9

I tend to use Ensembl Biomart for such queries since there are APIs for various programming languages, e.g. biomaRt, and, maybe more interestingly, via a REST API (although it’s a pretty terrible one). To translate identifiers from different databases, proceed as follows: Choose database “Ensembl genes” Choose dataset your desired oganism Go on “Filters” › ...


8

If you're comfortable doing a little programming, check out mygene.info (web services for gene annotations of all sorts). ID translation is specifically one of the use cases addressed in the bioconductor client (see the vignette), and there is a python client as well available through pypi. The documentation for mygene can be found here.


7

Conversion using R: library(biomaRt) mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl")) genes <- getBM( filters="ensembl_gene_id", attributes=c("ensembl_gene_id", "entrezgene"), values=ensembl.genes, mart=mart) Where ensembl.genes is a vector of Ensembl gene IDs.


7

As others have mentioned in their answer, bioMart is usually the best place to go for this infomation because it draws its data directly from the Ensembl database. However, you will find you do not get a full translation of Ensembl IDs to Entrez IDs even there. The reason for this is there simply does not exist a simple 1-to-1 mapping of Entrez IDs to ...


6

If you don't mind hitting it 50k times and are OK with python3... from urllib import request import json def getPathways(proteinID): baseURL = 'http://reactome.org/ContentService/data/query' PathwayIDs = set() try: response = request.urlopen('{}/{}'.format(baseURL, proteinID)).read().decode() data = json.loads(response) ...


6

The ID you need is the NCBI gene ID, which is the same as the EntrezGene ID.


4

You can do the same using AnnotationDbi Package from Bioconductor. Download the organism specific annotation file like org.Mm.eg.db for mouse and map current gene ids to the gene names/gene symbols.


3

Ok. Answering my own question. I did this way. And I got the output I want. install.packages("msigdbr") library(msigdbr) m_df = msigdbr(species = "Homo sapiens", category = "C5") head(m_df) Output: gs_name gs_id gs_cat gs_subcat human_gene_symb~ species_name entrez_gene <chr> <chr> <chr> <chr> <chr> <...


3

I'm not a huge fan of the Ensembl BioMart system because I find it difficult to use. The Synergizer has a very straightforward interface and works pretty well for most lists. Note: it hasn't been updated in a while.


3

My favourite gene database conversion site is db2db. You provide a list of IDs in one of a large number of different public formats, and can select one or more IDs as translation targets. It will then walk through various known paths to do the translation, picking what it determines to be the most reliable route to get the information you have asked for. ...


3

On Reactome website they have at the download page mapping files (uniprot, ensembl, etc.), but unfortunately not for the protein IDs you are using (stable identifiers). I had contact with their helpdesk, and they sent me a file containing all protein IDs to the pathways. Exactly what you need. I have asked them if they wanted to put it on their download ...


2

I think there is no general answer to your question. Which type of ID you will use depends on how practical it will be for your downstream processing. This depends on what other source of information you have, how much conversion work you will need to do in order to match your data to these information if you use one or the other. I think the choice may ...


2

By far my preferred option for doing this manually is PICR: http://www.ebi.ac.uk/Tools/picr/ BTW it is not "ridiculous" to get different numbers of genes reported for a given set of proteins. For several reasons: Uniprot IDs can disappear, merge or be split not all uniprot and gene IDs have a 1-to-1 relationship depending on species some gene symbols can ...


2

SMILES are meant for machines, but reading up on the format is very handy and one totally can read and write them —I do it often and could not recommend it enough. You can have SMILES written differently and they give the same result —hence why you should never compare SMILES, but Inchi or better still a parsed molecule —e.g. in RDKit. Looking at them, I can ...


1

Mice genes are usually the same as the human ones, but lower case with only the first character as upper (Sox17 -> SOX17). toupper(rownames(mouse_only)) will do the trick. Unfortunately, there are exceptions, and it is always better to relay on information from databases. The function bellow takes a vector of human genes, and uses biomaRt to return the ...


1

The ID history converter is available as a Perl script. This script accesses these MySQL tables, so you could query these tables directly. Note that the mapping session is only one release at a time, so you would need to run 25 queries to go from release 75 to 100.


1

You can use GSA.read.gmt function from GSA package. The following code can be used to convert the file to a dataframe. Just ignore the warnings. Original_response library(GSA) data <- GSA.read.gmt("c5.all.v6.2.symbols.gmt") gene_names <- unlist(data$genesets, use.names=FALSE) your_dataframe <- cbind(data$geneset.names,gene_names) colnames(...


1

You can import the csv file as a table. This does not require further libraries: #you're reading a csv file, using tab as field separator and considering the first line as the headers of the data table data <- read.csv(filename, sep="\t", header=TRUE) then what you need are just two columns of your data: colnames(data) #will give you the names of the ...


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