source]

get_ms_concensus(ms_list)

+
+ +
+ +
+ +
+
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+
+ +
+ + +
+

plot_3d_structure[source]

plot_3d_structure(df:DataFrame, name:str, protein:str, fasta:pyteomics.fasta, selected_coloring:str, dashboard:bool=False, download_folder:str='/var/folders/zx/n29r0swn0hddt1qgy159sjrw0000gn/T')

+
+

Function to generate the sequence plot.

+

Args: + df (pd.DataFrame/list): Single dataframe or list of dataframes containing the datasets to plot. + name (str): Single string containing the name of the MS dataset in df. + protein (str): Uniprot protein accession. + fasta (fasta): Fasta file imported by pyteomics 'fasta.IndexedUniProt'. + selected_coloring (str): Coloring to show. + dashboard (bool, optional): Flag if the function is called from the dashboard. Default is 'False'.

+

Returns: + go.Figure: 3D plot.

+ +
+ +
+ +
+
+ +
+ {% endraw %} + + {% raw %} + +
+ +
+ {% endraw %} + + {% raw %} + +
+
+ +
+
+
# 'alphafold_quality'
+# 'alphafold_exposure'
+# 'alphafold_IDR'
+# 'alphafold_secondary_structures'
+# 'MS data'
+# 'MS PTMs'
+# 'MS amino acids'
+
+ +
+
+
+ +
+ {% endraw %} + + {% raw %} + +
+
+ +
+
+
# plot_3d_structure(df = formatted_diann_data,
+#                     name = 'proteome',
+#                     protein = "P23284", #Q9H7C9, P23284
+#                     fasta = human_fasta,
+#                     selected_coloring = 'AlphaFold quality', #'AlphaFold exposure', #'AlphaFold quality', 'MS data'
+#                     dashboard = False)
+
+ +
+
+
+
{% endraw %} @@ -257,7 +602,7 @@

Create a pdf report -

create_pdf_report[source]

create_pdf_report(proteins:list, df:DataFrame, name:str, fasta:pyteomics.fasta, uniprot:DataFrame, selected_features:list, uniprot_feature_dict:dict, uniprot_color_dict:dict, selected_proteases:list=[], trace_colors:list=[])

+

create_pdf_report[source]

create_pdf_report(proteins:list, df:DataFrame, name:str, fasta:pyteomics.fasta, uniprot:DataFrame, selected_features:list, uniprot_feature_dict:dict, uniprot_color_dict:dict, selected_proteases:list=[], trace_colors:list=[])

