import pandas as pd
from collections import namedtuple
from protmapper.uniprot_client import load_fasta_sequences
from indra.sources.hprd.processor import HprdProcessor
_hprd_id_cols = ['HPRD_ID', 'HGNC_SYMBOL', 'REFSEQ_GENE', 'REFSEQ_PROTEIN',
'EGID', 'OMIM', 'UNIPROT', 'NAME']
_cplx_cols = ['CPLX_ID', 'HPRD_ID', 'HGNC_SYMBOL', 'REFSEQ_PROTEIN',
'EVIDENCE', 'PMIDS']
_ptm_cols = ['HPRD_ID', 'HGNC_SYMBOL', 'HPRD_ISOFORM', 'REFSEQ_PROTEIN',
'POSITION', 'RESIDUE', 'ENZ_HGNC_SYMBOL', 'ENZ_HPRD_ID',
'MOD_TYPE', 'EVIDENCE',
'PMIDS']
_ppi_cols = ['HGNC_SYMBOL_A', 'HPRD_ID_A', 'REFSEQ_PROTEIN_A',
'HGNC_SYMBOL_B', 'HPRD_ID_B', 'REFSEQ_PROTEIN_B',
'EVIDENCE', 'PMIDS']
[docs]def process_flat_files(id_mappings_file, complexes_file=None, ptm_file=None,
ppi_file=None, seq_file=None, motif_window=7):
"""Get INDRA Statements from HPRD data.
Of the arguments, `id_mappings_file` is required, and at least one of
`complexes_file`, `ptm_file`, and `ppi_file` must also be given. If
`ptm_file` is given, `seq_file` must also be given.
Note that many proteins (> 1,600) in the HPRD content are associated with
outdated RefSeq IDs that cannot be mapped to Uniprot IDs. For these, the
Uniprot ID obtained from the HGNC ID (itself obtained from the Entrez ID)
is used. Because the sequence referenced by the Uniprot ID obtained this
way may be different from the (outdated) RefSeq sequence included with the
HPRD content, it is possible that this will lead to invalid site positions
with respect to the Uniprot IDs.
To allow these site positions to be mapped during assembly, the
Modification statements produced by the HprdProcessor include an additional
key in the `annotations` field of their Evidence object. The annotations
field is called 'site_motif' and it maps to a dictionary with three
elements: 'motif', 'respos', and 'off_by_one'. 'motif' gives the peptide
sequence obtained from the RefSeq sequence included with HPRD. 'respos'
indicates the position in the peptide sequence containing the residue.
Note that these positions are ONE-INDEXED (not zero-indexed). Finally, the
'off-by-one' field contains a boolean value indicating whether the correct
position was inferred as being an off-by-one (methionine cleavage) error.
If True, it means that the given residue could not be found in the HPRD
RefSeq sequence at the given position, but a matching residue was found at
position+1, suggesting a sequence numbering based on the methionine-cleaved
sequence. The peptide included in the 'site_motif' dictionary is based on
this updated position.
Parameters
----------
id_mappings_file : str
Path to HPRD_ID_MAPPINGS.txt file.
complexes_file : Optional[str]
Path to PROTEIN_COMPLEXES.txt file.
ptm_file : Optional[str]
Path to POST_TRANSLATIONAL_MODIFICATIONS.txt file.
ppi_file : Optional[str]
Path to BINARY_PROTEIN_PROTEIN_INTERACTIONS.txt file.
seq_file : Optional[str]
Path to PROTEIN_SEQUENCES.txt file.
motif_window : int
Number of flanking amino acids to include on each side of the
PTM target residue in the 'site_motif' annotations field of the
Evidence for Modification Statements. Default is 7.
Returns
-------
HprdProcessor
An HprdProcessor object which contains a list of extracted INDRA
Statements in its statements attribute.
"""
id_df = pd.read_csv(id_mappings_file, delimiter='\t', names=_hprd_id_cols,
dtype='str')
id_df = id_df.set_index('HPRD_ID')
if complexes_file is None and ptm_file is None and ppi_file is None:
raise ValueError('At least one of complexes_file, ptm_file, or '
'ppi_file must be given.')
if ptm_file and not seq_file:
raise ValueError('If ptm_file is given, seq_file must also be given.')
# Load complexes into dataframe
cplx_df = None
if complexes_file:
cplx_df = pd.read_csv(complexes_file, delimiter='\t', names=_cplx_cols,
dtype='str', na_values=['-', 'None'])
# Load ptm data into dataframe
ptm_df = None
seq_dict = None
if ptm_file:
ptm_df = pd.read_csv(ptm_file, delimiter='\t', names=_ptm_cols,
dtype='str', na_values='-')
# Load protein sequences as a dict keyed by RefSeq ID
seq_dict = load_fasta_sequences(seq_file, id_index=2)
# Load the PPI data into dataframe
ppi_df = None
if ppi_file:
ppi_df = pd.read_csv(ppi_file, delimiter='\t', names=_ppi_cols,
dtype='str')
# Create the processor
return HprdProcessor(id_df, cplx_df, ptm_df, ppi_df, seq_dict, motif_window)