This blog post (Jupyter notebook) describes various features of the file server library using genomic files hosted from the NIH Roadmap Epigenomics project.


This post describes a general walkthrough of the features of the file server. More usecases will be posted soon!

Import Measurements from File

Since large data repositories contains hundreds of files, manually adding files would be cumbersome. In order to make this process easier, we create a configuration file that lists all files with their locations. An example configuration file is described below -

Configuration file

The following is a configuration file for data hosted on the roadmap FTP server. This contains data for ChIP-seq experiments for the H3k27me3 marker in Esophagus and Sigmoid Colon tissues. Most fields in the configuration file are self explanatory.

        url: "https://egg2.wustl.edu/roadmap/data/byFileType/signal/consolidated/macs2signal/foldChange/E079-H3K27me3.fc.signal.bigwig",
        file_type: "bigwig",
        datatype: "bp",
        name: "E079-H3K27me3",
        id: "E079-H3K27me3",
        annotation: {
            group: "digestive",
            tissue: "Esophagus",
            marker: "H3K27me3"
    }, {
        url: "https://egg2.wustl.edu/roadmap/data/byFileType/signal/consolidated/macs2signal/foldChange/E106-H3K27me3.fc.signal.bigwig",
        file_type: "bigwig",
        datatype: "bp",
        name: "E106-H3K27me3",
        id: "E106-H3K27me3",
        annotation: {
            group: "digestive",
            tissue: "Sigmoid Colon",
            marker: "H3K27me3"

Once the configuration file is generated, we can import these measurements into the file server. We first create a MeasurementManager object which handles measurements from files and databases. We can then use the helper function import_files to import all measurements from this configuration file.

mMgr = MeasurementManager()
fmeasurements = mMgr.import_files(os.getcwd() + "/roadmap.json", mHandler)

Query for a genomic location

After loading the measurements, we can query the object for data in a particular genomic region using the get_data function.

result, err = await fmeasurements[1].get_data("chr11", 10550488, 11554489)

The reponse is a tuple, DataFrame that contains all results and an error if there is any.

Compute a Function over files

We can define and create new measurements that can be computed using a Numpy function over the files loaded from the previous step.


you can also write a custom statistical function, that applies to every row in the DataFrame. It must follow the same syntax as any Numpy row-apply function.

As an example to demonstrate, we can calculate the average ChIP-seq expression for H3K27me3 marker.

computed_measurement = mMgr.add_computed_measurement("computed", "avg_ChIP_seq", "Average ChIP seq expression",
                                        measurements=fmeasurements, computeFunc=numpy.mean)

After defining a computed measurement, we can query this measurement for a genomic location.

result, err = await computed_measurement.get_data("chr11", 10550488, 11554489)

Setup a REST API

Often times, developers would like to include data from genomic files into a web application for visualization or into their workflows. We can quickly setup a REST API web server from the measurements we loaded -

from epivizfileserver import setup_app
    app = setup_app(mMgr)

The REST API is an asynchronous web server that is built on top of SANIC.

Query Files from AnnotationHub

We can also use the Bioconductor’s AnnotationHub to search for files and setup the file server. We are working on simplifying this process.

Annotation Hub API is hosted at https://annotationhub.bioconductor.org/.

We first download the annotationhub sqlite database for available data resources.

wget http://annotationhub.bioconductor.org/metadata/annotationhub.sqlite3

After download the resource database from AnnotatiobnHub, we can now load the sqlite database into python and query for datasets.

import pandas
import os
import sqlite3

conn = sqlite3.connect("annotationhub.sqlite3")
cur = conn.cursor()
cur.execute("select * from resources r JOIN input_sources inp_src ON r.id = inp_src.resource_id;")
results = cur.fetchall()
pd = pandas.DataFrame(results, columns = ["id", "ah_id", "title", "dataprovider", "species", "taxonomyid", "genome",
                                        "description", "coordinate_1_based", "maintainer", "status_id",
                                        "location_prefix_id", "recipe_id", "rdatadateadded", "rdatadateremoved",
                                        "record_id", "preparerclass", "id", "sourcesize", "sourceurl", "sourceversion",
                                        "sourcemd5", "sourcelastmodifieddate", "resource_id", "source_type"])

For the purpose of the tutorial, we will filter for Sigmoid Colon (“E106”) and Esophagus (“E079”) tissues, and the ChipSeq Data for “H3K27me3” histone marker files from the roadmap epigenomics project.

roadmap = pd.query('dataprovider=="BroadInstitute" and genome=="hg19"')
roadmap = roadmap.query('title.str.contains("H3K27me3") and (title.str.contains("E106") or title.str.contains("E079"))')
# only use fc files
roadmap = roadmap.query('title.str.contains("fc")')

After filtering for resources we are interested in, we can load them into the file server using the import_ahub helper function.

mMgr = MeasurementManager()
ahub_measurements = mMgr.import_ahub(roadmap)

The rest of the process is similar as described in the beginning of this tutorial.