Function to write pdf reports for selected proteins

Args: diff --git a/misc/bumpversion.cfg b/misc/bumpversion.cfg index 7ae1f59..6ce7c72 100644 --- a/misc/bumpversion.cfg +++ b/misc/bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.1.5 +current_version = 0.1.7 commit = False tag = False parse = (?P\d+)\.(?P\d+)\.(?P\d+)(\-(?P[a-z]+)(?P\d+))? diff --git a/misc/one_click_macos/Info.plist b/misc/one_click_macos/Info.plist index 4ccf970..024a33c 100644 --- a/misc/one_click_macos/Info.plist +++ b/misc/one_click_macos/Info.plist @@ -9,9 +9,9 @@ CFBundleIconFile alpha_logo.icns CFBundleIdentifier - alphamap.0.1.5 + alphamap.0.1.7 CFBundleShortVersionString - 0.1.5 + 0.1.7 CFBundleInfoDictionaryVersion 6.0 CFBundleName diff --git a/misc/one_click_macos/create_installer_macos.sh b/misc/one_click_macos/create_installer_macos.sh index 98adde7..8109fe0 100644 --- a/misc/one_click_macos/create_installer_macos.sh +++ b/misc/one_click_macos/create_installer_macos.sh @@ -17,7 +17,7 @@ rm -rf build python setup.py sdist bdist_wheel cd misc/one_click_macos pip install pyinstaller==4.2 -pip install "../../dist/alphamap-0.1.5-py3-none-any.whl" +pip install "../../dist/alphamap-0.1.7-py3-none-any.whl" conda list pyinstaller ../pyinstaller/alphamap.spec -y conda deactivate @@ -37,7 +37,7 @@ if false; then for f in $(find dist/alphamap -name '*.so' -or -name '*.dylib'); do codesign --sign "Developer ID Application: Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (7QSY5527AQ)" $f; done codesign --sign "Developer ID Application: Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (7QSY5527AQ)" dist/alphamap/Contents/MacOS/alphamap_gui --force --options=runtime --entitlements entitlements.xml codesign --sign "Developer ID Application: Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (7QSY5527AQ)" dist/alphamap/Contents/MacOS/kaleido/executable/bin/kaleido --force --options=runtime --entitlements entitlements.xml - pkgbuild --root dist/alphamap --identifier de.mpg.biochem.alphamap.app --version 0.1.5 --install-location /Applications/AlphaMap.app --scripts scripts alphamap.pkg --sign "Developer ID Installer: Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (7QSY5527AQ)" + pkgbuild --root dist/alphamap --identifier de.mpg.biochem.alphamap.app --version 0.1.7 --install-location /Applications/AlphaMap.app --scripts scripts alphamap.pkg --sign "Developer ID Installer: Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (7QSY5527AQ)" productbuild --distribution distribution.xml --resources Resources --package-path alphamap.pkg dist/alphamap_gui_installer_macos.pkg --sign "Developer ID Installer: Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (7QSY5527AQ)" requestUUID=$(xcrun altool --notarize-app --primary-bundle-id "de.mpg.biochem.alphamap.app" --username "willems@biochem.mpg.de" --password "@keychain:Alphatims-develop" --asc-provider 7QSY5527AQ --file dist/alphamap_gui_installer_macos.pkg 2>&1 | awk '/RequestUUID/ { print $NF; }') request_status="in progress" @@ -49,6 +49,6 @@ if false; then xcrun altool --notarization-info "$requestUUID" --username "willems@biochem.mpg.de" --password "@keychain:Alphatims-develop" xcrun stapler staple dist/alphamap_gui_installer_macos.pkg else - pkgbuild --root dist/alphamap --identifier de.mpg.biochem.alphamap.app --version 0.1.5 --install-location /Applications/AlphaMap.app --scripts scripts alphamap.pkg + pkgbuild --root dist/alphamap --identifier de.mpg.biochem.alphamap.app --version 0.1.7 --install-location /Applications/AlphaMap.app --scripts scripts alphamap.pkg productbuild --distribution distribution.xml --resources Resources --package-path alphamap.pkg dist/alphamap_gui_installer_macos.pkg fi diff --git a/misc/one_click_macos/distribution.xml b/misc/one_click_macos/distribution.xml index 3e9951f..77491ae 100755 --- a/misc/one_click_macos/distribution.xml +++ b/misc/one_click_macos/distribution.xml @@ -1,6 +1,6 @@ - AlphaMap 0.1.5 + AlphaMap 0.1.7 diff --git a/misc/one_click_windows/alphamap_innoinstaller.iss b/misc/one_click_windows/alphamap_innoinstaller.iss index 04616c2..fdfaf18 100644 --- a/misc/one_click_windows/alphamap_innoinstaller.iss +++ b/misc/one_click_windows/alphamap_innoinstaller.iss @@ -2,7 +2,7 @@ ; SEE THE DOCUMENTATION FOR DETAILS ON CREATING INNO SETUP SCRIPT FILES! #define MyAppName "AlphaMap" -#define MyAppVersion "0.1.5" +#define MyAppVersion "0.1.7" #define MyAppPublisher "Max Planck Institute of Biochemistry, Mann department" #define MyAppURL "https://github.com/MannLabs/alphamap" #define MyAppExeName "alphamap_gui.exe" diff --git a/misc/one_click_windows/create_installer_windows.bat b/misc/one_click_windows/create_installer_windows.bat index 735e4e1..16453a5 100644 --- a/misc/one_click_windows/create_installer_windows.bat +++ b/misc/one_click_windows/create_installer_windows.bat @@ -15,7 +15,7 @@ call rmdir dist /s /q call rmdir build /s /q call python setup.py sdist bdist_wheel call cd misc/one_click_windows -call pip install "../../dist/alphamap-0.1.5-py3-none-any.whl" +call pip install "../../dist/alphamap-0.1.7-py3-none-any.whl" call pip install pyinstaller==4.2 call pyinstaller ../pyinstaller/alphamap.spec -y call conda deactivate diff --git a/misc/one_click_windows/create_installer_windows.sh b/misc/one_click_windows/create_installer_windows.sh index f872ed0..7e52ad3 100644 --- a/misc/one_click_windows/create_installer_windows.sh +++ b/misc/one_click_windows/create_installer_windows.sh @@ -7,7 +7,7 @@ rm -rf dist rm -rf build python setup.py sdist bdist_wheel cd misc/one_click_windows -pip install "../../dist/alphamap-0.1.5-py3-none-any.whl" +pip install "../../dist/alphamap-0.1.7-py3-none-any.whl" pip install pyinstaller==4.2 # TODO https://stackoverflow.com/questions/54175042/python-3-7-anaconda-environment-import-ssl-dll-load-fail-error/60405693#60405693 pyinstaller ../pyinstaller/alphamap.spec -y diff --git a/nbs/Importing.ipynb b/nbs/Importing.ipynb index b5d2702..32e593a 100644 --- a/nbs/Importing.ipynb +++ b/nbs/Importing.ipynb @@ -1242,7 +1242,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" } diff --git a/nbs/Preprocessing.ipynb b/nbs/Preprocessing.ipynb index efd0098..19cee8e 100644 --- a/nbs/Preprocessing.ipynb +++ b/nbs/Preprocessing.ipynb @@ -519,7 +519,37 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " unique_protein_id modified_sequence naked_sequence \\\n", + "0 A0A024R161 PEPT[Phospho (STY)]IDER PEPTIDER \n", + "1 A0A087WT10 PEPT[Phospho (STY)]IDER PEPTIDER \n", + "2 A0A087WTH1 PEPT[Phospho (STY)]IDER PEPTIDER \n", + "3 A0A024R161 SEQ[GlyGly (K)]UENCE[GlyGly (K)]R SEQUENCER \n", + "4 A0A087WT10 SEQ[GlyGly (K)]UENCE[GlyGly (K)]R SEQUENCER \n", + "5 A0A087WTH5 VIEWER VIEWER \n", + "\n", + " all_protein_ids start end PTMsites \\\n", + "0 A0A024R161;A0A087WT10;A0A087WTH1 