dnarecords 0.1.0

Genomics data ML ready

DNARecords

Genomics data ML ready.

Transform your vcf, bgen, etc. genomics datasets into a sample wise format so that you can use it for Deep Learning models.

Installation

DNARecords package has two main dependencies:

• Hail, if you are transforming your genomics data into DNARecords
• Tensorflow, if you are using a previously DNARecords dataset, for example, to train a DL model

As you may know, Tensorflow and Spark does not play very well together on a cluster with more than one machine.

However, dnarecords package needs to be installed only on the driver machine of a Hail cluster.

For that reason, we recommend following these installation tips.

On a dev environment

$ pip install dnarecords

On a Hail cluster or summiting a job to it

You will already have Pyspark installed and will not intend to install Tensorflow.

So, just install dnarecords without dependencies on the driver machine.

There will be no missing modules as soon as you use the classes and functionality intended for Spark.

$ /opt/conda/miniconda3/bin/python -m pip install dnarecords --no-deps

Note: assuming Hail python executable is /opt/conda/miniconda3/bin/python

On a Tensorflow environment or summiting a job to it

You will already have Tensorflow installed and will not intend to install Pyspark.

So, just install dnarecords without dependencies.

There will be no missing modules as soon as you use the classes and functionality intended for Tensorflow.

$ pip install dnarecords --no-deps

Working on Google Dataproc

Just use and initialization action that installs dnarecords without dependencies.

$ hailctl dataproc start dnarecords --init gs://dnarecords/dataproc-init.sh

Iy you need to work with other cloud providers, refer to Hail docs.

Usage

It is quite straightforward to understand the functionality of the package.

Given some genomics data, you can transform it into a DNARecords Dataset this way:

import dnarecords as dr


hl = dr.helper.DNARecordsUtils.init_hail()
hl.utils.get_1kg('/tmp/1kg')
mt = hl.read_matrix_table('/tmp/1kg/1kg.mt')
mt = mt.annotate_entries(dosage=hl.pl_dosage(mt.PL))

dnarecords_path = '/tmp/dnarecords'
writer = dr.writer.DNARecordsWriter(mt.dosage)
writer.write(dnarecords_path, sparse=True, sample_wise=True, variant_wise=True,
             tfrecord_format=True, parquet_format=True,
             write_mode='overwrite', gzip=True)

Given a DNARecords Dataset, you can read it as Tensorflow Datasets this way:

import dnarecords as dr


dnarecords_path = '/tmp/dnarecords'
reader = dr.reader.DNARecordsReader(dnarecords_path)
samplewise_ds = reader.sample_wise_dataset()
variantwise_ds = reader.variant_wise_dataset()

Or, given a DNARecords Dataset, you can read it as Pyspark DataFrames this way:

import dnarecords as dr


dnarecords_path = '/tmp/dnarecords'
reader = dr.reader.DNASparkReader(dnarecords_path)
samplewise_df = reader.sample_wise_dnarecords()
variantwise_df = reader.variant_wise_dnarecords()

We will provide more examples and integrations soon.

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

dnarecords was created by Atray Dixit, Andrés Mañas Mañas, Lucas Seninge. It is licensed under the terms of the MIT license.

Credits

dnarecords was created with cookiecutter and the py-pkgs-cookiecutter template.