DeepConsensus

DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

Installation

From pip package

If you're on a GPU machine:

pip install deepconsensus[gpu]==0.2.0
# To make sure the `deepconsensus` CLI works, set the PATH:
export PATH="/home/${USER}/.local/bin:${PATH}"

If you're on a CPU machine:

pip install deepconsensus[cpu]==0.2.0
# To make sure the `deepconsensus` CLI works, set the PATH:
export PATH="/home/${USER}/.local/bin:${PATH}"

From Docker image

For GPU:

sudo docker pull google/deepconsensus:0.2.0-gpu

For CPU:

sudo docker pull google/deepconsensus:0.2.0

From source

git clone https://github.com/google/deepconsensus.git
cd deepconsensus
source install.sh

If you have GPU, run source install-gpu.sh instead. Currently the only difference is that the GPU version installs tensorflow-gpu instead of intel-tensorflow.

(Optional) After source install.sh, if you want to run all unit tests, you can do:

./run_all_tests.sh

Usage

See the quick start.

Where does DeepConsensus fit into my pipeline?

After a PacBio sequencing run, DeepConsensus is meant to be run on the subreads to create new corrected reads in FASTQ format that can take the place of the CCS reads for downstream analyses.

See the quick start for an example of inputs and outputs.

How to cite

If you are using DeepConsensus in your work, please cite:

DeepConsensus: Gap-Aware Sequence Transformers for Sequence Correction

Disclaimer

This is not an official Google product.

NOTE: the content of this research code repository (i) is not intended to be a medical device; and (ii) is not intended for clinical use of any kind, including but not limited to diagnosis or prognosis.