3 10 [3] \n", + "1 A0A024R161;A0A087WT10;A0A087WTH1 28 35 [3] \n", + "2 A0A024R161;A0A087WT10;A0A087WTH1 107 114 [3] \n", + "3 A0A024R161;A0A087WT10 95 103 [2, 7] \n", + "4 A0A024R161;A0A087WT10 150 158 [2, 7] \n", + "5 A0A087WTH5 1 6 [] \n", + "\n", + " PTMtypes \n", + "0 [[Phospho (STY)]] \n", + "1 [[Phospho (STY)]] \n", + "2 [[Phospho (STY)]] \n", + "3 [[GlyGly (K)], [GlyGly (K)]] \n", + "4 [[GlyGly (K)], [GlyGly (K)]] \n", + "5 [] \n" + ] + } + ], "source": [ "#hide\n", "\n", @@ -530,6 +560,7 @@ " assert \"Peptide sequence NONSEQ could not be mached\" in str(w[0].message)\n", " assert \"No matching entry for Nonsense\" in str(w[1].message) \n", " pd.testing.assert_frame_equal(res, test_df_modifications)\n", + " print(res)\n", "\n", "test_format_input_data()" ] @@ -583,7 +614,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" } diff --git a/nbs/SequencePlot.ipynb b/nbs/SequencePlot.ipynb index e83a043..17efd0b 100644 --- a/nbs/SequencePlot.ipynb +++ b/nbs/SequencePlot.ipynb @@ -28,6 +28,17 @@ "from alphamap.uniprot_integration import uniprot_feature_dict" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "import os\n", + "import tempfile" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -37,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -71,15 +82,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/opt/anaconda3/envs/alphamap/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3361: DtypeWarning: Columns (2) have mixed types.Specify dtype option on import or set low_memory=False.\n", - " if (await self.run_code(code, result, async_=asy)):\n" + "/var/folders/zx/n29r0swn0hddt1qgy159sjrw0000gn/T/ipykernel_44445/1606015572.py:17: DtypeWarning: Columns (2) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " test_format_uniprot_annotation()\n" ] } ], @@ -105,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -197,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -298,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -315,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -453,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -472,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -531,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -571,7 +582,171 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "from structuremap.processing import download_alphafold_cif, download_alphafold_pae, format_alphafold_data, annotate_accessibility, get_smooth_score" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "def get_quality_category(s):\n", + " if s >= 90:\n", + " n='Very high'\n", + " elif s >= 70:\n", + " n='Confident'\n", + " elif s >= 50:\n", + " n='Low'\n", + " else:\n", + " n='Very low'\n", + " return(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "def get_exposure_category(s):\n", + " if s > 10:\n", + " n='Surrounded'\n", + " elif s > 5:\n", + " n='Many neighbors'\n", + " elif s > 0:\n", + " n='Few neighbors'\n", + " else:\n", + " n='No neighbors'\n", + " return(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "def get_alphafold_annotation(protein: str, \n", + " selected_features: list, \n", + " download_folder: str = tempfile.gettempdir()) -> pd.DataFrame:\n", + " \n", + " alphafold_feature_dict = dict({'AlphaFold confidence':'quality', \n", + " 'AlphaFold exposure':'nAA_12_70_pae', \n", + " 'AlphaFold IDR':'AlphaFold IDR',\n", + " 'AlphaFold secondary structures':'structure_group'})\n", + " \n", + " alphafold_features = [i for i in [\n", + " 'AlphaFold confidence', \n", + " 'AlphaFold exposure', \n", + " 'AlphaFold IDR',\n", + " 'AlphaFold secondary structures'\n", + " ] if i in selected_features]\n", + "\n", + " if len(alphafold_features) > 0:\n", + "\n", + " # download cif file\n", + " valid_proteins, invalid_proteins, existing_proteins = download_alphafold_cif(\n", + " proteins=[protein],\n", + " out_folder=download_folder)\n", + " is_available = np.sum([len(valid_proteins),len(existing_proteins)])\n", + " \n", + " if is_available > 0:\n", + "\n", + " af_data = format_alphafold_data(directory = download_folder, protein_ids = [protein])\n", + "\n", + " if len([i for i in ['AlphaFold exposure',\n", + " 'AlphaFold IDR'] if i in selected_features]) > 0:\n", + " # download pae file\n", + " valid_pae, invalid_pae, existing_pae = download_alphafold_pae(\n", + " proteins=[protein],\n", + " out_folder=download_folder)\n", + " is_available_pae = np.sum([len(valid_pae),len(existing_pae)])\n", + " #@ToDo\n", + " #if is_available == 0:\n", + " # print('error')\n", + "\n", + " # annotate exposure\n", + " exposure_df = annotate_accessibility(df = af_data,\n", + " max_dist = 12,\n", + " max_angle = 70,\n", + " error_dir = download_folder)\n", + "\n", + " af_data = af_data.merge(exposure_df, how='left', on=['protein_id','AA','position'])\n", + " \n", + " # annotate exposure\n", + " full_exposure_df = annotate_accessibility(df = af_data,\n", + " max_dist = 24,\n", + " max_angle = 180,\n", + " error_dir = download_folder)\n", + "\n", + " af_data = af_data.merge(full_exposure_df, how='left', on=['protein_id','AA','position'])\n", + "\n", + " # smooth scores\n", + " smoothing_scores = []\n", + " if len([i for i in ['AlphaFold IDR'] if i in alphafold_features]) > 0:\n", + " smoothing_scores.append('nAA_24_180_pae')\n", + " if len(smoothing_scores) > 0: \n", + " af_data = get_smooth_score(af_data, np.array(smoothing_scores),[10])\n", + "\n", + " # annotate predicted IDRs\n", + " if 'AlphaFold IDR' in alphafold_features:\n", + " af_data['AlphaFold IDR'] = np.where(af_data['nAA_24_180_pae_smooth10']<=34.27, \"IDR\", \"folded\")\n", + "\n", + " af_columns = ['position','AA']\n", + " af_columns = af_columns + [alphafold_feature_dict[i] for i in alphafold_features]\n", + " af_data = af_data[af_columns]\n", + "\n", + " if 'quality' in af_data.columns:\n", + " af_data[\"AlphaFold confidence\"] = [get_quality_category(x) for x in af_data.quality] \n", + "\n", + " if 'nAA_12_70_pae' in af_data.columns:\n", + " af_data[\"AlphaFold exposure\"] = [get_exposure_category(x) for x in af_data.nAA_12_70_pae] \n", + " \n", + " if 'structure_group' in af_data.columns:\n", + " af_data = af_data.rename(columns={\"structure_group\": \"AlphaFold secondary structures\"})\n", + "\n", + " else:\n", + " af_data = pd.DataFrame({'AA' : []})\n", + " else:\n", + " af_data = pd.DataFrame({'AA' : []})\n", + " \n", + " return(af_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# #hide\n", + "# af_annotation = get_alphafold_annotation(protein= \"Q00266\", \n", + "# selected_features= [\"AlphaFold confidence\", \n", + "# \"AlphaFold exposure\", \n", + "# \"AlphaFold IDR\", \n", + "# \"AlphaFold secondary structures\"], \n", + "# download_folder= '/Users/isabell/Downloads/') " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -589,8 +764,10 @@ " uniprot_feature_dict: dict,\n", " uniprot_color_dict: dict,\n", " selected_proteases: list = [],\n", + " selected_alphafold_features: list = [],\n", " dashboard: bool = False,\n", - " trace_colors: list = []):\n", + " trace_colors: list = [],\n", + " download_folder: str = tempfile.gettempdir()):\n", "\n", " \"\"\"\n", " Function to generate the sequence plot.\n", @@ -635,6 +812,8 @@ " # subset for selected features\n", " uniprot_annotation_p_f_f = uniprot_annotation_p_f[uniprot_annotation_p_f.feature.isin(selected_features)]\n", "\n", + " #@ToDo if \n", + " \n", " if isinstance(df, pd.DataFrame):\n", " df_plot = get_plot_data(protein=protein,\n", " df = df,\n", @@ -757,6 +936,8 @@ " #hoverinfo='text'\n", " ))\n", " fig.update_layout(barmode='stack', bargap=0, hovermode='x unified',hoverdistance=1)\n", + " \n", + " y_max = y_max + (len(unique_features)/2)\n", "\n", " selected_proteases = sorted(selected_proteases)\n", " if len(selected_proteases) > 0:\n", @@ -772,7 +953,7 @@ " for s in sites:\n", " fig.add_trace(go.Bar(x=list(range(s+1,s+2)),\n", " y=[0.2],\n", - " base=y_max+(len(unique_features)/2)+(u/2)-0.1,\n", + " base=y_max+(u/2)-0.1,\n", " marker_color=\"grey\",\n", " opacity=0.8,\n", " showlegend=False,\n", @@ -783,7 +964,79 @@ " #hovertext=protease,\n", " #hoverinfo='text'\n", " ))\n", + " \n", + " y_max = y_max + (len(selected_proteases)/2)\n", + "\n", + " \n", + " alphafold_annotation = get_alphafold_annotation(protein = protein, \n", + " selected_features = selected_alphafold_features,\n", + " download_folder = download_folder)\n", + " \n", + " if alphafold_annotation.empty:\n", + " #print('no alphafold')\n", + " alphafold_features = []\n", + " else:\n", + " #print('alphafold')\n", + " \n", + " alphafold_features = [i for i in alphafold_annotation.columns if i in [\n", + " \"AlphaFold confidence\", \n", + " \"AlphaFold exposure\",\n", + " \"AlphaFold IDR\",\n", + " \"AlphaFold secondary structures\"]]\n", + " \n", + " alphafold_color_dict = {'HELX': '#5dabe8', \n", + " 'STRN': '#8cdbad',\n", + " 'TURN': '#e094bc',\n", + " 'BEND': '#fac682',\n", + " 'unstructured': '#aaaaaa',\n", + " 'Very high':'#4035de',\n", + " 'Confident':'#6aaff7',\n", + " 'Low':'#f5d44e',\n", + " 'Very low':'#ff8f4a',\n", + " 'No neighbors': \"#8FF5A2\",\n", + " 'Few neighbors': \"#38BFA7\",\n", + " 'Many neighbors': \"#367BC3\",\n", + " 'Surrounded': \"#524582\",\n", + " 'IDR': \"#B5B5B5\",\n", + " 'folded': \"#367BC3\"}\n", + " \n", + " y_max = y_max+1\n", + " \n", + " for j in range(0,len(alphafold_features)):\n", + " \n", + " figure_height = figure_height + 50\n", + " \n", + " af_column = alphafold_features[j]\n", + " if af_column == \"AlphaFold confidence\":\n", + " extra_annotation = alphafold_annotation[\"quality\"].values\n", + " elif af_column == \"AlphaFold exposure\":\n", + " extra_annotation = alphafold_annotation[\"nAA_12_70_pae\"].values\n", + " else:\n", + " extra_annotation = np.repeat(\"\",alphafold_annotation.shape[0])\n", + " \n", + " for i in range(0, alphafold_annotation.shape[0]):\n", + " start = alphafold_annotation.position[i]\n", + " end = alphafold_annotation.position[i] \n", "\n", + " alpha_col = alphafold_color_dict[alphafold_annotation[af_column][i]]\n", + "\n", + " fig.add_trace(go.Bar(x=list(range(start,end+1)), \n", + " y=list(np.repeat(0.2,end-start+1)),\n", + " base=y_max+(j/2)-0.1,\n", + " marker_color=alpha_col,\n", + " marker_line_width=0,\n", + " opacity=0.8,\n", + " showlegend=False,\n", + " xaxis='x2',\n", + " name='',\n", + " text=np.repeat(str(alphafold_annotation[af_column][i]) + \" \" + str(extra_annotation[i]),len(range(start,end+1))),\n", + " hovertemplate ='%{text}'\n", + " #hovertext=domain_info_sub.annotation[i],\n", + " #hoverinfo='text'\n", + " )) \n", + " \n", + " y_max = y_max + (len(alphafold_features)/2)\n", + " \n", " fig.add_trace(go.Scatter(x=np.arange(1,len(protein_sequence)+1,1),\n", " y=np.repeat(0,len(protein_sequence)),\n", " marker=dict(color='rgba(135, 206, 250, 0)'),\n", @@ -806,24 +1059,45 @@ "\n", " fig.update_layout(barmode='stack', bargap=0, hovermode='x unified',hoverdistance=1,\n", " width=plot_width, height=figure_height)\n", - "\n", + " \n", " mapped_feature_names = [uniprot_feature_dict_rev.get(key) for key in unique_features]\n", " if isinstance(df, pd.DataFrame):\n", - " fig.update_yaxes(showticklabels=True,\n", - " #tickvals= np.arange(0, 1+len(unique_features)+len(selected_proteases)),\n", - " tickvals= np.concatenate((np.array([0]),np.arange(1+1,1+1+(len(unique_features)/2),0.5),np.arange(1+(1*np.min([1,len(unique_features)]))+(len(unique_features)/2)+1,1+(1*np.min([1,len(unique_features)]))+(len(unique_features)/2)+1+(len(selected_proteases)/2),0.5))),\n", - " ticktext=np.hstack((np.array(trace_name),np.array(mapped_feature_names),np.array(selected_proteases))),\n", - " automargin=True,\n", - " range=[-1, y_max+(len(unique_features)/2)+(len(selected_proteases)/2)+0.2],\n", - " showgrid=False)\n", + " trace_ax = list([0])\n", + " trace_count = 1\n", " elif isinstance(df, list):\n", - " fig.update_yaxes(showticklabels=True,\n", - " #tickvals= 1 + np.arange(0, len(df_plot)+len(unique_features)+len(selected_proteases)),\n", - " tickvals= 1 + np.concatenate((np.array([0]),np.arange(1,len(df_plot),1),np.arange(len(df_plot)+1,len(df_plot)+1+(len(unique_features)/2),0.5),np.arange(len(df_plot)+1+(len(unique_features)/2)+(1*np.min([1,len(unique_features)])),len(df_plot)+1+(len(unique_features)/2)+(1*np.min([1,len(unique_features)]))+(len(selected_proteases)/2),0.5))),\n", - " ticktext=np.hstack((np.array(trace_name),np.array(mapped_feature_names),selected_proteases)),\n", - " automargin=True,\n", - " range=[0, y_max+(len(unique_features)/2)+(len(selected_proteases)/2)+0.2],\n", - " showgrid=False)\n", + " trace_ax = list(np.arange(1,len(df_plot)+1,1))\n", + " trace_count = len(df_plot) + 1\n", + " #print(trace_ax)\n", + " \n", + " if len(unique_features) > 0:\n", + " trace_count += 1\n", + " uniprot_ax = list(np.arange(0,len(unique_features)/2, 0.5) + trace_count) \n", + " trace_count = max(uniprot_ax)+0.5\n", + " #print(uniprot_ax)\n", + " else:\n", + " uniprot_ax = []\n", + " if len(selected_proteases) > 0:\n", + " trace_count += 1\n", + " protease_ax = list(np.arange(0, len(selected_proteases)/2,0.5) + trace_count) \n", + " trace_count = max(protease_ax)+0.5\n", + " #print(protease_ax)\n", + " else:\n", + " protease_ax = []\n", + " if len(alphafold_features) > 0:\n", + " trace_count += 1\n", + " alphafold_ax = list(np.arange(0, len(alphafold_features)/2,0.5) + trace_count) \n", + " trace_count = max(alphafold_ax)+0.5\n", + " #print(alphafold_ax)\n", + " else:\n", + " alphafold_ax = []\n", + "\n", + " fig.update_yaxes(showticklabels=True,\n", + " #tickvals= np.arange(0, 