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Using EnsDb's annotation database in Python

I found that there isn’t a systematic way to query and convert genomic annotation IDs in Python. At least there isn’t one as good as what R/Bioconductor currently has. If you’ve never heard of R/Bioconductor annotation tool stack before, check out the official workflow or my post in 2016 specific for querying Ensembl annotations.

Although I enjoy using R for genomic annotation conversion, a few days ago I wanted to do the same thing inside my text processing script in Python. I might be able to re-write the script in R but I feel like R is not really the right tool for this task and on top of it, I don’t know how to write an efficent text processing in R1.

Knowing the fact that all annotations in R are stored in single-file SQLite databases, I should be able to connect the database directly Python or any other language and wirte SQL query to retrieve the same information. So my question now becomes to how to extract or find the path to the databases. Turn out that many new Bioconductor annotation packages are hosted via AnnotationHub, and user can search for the annotation package and retrieve them locally by their ID. For example, all the recent Ensembl releases, e.g., EnsDb.Hsapiens.vXX, are available on AnnotationHub.

After digging around a bit, I am able to query the AnnotationHub, download the correct EnsDB SQLite database file, and make SQL queries for the annotation ID conversion without any R package. I will share the details in the rest of the post.

But before we start with the details, I want to clarify that it wasn’t my intention to persuade people away from the current R ecosystem. The current R ecosystem is great and I will recommend people to stick with it as much as you can. I am pretty sure I will hit a lot of issues if I want to do more complex analysis or queries without the help of what R packages provide.

Manual query in AnnotationHub

When one wants to use the R package AnnotationHub, the common usage is

ah <- AnnotationHub()
## snapshotDate(): 2017-10-27

query(ah, c("EnsDb", "Homo sapiens"))

The function call AnnotationHub() will download the latest version of the metadata of all available annotation object. The subsequent query(...) function will talk to the local metadata database.

Now let’s do it manually without any R function calls.

The default AnnotationHub is at By visiting the page we can find several relevant endpoints:

  • /metadata/annotationhub.sqlite3
  • /fetch/:id # id =>

So as long as we get the of the EnsDb using the metadata, we can download it via the /fetch/:id endpoint.

After downloading the metadata database, we can inspect it in SQLite3 by connecting it directly:

sqlite3 annotationhub.sqlite3

Some useful commands to inspect a foreign database (or the ultimate help command .help):

sqlite> .header on 
sqlite> .mode column
sqlite> .tables
biocversions       rdatapaths         schema_info        test             
input_sources      recipes            statuses           timestamp        
location_prefixes  resources          tags
sqlite> .schema rdatapaths
CREATE TABLE `rdatapaths`(`id` integer DEFAULT (NULL) NOT NULL PRIMARY KEY , `rdatapath` varchar(255) DEFAULT (NULL) NULL, `rdataclass` varchar(255) DEFAULT (NULL) NULL, `resource_id` integer DEFAULT (NULL) NULL, `dispatchclass` varchar(255) DEFAULT (NULL) NULL, CONSTRAINT `rdatapaths_ibfk_1` FOREIGN KEY (`resource_id`) REFERENCES `resources`(`id`));
CREATE INDEX `rdatapaths_resource_id` ON `rdatapaths` (`resource_id`);

So let’s make a SQL query to find all Human’s EnsDb:

SELECT r.ah_id, AS rdatapaths_id, rdp.rdatapath, r.title
FROM resources AS r
JOIN rdatapaths AS rdp
ON = rdp.resource_id
WHERE r.title LIKE '%EnsDb for Homo Sapiens%';
-- ah_id       rdatapaths_id  rdatapath                               title                            
-- ----------  -------------  --------------------------------------  -- ---------------------------------
-- AH53211     59949          AHEnsDbs/v87/EnsDb.Hsapiens.v87.sqlite  Ensembl 87 EnsDb for Homo Sapiens
-- AH53715     60453          AHEnsDbs/v88/EnsDb.Hsapiens.v88.sqlite  Ensembl 88 EnsDb for Homo Sapiens
-- AH56681     63419          AHEnsDbs/v89/EnsDb.Hsapiens.v89.sqlite  Ensembl 89 EnsDb for Homo Sapiens
-- AH57757     64495          AHEnsDbs/v90/EnsDb.Hsapiens.v90.sqlite  Ensembl 90 EnsDb for Homo Sapiens

All the Ensembl releases 87+ are available! I will use the release 90 for example. we can download it by its rdatapaths id:

wget -O EnsDb.Hsapiens.v90.sqlite

For older Ensembl release, one may need to build the SQLite database based by the instructions from ensembldb. For the last GRCh37 release, Ensembl release 75, one can download the source of the Bioconductor annotation package EnsDb.Hsapiens.v75 and extract it. The database will be under inst/extdata.

Manual query in EnsDB

EnsDb SQLite database are Ensembl annotation databases created by the R package ensembldb.

Here I will show how to find a transcript’s gene name, its genomic location, and all its exon locations given its Ensembl transcript ID.

First connect the database by sqlite3 EnsDb.Hsapiens.v90.sqlite. Its table design is very straightforward:

sqlite> .tables
chromosome      exon            metadata        protein_domain  tx2exon
entrezgene      gene            protein         tx              uniprot

So it didn’t take me long to figure out how to join the transcript and gene information:

SELECT tx.tx_id, tx.gene_id, gene.gene_name, seq_name, seq_strand
FROM tx JOIN gene ON tx.gene_id = gene.gene_id
WHERE tx_id='ENST00000358731';
-- tx_id            gene_id          gene_name   seq_name    seq_strand
-- ---------------  ---------------  ----------  ----------  ----------
-- ENST00000358731  ENSG00000145734  BDP1        5           1

And for the genomic ranges of its exon:

SELECT tx_id, exon_idx, exon_seq_start, exon_seq_end
FROM tx2exon JOIN exon ON tx2exon.exon_id = exon.exon_id
WHERE tx_id = 'ENST00000380139'
ORDER BY exon_idx;
-- tx_id            exon_idx    exon_seq_start  exon_seq_end
-- ---------------  ----------  --------------  ------------
-- ENST00000380139  1           32427904        32428133    
-- ENST00000380139  2           32407645        32407772    
-- ENST00000380139  3           32407250        32407338    
-- ENST00000380139  4           32404203        32404271    
-- ENST00000380139  5           32400723        32403200  

All the coordinates are 1-based and the ranges are inclusive.


By downloading the underlying annotation database, one can do the same annotation query out of R language and sometimes it may be helpful. I feel like instead of trying to come up with my own layout of annotation mapping across multiple sources, it is more reliable to use a more official build. On the other hand, it is very hard to get the annotaion mapping correct and there are tons of corner cases that require careful and systematic decisions. So I don’t really recommend to build my own mapping at the first place anyway. The method here should help the situation of annotation query out of R a bit.

Potentially one can try copy the full R infrastructure but using the same underlying database and replicate the same experience to other languages, but it might require substantial work to get the infrastructure done and correct.

EDIT 2017-12-13: Add instructions of using older Ensembl release.

  1. Based on my impression, my R expert friends would probably recommend me to write it with R-cpp, which I think would be over-kill for such a small task. But my impression can be wrong. Feel free to share your thoughts!