Installation

pip install encode-dataframe

Usage

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)
5865

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)
60

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())
46

So here are the files I should download:

>>> urls = m.url.values