1+len(unique_features)+len(selected_proteases)),\n", + " tickvals= trace_ax + uniprot_ax + protease_ax + alphafold_ax,\n", + " ticktext=np.hstack((np.array(trace_name),np.array(mapped_feature_names),np.array(selected_proteases),np.array(alphafold_features))),\n", + " automargin=True,\n", + " range=[-1, y_max + 0.2],\n", + " showgrid=False)\n", "\n", " 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"trypsin" + "Few neighbors 3" ], "type": "bar", "x": [ - 56 + 95 ], "xaxis": "x2", "y": [ @@ -15203,20 +35677,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 1" ], "type": "bar", "x": [ - 66 + 96 ], "xaxis": "x2", "y": [ @@ -15224,20 +35701,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 2" ], "type": "bar", "x": [ - 70 + 97 ], "xaxis": "x2", "y": [ @@ -15245,20 +35725,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 2" ], "type": "bar", "x": [ - 75 + 98 ], "xaxis": "x2", "y": [ @@ -15266,20 +35749,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 1" ], "type": "bar", "x": [ - 78 + 99 ], "xaxis": "x2", "y": [ @@ -15287,20 +35773,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 2" ], "type": "bar", "x": [ - 82 + 100 ], "xaxis": "x2", "y": [ @@ -15308,20 +35797,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 2" ], "type": "bar", "x": [ - 83 + 101 ], "xaxis": "x2", "y": [ @@ -15329,20 +35821,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 2" ], "type": "bar", "x": [ - 88 + 102 ], "xaxis": "x2", "y": [ @@ -15350,20 +35845,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 3" ], "type": "bar", "x": [ - 104 + 103 ], "xaxis": "x2", "y": [ @@ -15371,20 +35869,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 1" ], "type": "bar", "x": [ - 109 + 104 ], "xaxis": "x2", "y": [ @@ -15392,20 +35893,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 2" ], "type": "bar", "x": [ - 129 + 105 ], "xaxis": "x2", "y": [ @@ -15413,20 +35917,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 1" ], "type": "bar", "x": [ - 147 + 106 ], "xaxis": "x2", "y": [ @@ -15434,20 +35941,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 2" ], "type": "bar", "x": [ - 162 + 107 ], "xaxis": "x2", "y": [ @@ -15455,20 +35965,23 @@ ] }, { - "base": 7.9, + "base": 10.9, "hovertemplate": "%{text}", "marker": { - "color": "grey" + "color": "#38BFA7", + "line": { + "width": 0 + } }, "name": "", "opacity": 0.8, "showlegend": false, "text": [ - "trypsin" + "Few neighbors 1" ], "type": "bar", "x": [ - 165 + 108 ], "xaxis": "x2", "y": [ @@ -15482,350 +35995,242 @@ }, "meta": [ "M", - "G", - "A", - "P", "E", - "P", - "T", - "I", "D", "E", - "R", - "P", - "L", - "L", - "S", - "P", - "G", - "W", - "G", - "A", - "G", - "A", - "A", - "G", - "R", - "R", - "W", - "W", - "M", - "L", - "L", - "A", - "P", - "L", - "L", - "P", - "A", - "L", - "L", - "L", - "V", - "R", - "P", - "A", - "G", + "E", + "K", "A", - "L", "V", "E", - "G", + "I", "L", - "Y", - "C", "G", + "N", "T", - "R", - "D", - "C", - "Y", "E", - "V", - "L", - "G", - "V", - "S", - "R", - "S", "A", - "G", - "K", "A", - "E", + "H", + "P", + "P", + "S", + "P", "I", - "A", - "R", - "A", - "Y", "R", - "Q", + "C", + "C", + "W", "L", - "A", - "R", - "R", - "Y", - "H", - "P", - "D", "R", - "Y", + "L", "R", - "P", - "Q", - "P", + "C", + "L", + "A", + "A", + "T", + "S", + "I", + "I", + "C", "G", - "D", + "C", "S", - "E", - "Q", - "U", - "E", - "N", "C", + "L", + "G", + "V", + "M", + "A", + "L", + "V", + "F", + "A", + "I", + "K", + "A", "E", - "R", "E", - "G", - "P", + "R", + "H", + "K", + "A", "G", "R", - "T", - "P", - "Q", "S", - "A", "E", "E", "A", - "F", - "L", - "L", "V", + "R", + "W", + "G", "A", - "T", + "R", "A", - "Y", - "E", - "T", - "L", + "R", "K", - "V", - "S", - "Q", - "A", + "L", + "I", + "L", "A", + "S", + "F", "A", - "E", + "V", + "W", "L", - "Q", - "Q", - "Y", - "C", - "M", - "Q", - "N", - "A", - "C", - "K", - "D", "A", + "V", "L", + "I", "L", - "V", "G", - "V", "P", - "A", - "G", + "L", + "L", + "L", + "W", + "L", + "L", "S", - "N", + "Y", + "A", + "I", + "A", + "Q", + "A", "P", - "F", - "R", "E", "P", + "T", + "I", + "D", + "E", "R", - "S", - "C", - "A", - "L", - "L" + "E" ], "mode": "markers", "name": "", "showlegend": false, "text": [ "Methionine", - "Glycine", - "Alanine", - "Proline", "Glutamic acid", - "Proline", - "Threonine", - "Isoleucine", "Aspartic acid", "Glutamic acid", - "Arginine", - "Proline", - "Leucine", - "Leucine", - "Serine", - "Proline", - "Glycine", - "Tryptophan", - "Glycine", + "Glutamic acid", + "Lysine", "Alanine", + "Valine", + "Glutamic acid", + "Isoleucine", + "Leucine", "Glycine", + "Asparagine", + "Threonine", + "Glutamic acid", "Alanine", "Alanine", - "Glycine", - "Arginine", - "Arginine", - "Tryptophan", - "Tryptophan", - "Methionine", - "Leucine", - "Leucine", - "Alanine", + "Histidine", "Proline", - "Leucine", - "Leucine", "Proline", - "Alanine", - "Leucine", + "Serine", + "Proline", + "Isoleucine", + "Arginine", + "Cysteine", + "Cysteine", + "Tryptophan", "Leucine", + "Arginine", "Leucine", - "Valine", "Arginine", - "Proline", + "Cysteine", + "Leucine", "Alanine", - "Glycine", "Alanine", - "Leucine", - "Valine", - "Glutamic acid", - "Glycine", - "Leucine", - "Tyrosine", + "Threonine", + "Serine", + "Isoleucine", + "Isoleucine", "Cysteine", "Glycine", - "Threonine", - "Arginine", - "Aspartic acid", "Cysteine", - "Tyrosine", - "Glutamic acid", - "Valine", + "Serine", + "Cysteine", "Leucine", "Glycine", "Valine", - "Serine", - "Arginine", - "Serine", + "Methionine", "Alanine", - "Glycine", - "Lysine", + "Leucine", + "Valine", + "Phenylalanine", "Alanine", - "Glutamic acid", "Isoleucine", + "Lysine", "Alanine", - "Arginine", - "Alanine", - "Tyrosine", - "Arginine", - "Glutamine", - "Leucine", - "Alanine", - "Arginine", - "Arginine", - "Tyrosine", - "Histidine", - "Proline", - "Aspartic acid", - "Arginine", - "Tyrosine", - "Arginine", - "Proline", - "Glutamine", - "Proline", - "Glycine", - "Aspartic acid", - "Serine", - "Glutamic acid", - "Glutamine", - "nan", "Glutamic acid", - "Asparagine", - "Cysteine", "Glutamic acid", "Arginine", - "Glutamic acid", - "Glycine", - "Proline", + "Histidine", + "Lysine", + "Alanine", "Glycine", "Arginine", - "Threonine", - "Proline", - "Glutamine", "Serine", - "Alanine", "Glutamic acid", "Glutamic acid", "Alanine", - "Phenylalanine", - "Leucine", - "Leucine", "Valine", + "Arginine", + "Tryptophan", + "Glycine", "Alanine", - "Threonine", + "Arginine", "Alanine", - "Tyrosine", - "Glutamic acid", - "Threonine", - "Leucine", + "Arginine", "Lysine", - "Valine", - "Serine", - "Glutamine", - "Alanine", + "Leucine", + "Isoleucine", + "Leucine", "Alanine", + "Serine", + "Phenylalanine", "Alanine", - "Glutamic acid", + "Valine", + "Tryptophan", "Leucine", - "Glutamine", - "Glutamine", - "Tyrosine", - "Cysteine", - "Methionine", - "Glutamine", - "Asparagine", - "Alanine", - "Cysteine", - "Lysine", - "Aspartic