UniProt Database Web Parser Project
--
[](https://pepy.tech/project/uniprotparser)
TLDR: This parser can be used to parse UniProt accession id and obtain related data from the UniProt web database.
To use:
```bash
python -m pip install uniprotparser
```
or
```bash
python3 -m pip install uniprotparser
```
With version 1.2.0, we have exposed `to` and `from` mapping parameters for UniProt API where you can indicate which database you want to map to and from.
```python
from uniprotparser import get_from_fields, get_to_fields
#to get all available fields to map from
from_fields = get_from_fields()
print(from_fields)
#to get all available fields to map to
to_fields = get_to_fields()
print(to_fields)
```
These parameters can be passed to the `parse` method of the `UniprotParser` class as follow
```python
from uniprotparser.betaparser import UniprotParser
parser = UniprotParser()
for p in parser.parse(ids=["P06493"], to_key="UniProtKB", from_key="UniProtKB_AC-ID"):
print(p)
```
With version 1.1.0, a simple CLI interface has been added to the package.
```bash
Usage: uniprotparser [OPTIONS]
Options:
-i, --input FILENAME Input file containing a list of accession ids
-o, --output FILENAME Output file
--help Show this message and exit.
```
With version 1.0.5, support for asyncio through `aiohttp` has been added to `betaparser`. Usage can be seen as follow
```python
from uniprotparser.betaparser import UniprotParser
from io import StringIO
import asyncio
import pandas as pd
async def main():
example_acc_list = ["Q99490", "Q8NEJ0", "Q13322", "P05019", "P35568", "Q15323"]
parser = UniprotParser()
df = []
#Yield result for 500 accession ids at a time
async for r in parser.parse_async(ids=example_acc_list):
df.append(pd.read_csv(StringIO(r), sep="\t"))
#Check if there were more than one result and consolidate them into one dataframe
if len(df) > 0:
df = pd.concat(df, ignore_index=True)
else:
df = df[0]
asyncio.run(main())
```
With version 1.0.2, support for the new UniProt REST API have been added under `betaparser` module of the package.
In order to utilize this new module, you can follow the example bellow
```python
from uniprotparser.betaparser import UniprotParser
from io import StringIO
import pandas as pd
example_acc_list = ["Q99490", "Q8NEJ0", "Q13322", "P05019", "P35568", "Q15323"]
parser = UniprotParser()
df = []
#Yield result for 500 accession ids at a time
for r in parser.parse(ids=example_acc_list):
df.append(pd.read_csv(StringIO(r), sep="\t"))
#Check if there were more than one result and consolidate them into one dataframe
if len(df) > 0:
df = pd.concat(df, ignore_index=True)
else:
df = df[0]
```
---
To parse UniProt accession with legacy API
```python
from uniprotparser.parser import UniprotSequence
protein_id = "seq|P06493|swiss"
acc_id = UniprotSequence(protein_id, parse_acc=True)
#Access ACCID
acc_id.accession
#Access isoform id
acc_id.isoform
```
To get additional data from UniProt online database
```python
from uniprotparser.parser import UniprotParser
from io import StringIO
#Install pandas first to handle tabulated data
import pandas as pd
protein_accession = "P06493"
parser = UniprotParser([protein_accession])
#To get tabulated data
result = []
for i in parser.parse("tab"):
tab_data = pd.read_csv(i, sep="\t")
last_column_name = tab_data.columns[-1]
tab_data.rename(columns={last_column_name: "query"}, inplace=True)
result.append(tab_data)
fin = pd.concat(result, ignore_index=True)
#To get fasta sequence
with open("fasta_output.fasta", "wt") as fasta_output:
for i in parser.parse():
fasta_output.write(i)
```
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