Can I search without going through the Hub
Of course you CAN!~
This module is the core module of patpat_env and allows users to aggregate other classes of patpat_env through the classes in this module. init function provides the runtime environment required for patpat_env. queryHub class provides querying of protein metadata and generating peptides to be retrieved, while MapperHub class provides breakpoints for merging and retrieving multiple proteomic databases . Furthermore, both classes are pluggable, so it is easy to insert self-built methods or classes into them, as long as the developer adheres to the interface design.
Typical usage example:
from patpat import hub
from patpat import mapper
from patpat import utility
utility.init()
identifier_ = 'P05067'
q = hub.QueryHub()
q.identifier = identifier_
q.simple_query()
m = hub.MapperHub(config=q.get_query_config(),
mappers=[mapper.PrideMapper(), mapper.IProXMapper()],
task=None
# task=[your task's uuid]
)
m.mapping()
result_ = m.export()The classic usage of the Hub module above is the same as that described in Quick Start. If you don't understand it, please go back and take a look~
The classes in this module use three levels of inheritance. First,(1) all subclasses need to inherit from the Retriever base class, second,(2) depending on the database to be targeted, a base class needs to be created for that database, and finally, the subclass that collects the specified data from that database needs to inherit from that database's base class (3). For example, the base class of PRIDE database is named GenericPrideRetriever (2), which inherits Retriever (1) base class, and the subclass that collects protein information from PRIDE is named PrideProteinRetriever (3).
Typical usage example:
from patpat import logger
from patpat import retriever
# Logger module for testing, with console output available through TestLogger when using other modules alone
l = logger.TestLogger()
r = retriever.PrideProteinRetriever()
r.request_word = "Q9CWY9"
r.get_payloads_on_web()
r.payloads["pageSize"] = 200 # Optional: The parameters can be modified
r.retrieve()
output = r.responsefrom patpat import logger
from patpat import retriever
# Logger module for testing, with console output available through TestLogger when using other modules alone
l = logger.TestLogger()
r = retriever.PridePeptideRetriever()
r.request_word = 'TCVADESAENCDK'
r.get_payloads_on_web()
r.retrieve()
output = r.responsefrom patpat import retriever
r = retriever.IProXProjectRetriever()
r.request_word = 'PXD006512'
r.retrieve()
output = r.response It is easy to see that there are two keys to automatic information acquisition.
- Getting the sequence of the peptide to be queried and the number of the data set (Queryer module)
- Calling the retriever class repeatedly (Mapper module)
Next, let's see how we should use the Mapper module!
This module builds different classes according to the database oriented, and each class calls the corresponding retriever to realize the retrieve by itself. The protein/peptide retriever is used to implement the protein/peptide level search, and the protein-to-project mapping is implemented by the project retriever. In addition, each class also needs to implement other functions:
- a filtering function to remove incorrect mappings;
- an export function to structure the retrieve results for downstream modules to call.
Typical usage example:
from patpat import hub
from patpat import mapper
from patpat import utility
utility.init()
utility.initiate_uniprot_proteome_catalog()
identifier = 'E9PV96'
q = hub.QueryHub()
q.identifier = identifier
q.simple_query()
conf = q.get_query_config()
m = mapper.PrideMapper()
# Add configs
m._identifier = conf['identifier']
m._peptides = conf['peptides']
m._organism = conf['organism']
m.mapping()
m.filtering()
output = m.export()4 Querier module: This module is used to build the configuration information that needs to be retrieved
The configuration information required to complete the mapping are the protein metadata and the peptide sequences to be retrieved. The necessary protein metadata are: the protein identifier, the species or tissue to which the protein belongs, and the protein sequence, where the protein sequence is used to generate the peptide sequences to be retrieved.
from patpat import querier
p1 = querier.UniProtProteinQuerier()
p1.identifier = 'E9PV96'
p1.query()
_, organism_, fasta_ = p1.get_properties()
p2 = querier.LocalPeptideQuerier()
p2.set_params(sequence=fasta_['sequence'],
organism=organism_)
p2.query()
digestion_params_, source_, filtered_peptides_ = p2.get_properties()The class CoreLogger is used to generate logs and search temporary files, and is recommended to be used with hub.py and viewer.py.
Typical usage example:
from patpat import logger
uuid = ''
l = logger.CoreLogger(uuid)
l.set_core()
l.set_tmp()Typical usage example:
from patpat import checker
from patpat import retriever
c = checker.Checker()
c.peptide_retrievers = [
retriever.PridePeptideRetriever(),
retriever.IProXPeptideRetriever()
]
c.check()Can't get enough? Start learning how to extend Patpat!