acid", "Alanine", + "Valine", "Leucine", + "Isoleucine", "Leucine", - "Valine", "Glycine", - "Valine", "Proline", - "Alanine", - "Glycine", + "Leucine", + "Leucine", + "Leucine", + "Tryptophan", + "Leucine", + "Leucine", "Serine", - "Asparagine", + "Tyrosine", + "Alanine", + "Isoleucine", + "Alanine", + "Glutamine", + "Alanine", "Proline", - "Phenylalanine", - "Arginine", "Glutamic acid", "Proline", + "Threonine", + "Isoleucine", + "Aspartic acid", + "Glutamic acid", "Arginine", - "Serine", - "Cysteine", - "Alanine", - "Leucine", - "Leucine" + "Glutamic acid" ], "type": "scatter", "x": [ @@ -15944,61 +36349,7 @@ 113, 114, 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170 + 116 ], "xaxis": "x2", "y": [ @@ -16117,60 +36468,6 @@ 0, 0, 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, 0 ] } @@ -16178,7 +36475,7 @@ "layout": { "bargap": 0, "barmode": "stack", - "height": 600, + "height": 800, "hoverdistance": 1, "hovermode": "x unified", "margin": { @@ -16187,7 +36484,7 @@ "r": 20, "t": 150 }, - "meta": 10, + "meta": 7, "plot_bgcolor": "rgba(0,0,0,0)", "shapes": [ { @@ -16196,56 +36493,8 @@ "width": 1 }, "type": "line", - "x0": 7, - "x1": 7, - "xref": "x2", - "y0": 1, - "y1": 1.3 - }, - { - "line": { - "color": "#023e8a", - "width": 1 - }, - "type": "line", - "x0": 98, - "x1": 98, - "xref": "x2", - "y0": 1, - "y1": 1.3 - }, - { - "line": { - "color": "#023e8a", - "width": 1 - }, - "type": "line", - "x0": 103, - "x1": 103, - "xref": "x2", - "y0": 1, - "y1": 1.3 - }, - { - "line": { - "color": "#023e8a", - "width": 1 - }, - "type": "line", - "x0": 7, - "x1": 7, - "xref": "x2", - "y0": 1, - "y1": 1.3 - }, - { - "line": { - "color": "#023e8a", - "width": 1 - }, - "type": "line", - "x0": 98, - "x1": 98, + "x0": 111, + "x1": 111, "xref": "x2", "y0": 1, "y1": 1.3 @@ -16256,8 +36505,8 @@ "width": 1 }, "type": "line", - "x0": 103, - "x1": 103, + "x0": 111, + "x1": 111, "xref": "x2", "y0": 1, "y1": 1.3 @@ -16268,32 +36517,8 @@ "width": 1 }, "type": "line", - "x0": 7, - "x1": 7, - "xref": "x2", - "y0": 2, - "y1": 2.3 - }, - { - "line": { - "color": "#0096c7", - "width": 1 - }, - "type": "line", - "x0": 98, - "x1": 98, - "xref": "x2", - "y0": 2, - "y1": 2.3 - }, - { - "line": { - "color": "#0096c7", - "width": 1 - }, - "type": "line", - "x0": 103, - "x1": 103, + "x0": 111, + "x1": 111, "xref": "x2", "y0": 2, "y1": 2.3 @@ -16304,32 +36529,8 @@ "width": 1 }, "type": "line", - "x0": 7, - "x1": 7, - "xref": "x2", - "y0": 3, - "y1": 3.3 - }, - { - "line": { - "color": "#90e0ef", - "width": 1 - }, - "type": "line", - "x0": 98, - "x1": 98, - "xref": "x2", - "y0": 3, - "y1": 3.3 - }, - { - "line": { - "color": "#90e0ef", - "width": 1 - }, - "type": "line", - "x0": 103, - "x1": 103, + "x0": 111, + "x1": 111, "xref": "x2", "y0": 3, "y1": 3.3 @@ -17141,7 +37342,7 @@ } }, "title": { - "text": "Sequence plot for: Guanine nucleotide-binding protein subunit gamma
DNAJC25-GNG10 - A0A024R161" + "text": "Sequence plot for: Transmembrane protein 265
TMEM265 - A0A087WTH1" }, "width": 1500, "xaxis": { @@ -17149,182 +37350,128 @@ "matches": "x2", "range": [ -10, - 180 + 126 ], "showgrid": false, "tickangle": 0, "tickmode": "array", "ticktext": [ "M", - "G", - "A", - "P", "E", - "P", - "T", - "I", "D", "E", - "R", - "P", - "L", - "L", - "S", - "P", - "G", - "W", - "G", + "E", + "K", "A", + "V", + "E", + "I", + "L", "G", + "N", + "T", + "E", "A", "A", - "G", - "R", - "R", - "W", - "W", - "M", - "L", - "L", - "A", + "H", "P", - "L", - "L", "P", - "A", - "L", + "S", + "P", + "I", + "R", + "C", + "C", + "W", "L", + "R", "L", - "V", "R", - "P", + "C", + "L", "A", - "G", "A", - "L", - "V", - "E", - "G", - "L", - "Y", + "T", + "S", + "I", + "I", "C", "G", - "T", - "R", - "D", "C", - "Y", - "E", - "V", + "S", + "C", "L", "G", "V", - "S", - "R", - "S", + "M", "A", - "G", - "K", + "L", + "V", + "F", "A", - "E", "I", + "K", "A", - "R", - "A", - "Y", - "R", - "Q", - "L", - "A", - "R", - "R", - "Y", - "H", - "P", - "D", - "R", - "Y", - "R", - "P", - "Q", - "P", - "G", - "D", - "S", "E", - "Q", - "U", - "E", - "N", - "C", "E", "R", - "E", - "G", - "P", + "H", + "K", + "A", "G", "R", - "T", - "P", - "Q", "S", - "A", "E", "E", "A", - "F", - "L", - "L", "V", + "R", + "W", + "G", "A", - "T", + "R", "A", - "Y", - "E", - "T", - "L", + "R", "K", - "V", - "S", - "Q", - "A", + "L", + "I", + "L", "A", + "S", + "F", "A", - "E", + "V", + "W", "L", - "Q", - "Q", - "Y", - "C", - "M", - "Q", - "N", - "A", - "C", - "K", - "D", "A", + "V", "L", + "I", "L", - "V", "G", - "V", "P", - "A", - "G", + "L", + "L", + "L", + "W", + "L", + "L", "S", - "N", + "Y", + "A", + "I", + "A", + "Q", + "A", "P", - "F", - "R", "E", "P", + "T", + "I", + "D", + "E", "R", - "S", - "C", - "A", - "L", - "L" + "E" ], "tickvals": [ 1, @@ -17442,61 +37589,7 @@ 113, 114, 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170 + 116 ], "title": { "text": "protein sequence" @@ -17509,7 +37602,7 @@ "matches": "x", "range": [ -10, - 180 + 126 ], "showgrid": false, "side": "top", @@ -17525,21 +37618,25 @@ "yaxis": { "automargin": true, "range": [ - 0, - 8.7 + -1, + 11.7 ], "showgrid": false, "showticklabels": true, "tickmode": "array", "ticktext": [ - "proteome 1
(10% coverage)", - "proteome 2
(10% coverage)", - "proteome 3
(10% coverage)", + "proteome 1
(7% coverage)", + "proteome 2
(7% coverage)", + "proteome 3
(7% coverage)", "Chain", - "Signal peptide", - "Domain", + "Transmembrane", + "Secondary structure", "chymotrypsin high specificity", - "trypsin" + "trypsin", + "AlphaFold IDR", + "AlphaFold secondary structures", + "AlphaFold confidence", + "AlphaFold exposure" ], "tickvals": [ 1, @@ -17549,7 +37646,11 @@ 5.5, 6, 7.5, - 8 + 8, + 9.5, + 10, + 10.5, + 11 ], "title": { "text": "" @@ -17559,9 +37660,9 @@ } }, "text/html": [ - "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#hide\n", + "plot_peptide_traces([test_formatted_proteome_data,test_formatted_proteome_data,test_formatted_proteome_data],\n", + " name = ['proteome 1','proteome 2','proteome 3'],\n", + " protein = \"A0A024R161\",\n", + " fasta = test_fasta,\n", + " uniprot=uniprot_annotation,\n", + " selected_features=[],\n", + " uniprot_feature_dict=uniprot_feature_dict,\n", + " uniprot_color_dict=uniprot_color_dict,\n", + " selected_proteases=[\"trypsin\",\"chymotrypsin high specificity\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Import DIA-NN output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/zx/n29r0swn0hddt1qgy159sjrw0000gn/T/ipykernel_44445/3024483264.py:5: DtypeWarning:\n", + "\n", + "Columns (2) have mixed types.Specify dtype option on import or set low_memory=False.\n", + "\n" + ] + } + ], + "source": [ + "#hide\n", + "from alphamap.organisms_data import import_fasta\n", + "human_fasta = import_fasta('Human')\n", + "from alphamap.organisms_data import import_uniprot_annotation\n", + "human_uniprot = import_uniprot_annotation('Human')\n", + "from alphamap.importing import import_data\n", + "diann_data = import_data('../testdata/test_diann_input.tsv')\n", + "from alphamap.preprocessing import format_input_data\n", + "formatted_diann_data = format_input_data(df=diann_data, fasta = human_fasta, modification_exp = r'\\[.*?\\]')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 6213.78it/s]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████| 236/236 [00:00<00:00, 2180.32it/s]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2826.35it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 116.59it/s]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 66.64it/s]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 286.11it/s]\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Peptide: %{meta}
