This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!
Project Description

I wanted a better way of exploring and downloading raw data from the ENCODE project.

For example, I’d like to get the BAM files for all ChIP-seq experiments done in uninduced MEL cells (from the mm9 assembly).

One strategy would be to individually go through each track hub (e.g., histone mods from LICR,, filter data, and download files individually.

Another strategy would be to go directly to the download page ( and extract the files that end in .bam.

This small package takes advantage of the files.txt files (here’s an example) that describe all the metadata on the download page.

The files.txt files are downloaded from each ENCODE track hub in the assembly of interest. Then these files are parsed and concatenated together into one big pandas.DataFrame that can be used to find the data you care about.


pip install encode-dataframe


Mirror the files. This may take a minute or so. If you’ve cloned the git repo, you already have a copy of the mm9 files.

>>> import encode_dataframe as edf
>>> edf.mirror_metadata_files('mm9')

Create a large DataFrame:

>>> df = edf.encode_dataframe('mm9')
>>> len(df)

Armed with the dataframe, we can now slice and dice to get the data we care about. Eventually I’d like to run a ChromHMM segmentation on MEL cells, but I need to get the data first …

Choose a cell type

>>> interesting = df.cell == 'MEL'

And only BAM files

>>> interesting &= df.type == 'bam'

And only ChIP- or DNase-seq

>>> interesting &= df.dataType.isin(['ChipSeq', 'DnaseSeq'])

And only untreated (in this case, uninduced) cells:

>>> interesting &= df.treatment != 'DMSO_2.0pct'

And only one replicate (some have 2 or 3)

>>> interesting &= df.replicate == '1'

And only those that don’t have some issue with them (looks like older versions have some text in the objStatus field):

>>> interesting &= df.objStatus.isnull()

How many do we have to work with?

>>> m = df[interesting]
>>> len(m)

Some of these are controls (input or IgG), and there are some duplicates (looks like H3K4me3 ChIP-seq uses 2 different controls; CTCF was done by different groups). How many unique antibodies?

>>> len(m.antibody.unique())

So here are the files I should download:

>>> urls = m.url.values
Release History

Release History


This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

Download Files

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
encode_dataframe-0.1-py2.py3-none-any.whl (5.5 kB) Copy SHA256 Checksum SHA256 2.7 Wheel Aug 22, 2014
encode-dataframe-0.1.tar.gz (3.3 kB) Copy SHA256 Checksum SHA256 Source Aug 22, 2014

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting