dzjxzyd/UniRef100_len_0_50

This is a dataset download from UniRef100 database with sequence length ranging from 0 to 50

codes for the data mining (downloaded on September 30 2024)

import requests
query_url = 'https://rest.uniprot.org/uniref/stream?compressed=true&fields=id%2Clength%2Cidentity%2Csequence&format=tsv&query=%28%28length%3A%5B*+TO+50%5D%29+AND+%28identity%3A1.0%29%29'
uniprot_request = requests.get(query_url)
from io import BytesIO
import pandas

bio = BytesIO(uniprot_request.content)

df = pandas.read_csv(bio, compression='gzip', sep='\t')
df.to_parquet('peptide_UniRef100_0_50.parquet')

Data download procedure from UniProt database

we only need the sequences within a specific length range from a specific data sources (UniRef and UniParc). For example, download data from UniRef; we selected UniRef first and searched for

• (length:[* TO 50]) AND (identity:1.0) ######### length between 0 - 50 from UniRef100
• (length:[* TO 50]) AND (identity:0.9) ######### length between 0 - 50 from UniRef90
• (length:[* TO 50]) AND (identity:0.5) ######### length between 0 - 50 from UniRef50

for UniPrac dataset, we selected UniPrac and searched for

• (length:[* TO 50]) ######### length between 0 - 50 from UniPrac

1. after it return the results, let's say around 19 million sequences.

2. then selected 'Download', and set the format to TSV and the customize columns to Sequence and length (we actually only need the sequence column, but the rest might be needed, so we keey them)

3. choose compressed format

4. Once that's done, selecting Generate URL for API gives you a URL you can pass to Requests (this is the query_url variable below).

5. To get this data into Pandas, we use a BytesIO object, which Pandas will treat like a file. If you downloaded the data as a file you can skip this bit and just pass the filepath directly to read_csv.

Explanation regarding rhe UniRef and UniParc databases

UniRef (UniProt Reference Clusters): UniRef is a clustering system for protein sequences that helps in reducing redundancy and speeding up sequence similarity searches. It consists of three databases:

UniRef100: Contains all sequences from UniProtKB (Swiss-Prot and TrEMBL), as well as selected UniParc sequences, without any redundancy. UniRef90: Clusters sequences that have at least 90% sequence identity to each other and cover 80% of the longest sequence. This reduces redundancy by grouping highly similar sequences together. UniRef50: Further reduces redundancy by clustering sequences that have at least 50% identity to each other. The UniRef databases make it easier to search protein sequences and analyze large datasets by removing highly similar sequences and presenting representative clusters.

UniParc (UniProt Archive): UniParc is a comprehensive protein sequence archive that contains all publicly available protein sequences from various sources, such as UniProtKB, Ensembl, RefSeq, PDB, and many others. Its goal is to capture and store every protein sequence ever published, independent of any annotation or quality check.

UniParc assigns a unique identifier to each distinct protein sequence and tracks all updates or changes to that sequence over time. This allows researchers to access historical versions of protein sequences and follow the evolution of sequence data across different databases.