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], + "base": 5.4, "hovertemplate": "%{text}", "marker": { - "color": "#8cdbad", + "color": "#367BC3", "line": { "width": 0 } @@ -27692,40 +59783,22 @@ "opacity": 0.8, "showlegend": false, "text": [ - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand" + "Many neighbors 6" ], "type": "bar", "x": [ - 13, - 14, - 15, - 16 + 112 ], "xaxis": "x2", "y": [ - 0.2, - 0.2, - 0.2, 0.2 ] }, { - "base": [ - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4 - ], + "base": 5.4, "hovertemplate": "%{text}", "marker": { - "color": "#8cdbad", + "color": "#38BFA7", "line": { "width": 0 } @@ -27734,49 +59807,22 @@ "opacity": 0.8, "showlegend": false, "text": [ - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand" + "Few neighbors 4" ], "type": "bar", "x": [ - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29 + 113 ], "xaxis": "x2", "y": [ - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, 0.2 ] }, { - "base": [ - 2.4, - 2.4, - 2.4, - 2.4, - 2.4 - ], + "base": 5.4, "hovertemplate": "%{text}", "marker": { - "color": "#8cdbad", + "color": "#524582", "line": { "width": 0 } @@ -27785,39 +59831,22 @@ "opacity": 0.8, "showlegend": false, "text": [ - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand" + "Surrounded 11" ], "type": "bar", "x": [ - 32, - 33, - 34, - 35, - 36 + 114 ], "xaxis": "x2", "y": [ - 0.2, - 0.2, - 0.2, - 0.2, 0.2 ] }, { - "base": [ - 2.4, - 2.4, - 2.4, - 2.4 - ], + "base": 5.4, "hovertemplate": "%{text}", "marker": { - "color": "#5dabe8", + "color": "#367BC3", "line": { "width": 0 } @@ -27826,35 +59855,22 @@ "opacity": 0.8, "showlegend": false, "text": [ - "Helix", - "Helix", - "Helix", - "Helix" + "Many neighbors 9" ], "type": "bar", "x": [ - 38, - 39, - 40, - 41 + 115 ], "xaxis": "x2", "y": [ - 0.2, - 0.2, - 0.2, 0.2 ] }, { - "base": [ - 2.4, - 2.4, - 2.4 - ], + "base": 5.4, "hovertemplate": "%{text}", "marker": { - "color": "#8cdbad", + "color": "#367BC3", "line": { "width": 0 } @@ -27863,37 +59879,22 @@ "opacity": 0.8, "showlegend": false, "text": [ - "Beta strand", - "Beta strand", - "Beta strand" + "Many neighbors 9" ], "type": "bar", "x": [ - 45, - 46, - 47 + 116 ], "xaxis": "x2", "y": [ - 0.2, - 0.2, 0.2 ] }, { - "base": [ - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4 - ], + "base": 5.4, "hovertemplate": "%{text}", "marker": { - "color": "#5dabe8", + "color": "#38BFA7", "line": { "width": 0 } @@ -27902,51 +59903,22 @@ "opacity": 0.8, "showlegend": false, "text": [ - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix" + "Few neighbors 5" ], "type": "bar", "x": [ - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58 + 117 ], "xaxis": "x2", "y": [ - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, 0.2 ] }, { - "base": [ - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4 - ], + "base": 5.4, "hovertemplate": "%{text}", "marker": { - "color": "#8cdbad", + "color": "#367BC3", "line": { "width": 0 } @@ -27955,50 +59927,22 @@ "opacity": 0.8, "showlegend": false, "text": [ - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand" + "Many neighbors 10" ], "type": "bar", "x": [ - 63, - 64, - 65, - 66, - 67, - 68, - 69 + 118 ], "xaxis": "x2", "y": [ - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, 0.2 ] }, { - "base": [ - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4 - ], + "base": 5.4, "hovertemplate": "%{text}", "marker": { - "color": "#5dabe8", + "color": "#367BC3", "line": { "width": 0 } @@ -28007,52 +59951,22 @@ "opacity": 0.8, "showlegend": false, "text": [ - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix" + "Many neighbors 8" ], "type": "bar", "x": [ - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87 + 119 ], "xaxis": "x2", "y": [ - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, 0.2 ] }, { - "base": [ - 2.4, - 2.4, - 2.4, - 2.4, - 2.4 - ], + "base": 5.4, "hovertemplate": "%{text}", "marker": { - "color": "#8cdbad", + "color": "#367BC3", "line": { "width": 0 } @@ -28061,48 +59975,22 @@ "opacity": 0.8, "showlegend": false, "text": [ - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand" + "Many neighbors 10" ], "type": "bar", "x": [ - 91, - 92, - 93, - 94, - 95 + 120 ], "xaxis": "x2", "y": [ - 0.2, - 0.2, - 0.2, - 0.2, 0.2 ] }, { - "base": [ - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4 - ], + "base": 5.4, "hovertemplate": "%{text}", "marker": { - "color": "#5dabe8", + "color": "#8FF5A2", "line": { "width": 0 } @@ -28111,65 +59999,22 @@ "opacity": 0.8, "showlegend": false, "text": [ - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix", - "Helix" + "No neighbors 0" ], "type": "bar", "x": [ - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109 + 121 ], "xaxis": "x2", "y": [ - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, 0.2 ] }, { - "base": [ - 2.4, - 2.4, - 2.4, - 2.4, - 2.4, - 2.4 - ], + "base": 5.4, "hovertemplate": "%{text}", "marker": { - "color": "#8cdbad", + "color": "#8FF5A2", "line": { "width": 0 } @@ -28178,29 +60023,14 @@ "opacity": 0.8, "showlegend": false, "text": [ - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand", - "Beta strand" + "No neighbors 0" ], "type": "bar", "x": [ - 114, - 115, - 116, - 117, - 118, - 119 + 122 ], "xaxis": "x2", "y": [ - 0.2, - 0.2, - 0.2, - 0.2, - 0.2, 0.2 ] }, @@ -29847,7 +61677,7 @@ "automargin": true, "range": [ -1, - 3.2 + 6.2 ], "showgrid": false, "showticklabels": true, @@ -29855,12 +61685,20 @@ "ticktext": [ "proteome
(40% coverage)", "Chain", - "Secondary structure" + "Secondary structure", + "AlphaFold IDR", + "AlphaFold secondary structures", + "AlphaFold confidence", + "AlphaFold exposure" ], "tickvals": [ 0, 2, - 2.5 + 2.5, + 4, + 4.5, + 5, + 5.5 ], "title": { "text": "" @@ -29870,9 +61708,9 @@ } }, "text/html": [ - "
\n", + " \n", + " \n", + "\n", + " \n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "

