These are cytotoxic T-cell HLA alleles. HLA genotyping is very common and easy to do, so Genbank is the repository.
A*11:01 has very high frequency in Aborigine populations here . You can explore the population genetics and the past, present and current population genetics distributions per population of HLA at http://www.allelefrequencies.net/
These are NCBI taxonomy ID's rather than anything specific to UniProt. One way to parse them would be using BioPython via Bio.Entrez, for example:
from Bio import Entrez
Entrez.email = 'email@example.com'
handle = Entrez.efetch(db='taxonomy', id= id_, retmode='xml')
records = Entrez.read(handle)
Ok, figured it out by looking at the scheme uniprot provides:
SELECT ?protein ?yeast_id
Bind(<http://purl.uniprot.org/uniprot/P00330> as ?protein).
?protein up:encodedBy ?gene .
?gene up:locusName ?yeast_id
So one starts at the accession number in the middle and then uses the ...
For the correspondence between PDB and Uniprot entries you can use SIFTS -- a semi-automated mapping between PDB and UniProt maintained by PDBe.
The pipeline that creates the mappings uses BLAST and a few other criteria to decide which UniProt entry should be assigned to each PDB entry. The SIFTS website has all the data in CSV files.
The link to the FTP for the GOA database files is listed on the GOA Downloads page. The file containing the mapping info you seek, goa_uniprot.all, comes in two formats, .gaf and .gpa. The README in the FTP directory linked provides details on the structure of the files.
Below is the first 20 lines of the .gaf file after uncompressing. Column 2 contains the ...
it is because you do a text search and the "word" Q9UJL9 appears in multiple entries. If you want an id search, specifically say so and for that the query syntax is id:Q9UJL9. You will want to follow a redirect, and there are some rare cases where that might still lead to more than one entry if the id was made secondary and attached to multiple ...
You can use the SIFTS mapping: http://www.ebi.ac.uk/pdbe/docs/sifts/
From an article:
The Structure Integration with Function, Taxonomy and Sequences
resource (SIFTS; http://pdbe.org/sifts) is a close collaboration
between the Protein Data Bank in Europe (PDBe) and UniProt. The two
teams have developed a semi-automated process for maintaining
I think the mistake here is thinking that you need to use the sequence in order to find the genome co-ordinates. If these proteins come from a well annotated speices, you can easily do this with BioMart.
Go the the ENSEMBL web site.
From the top menu, select BioMart
Under "-CHOSE DATABASE-" select Ensembl Genes, and under "-CHOOSE DATASET-" select your ...
This answer is very late but may still help others:
Have a look at the UniProt index of human polymorphisms and disease mutations at https://www.uniprot.org/docs/humsavar
Please don't hesitate to contact the UniProt helpdesk if you have any additional questions.
The query link for all interactors would be thus : https://www.uniprot.org/uniprot/?query=organism%3A9606+AND+interactor%3A*&sort=score
There is always an option of downloading the static version of the service if you find it incovnient to deal with their API for your use case from here and have it all to yourself, pipe data into your dataframes locally....
Since you are already on UniProtKB, the simplest way will be if you add this information to the output table and download it as a that you can import and filter in R.
To get additional annotations, click the button labelled "Columns" on top of the UniPortKB website.
This allows you to add a wide variety of annotations such as subcellular location to the ...
I believe the attribute you should be looking for is dbxrefs:
[x for x in record.dbxrefs if x.startswith('GO:')]
Apparently SeqIO handles uniprot text files differently than the SwissProt module:
from Bio import SwissProt
record = SwissProt.read(open('single_record_from_uniprot.txt'))
So you should change you code to use the ...
Using the UniProt website API (https://www.uniprot.org/help/api) this could be done with a query like
If downloading the complete database is not an option, you may able to use the instructions from this help page (based on the gff format for any given query):
UniProt also provides a BLAST service (https://www.uniprot.org/blast), or you could use the Expasy BLAST too, at https://web.expasy.org/blast.
If you have performed your similarity search at NCBI or elsewhere, you can map the identifiers from that result to UniProt by using the UniProt IDmapping service at https://www.uniprot.org/uploadlists. This can also ...
This is a copy-paste nearly verbatim of comments so that the question has an answer
What you are trying to do is find what genes are in what homology cluster. This is common problem and there are many solution each with some issues.
Uniprot90 is indeed a cluster of homologues but it is too limited. Whereas you require clusters that span all the domains (...
From the UniProt.ws manual:
columns shows which kinds of data can be returned for the
So, you will need to go over the output of columns(your_uniprotws_object) if it includes the data type you are looking for.
And here is a related question.
Do you have a small sample of the proteins you are looking for and what you have tried so far in R using Uniprot.ws ? (including output of it), it could be helpful to identify your problem and how to solve it.
Personally, I would rather consider Bioconductor as a platform for providing R packages dedicated to bioinformatics than a package to analyze data (...
Thank you for asking this question. "Flag" is actually an obsolete notation which predates the introduction of proper evidence attribution in UniProtKB, and which escaped us in our efforts to update documentation to use the correct terminology. The correct term would now be "Evidence", and the next version of the user manual will have this corrected (...
I don't have a complete answer to your question, but as a primer I can tell you that you should look into the Bioconductor packages. The reason is that biological sequence data are deposited in online databases (like GenBank, and others), and exposed by these databases in such a way that you precisely don't need web scrapping techniques to retrieve the data ...
You can search databases like Cosmic(Cancer) with the gene names to get frequently occurring mutations in cancer. I'm not sure about any specific tool/pipeline available for this exact scenario.
As an alternative approach literature mining for gene associated mutation discovery, you can use text mining with machine learning to automate the whole process.
You can download a BLAST db containing all sequences of proteins in Uniprot and in the PDB. The way I would go about this is first download the databases for uniprot and PDB, then query the PDB database for each sequence from Uniprot. If you get a BLAST hit above a certain threshold (whatever you define as homolog) then add that sequence to a file, and voila,...
That file is actually trivial to parse. You only care about lines that have N=$species, so you can simply do:
sed -En 's/.* ([0-9]+): N=(.*)/\1\t\2/p' speclist
That will return a tab separated list of taxID and species:
$ sed -En 's/.* ([0-9]+): N=(.*)/\1\t\2/p' speclist | head
648330 Aedes albopictus densovirus (isolate Boublik/1994)