Identify kinases with enriched substrates in differentially included exons, using an adapted version of KSTAR#
Given that phosphorlaiton are one of the most commonly impacted modifications, there is potential for kinases targeting these sites to be indirectly impacted by alternative splicing through changes in the availability of their substrates. While we provide functions for performing enrichment of known kinase substrates from databases like PhosphoSitePlus, RegPhos, and PTMsigDB, these resources are limited by the overall number of validated substrates (<5%). For this purpose, we have adapted a previously developed algorithm called KSTAR (Kinase Substrate to Activity Relationships) for use with spliced PTM data, which harnesses kinase-substrate predictions to expand the overall number of phosphorylation sites that can be used as evidence. This particularly important as you may find many of the spliced PTMs in your dataset are less well studied and may not have any annotated kinases.
In order to perform KSTAR analysis, you will first need to download KSTAR networks from the following figshare.
Once you have downloaded the networks, all you need is your PTM data. You will need to run analysis for tyrosine kinases (Y) and serine/threonine kinases (ST)
[1]:
from ptm_pose import analyze
import pandas as pd
# Load spliced ptm and altered flank data
spliced_ptms = pd.read_csv('spliced_ptms.csv')
#perform kstar enrichment for tyrosine phosphorylation, denoted by "Y"
network_dir = './NetworKIN/'
kstar_enrichment = analyze.kstar_enrichment(spliced_ptms, network_dir = network_dir, phospho_type = 'Y')
kstar_enrichment.run_kstar_enrichment()
kstar_enrichment.return_enriched_kinases()
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[1], line 5
2 import pandas as pd
4 # Load spliced ptm and altered flank data
----> 5 spliced_ptms = pd.read_csv('spliced_ptms.csv')
7 #perform kstar enrichment for tyrosine phosphorylation, denoted by "Y"
8 network_dir = './NetworKIN/'
File ~/miniconda3/envs/pose_doc/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
1013 kwds_defaults = _refine_defaults_read(
1014 dialect,
1015 delimiter,
(...)
1022 dtype_backend=dtype_backend,
1023 )
1024 kwds.update(kwds_defaults)
-> 1026 return _read(filepath_or_buffer, kwds)
File ~/miniconda3/envs/pose_doc/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
617 _validate_names(kwds.get("names", None))
619 # Create the parser.
--> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
622 if chunksize or iterator:
623 return parser
File ~/miniconda3/envs/pose_doc/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
1617 self.options["has_index_names"] = kwds["has_index_names"]
1619 self.handles: IOHandles | None = None
-> 1620 self._engine = self._make_engine(f, self.engine)
File ~/miniconda3/envs/pose_doc/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1880, in TextFileReader._make_engine(self, f, engine)
1878 if "b" not in mode:
1879 mode += "b"
-> 1880 self.handles = get_handle(
1881 f,
1882 mode,
1883 encoding=self.options.get("encoding", None),
1884 compression=self.options.get("compression", None),
1885 memory_map=self.options.get("memory_map", False),
1886 is_text=is_text,
1887 errors=self.options.get("encoding_errors", "strict"),
1888 storage_options=self.options.get("storage_options", None),
1889 )
1890 assert self.handles is not None
1891 f = self.handles.handle
File ~/miniconda3/envs/pose_doc/lib/python3.12/site-packages/pandas/io/common.py:873, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
868 elif isinstance(handle, str):
869 # Check whether the filename is to be opened in binary mode.
870 # Binary mode does not support 'encoding' and 'newline'.
871 if ioargs.encoding and "b" not in ioargs.mode:
872 # Encoding
--> 873 handle = open(
874 handle,
875 ioargs.mode,
876 encoding=ioargs.encoding,
877 errors=errors,
878 newline="",
879 )
880 else:
881 # Binary mode
882 handle = open(handle, ioargs.mode)
FileNotFoundError: [Errno 2] No such file or directory: 'spliced_ptms.csv'
You can also run the same analysis for serine/threonine kinases:
[2]:
kstar_enrichment = analyze.kstar_enrichment(spliced_ptms, network_dir = network_dir, phospho_type = 'ST')
kstar_enrichment.run_kstar_enrichment()
kstar_enrichment.return_enriched_kinases()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[2], line 1
----> 1 kstar_enrichment = analyze.kstar_enrichment(spliced_ptms, network_dir = network_dir, phospho_type = 'ST')
2 kstar_enrichment.run_kstar_enrichment()
3 kstar_enrichment.return_enriched_kinases()
NameError: name 'spliced_ptms' is not defined