\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + "\n", + " \n", + " \"\"\"\n", + " \n", + " html_template = html_template.replace('your_file.cif',protein+'_'+data_name+'_mod.cif')\n", + " \n", + " if coloring == 'AlphaFold confidence':\n", + " color = 'af'\n", + " elif coloring == 'AlphaFold exposure':\n", + " color = 'exposure'\n", + " elif coloring == 'MS peptides':\n", + " color = 'aa'\n", + " elif coloring == 'MS modified peptides':\n", + " color = 'aa_ptm'\n", + " elif coloring == 'MS PTMs':\n", + " color = 'ptm'\n", + " elif coloring == 'AlphaFold IDR':\n", + " color = 'idr'\n", + " elif coloring == 'MS PTMs on AlphaFold IDR':\n", + " color = 'idr_ptm'\n", + " elif coloring == 'AlphaFold secondary structures':\n", + " color = 'structure_group'\n", + " else:\n", + " color = 'af'\n", + " \n", + " html = html_template.replace('globalThis.color_choice = your_choice',f\"globalThis.color_choice = '{color}'\")\n", + " \n", + " return html " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "def get_ms_concensus(ms_list):\n", + " unique_ms_list = set(ms_list)\n", + " if len(unique_ms_list)==1:\n", + " out = unique_ms_list\n", + " else:\n", + " out = [i for i in unique_ms_list if i != 'detected']\n", + "\n", + " out = ';'.join(out)\n", + " \n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "#export \n", + "import alphamap\n", + "def plot_3d_structure(df: pd.DataFrame or list,\n", + " name: str,\n", + " protein: str,\n", + " fasta: fasta,\n", + " selected_coloring: str,\n", + " dashboard: bool = False, \n", + " download_folder: str = tempfile.gettempdir()):\n", + "\n", + " \"\"\"\n", + " Function to generate the sequence plot.\n", + "\n", + " Args:\n", + " df (pd.DataFrame/list): Single dataframe or list of dataframes containing the datasets to plot.\n", + " name (str): Single string containing the name of the MS dataset in df.\n", + " protein (str): Uniprot protein accession.\n", + " fasta (fasta): Fasta file imported by pyteomics 'fasta.IndexedUniProt'.\n", + " selected_coloring (str): Coloring to show.\n", + " dashboard (bool, optional): Flag if the function is called from the dashboard. Default is 'False'.\n", + " \n", + " Returns:\n", + " go.Figure: 3D plot.\n", + "\n", + " \"\"\"\n", + "\n", + " if isinstance(df, pd.DataFrame):\n", + " df_plot = get_plot_data(protein=protein,\n", + " df = df,\n", + " fasta = fasta)\n", + " elif isinstance(df, list):\n", + " df_plot = [get_plot_data(protein=protein,\n", + " df = d,\n", + " fasta = fasta) for d in df]\n", + " df_plot = pd.concat(df_plot)\n", + " \n", + " if df_plot is None:\n", + " MS_annotation = pd.DataFrame({'position' : [], 'MS data': []}) \n", + " else:\n", + " MS_annotation = extract_annotation(df_plot) \n", + " MS_annotation = MS_annotation.groupby('position')['MS data'].apply(list).reset_index(name='MS data')\n", + " MS_annotation['MS data'] = MS_annotation['MS data'].apply(lambda x: get_ms_concensus(x))\n", + "\n", + " cif_available = manipulate_cif(protein = protein, \n", + " MS_data = MS_annotation,\n", + " download_folder = download_folder)\n", + " \n", + " BASE_PATH = os.path.dirname(alphamap.__file__)\n", + " js_path = os.path.join(BASE_PATH, 'js')\n", + " cif_path = download_folder\n", + " \n", + " \n", + " import shutil\n", + " new_file = os.path.join(cif_path, protein+'_'+name+'_mod.cif')\n", + " try:\n", + " os.remove(new_file)\n", + " except:\n", + " pass\n", + " shutil.copyfile(src = os.path.join(cif_path, protein+'_mod.cif'), \n", + " dst = new_file)\n", + "\n", + " if cif_available:\n", + " mod_html = adjust_html(protein=protein, \n", + " coloring=selected_coloring,\n", + " data_name=name)\n", + " else:\n", + " mod_html = f\"\"\"\n", + " \n", + "
No AlphaFold structure available for {protein}.
\n", + " \"\"\"\n", + " \n", + " \n", + " \n", + " return mod_html, js_path, cif_path" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# options: \n", + "# 'alphafold_quality'\n", + "# 'alphafold_exposure'\n", + "# 'alphafold_IDR'\n", + "# 'alphafold_secondary_structures'\n", + "# 'MS data'\n", + "# 'MS PTMs'\n", + "# 'MS amino acids'" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# #hide\n", + "# plot_3d_structure(df = formatted_diann_data,\n", + "# name = 'proteome',\n", + "# protein = \"P23284\", #Q9H7C9, P23284\n", + "# fasta = human_fasta,\n", + "# selected_coloring = 'AlphaFold quality', #'AlphaFold exposure', #'AlphaFold quality', 'MS data'\n", + "# dashboard = False)" ] }, { @@ -29922,7 +62153,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -30009,7 +62240,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -30031,7 +62262,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -30052,7 +62283,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -30063,7 +62294,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -30073,7 +62304,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -30096,6 +62327,13 @@ "notebook2script()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -30106,9 +62344,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:alphamap]", + "display_name": "Python [conda env:alphamap_fold]", "language": "python", - "name": "conda-env-alphamap-py" + "name": "conda-env-alphamap_fold-py" }, "language_info": { "codemirror_mode": { @@ -30120,7 +62358,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.11" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/nbs/index.ipynb b/nbs/index.ipynb index ae9ef60..d13a420 100644 --- a/nbs/index.ipynb +++ b/nbs/index.ipynb @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -210,18 +210,6 @@ "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" } }, "nbformat": 4, diff --git a/requirements.txt b/requirements.txt index 4b2da47..ee79f44 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,3 +9,5 @@ psutil==5.8.0 reportlab==3.5.59 kaleido==0.2.1 bokeh==2.2.2 +structuremap==0.0.6 +h5py==3.6.0 diff --git a/settings.ini b/settings.ini index 884b1f4..3455574 100644 --- a/settings.ini +++ b/settings.ini @@ -13,8 +13,8 @@ user = ibludau # author_email = email@example.com copyright = Mann Labs branch = master -version = 0.1.5 -min_python = 3.6 +version = 0.1.7 +min_python = 3.8 audience = Developers language = English # Set to True if you want to create a more fancy sidebar.json than the default