Signal-based demultiplexing of direct RNA sequencing reads using convolutional neural networks

IMPORTANT WARNING

Please note: If using GPU mode, please check which CUDA version you are using!

• DeePlexiCon works with CUDA v10.
• DeePlexiCon does NOT currently work with CUDA v11, which is typically used by latest GPU cards.
• DeePlexiCon does NOT currently work with RNA flowcells/RNA-004 kits.
• We will be offering a solution to demultiplex direct RNA reads using RNA flowcells/RNA-004 kits, CUDA v11 and/or latest GPU cards very soon. Thanks!

Table of Contents

• About DeePlexiCon
• Documentation
• Installation
• Releases and Usage
• How to build barcoded direct RNA sequencing libraries
• Dependencies and versions
• Citing this work
• Contact

About DeePlexiCon

DeePlexiCon is a tool to demultiplex barcoded direct RNA sequencing reads from Oxford Nanopore Technologies. Please note that the software has been tested and validated with a set of 4x20bp barcodes listed below:

• Barcode 1: GGCTTCTTCTTGCTCTTAGG
• Barcode 2: GTGATTCTCGTCTTTCTGCG
• Barcode 3: GTACTTTTCTCTTTGCGCGG
• Barcode 4: GGTCTTCGCTCGGTCTTATT

Please see below further instructions about how to build barcoded direct RNA libraries.

What's included

• Script to demultiplex direct RNA fast5 reads, barcoded using the strategy described above
• Example fast5 data built using the 4 custom barcoded adaptors

Documentation

• Full documentation: https://psy-fer.github.io/deeplexicon/
• Preprint: https://www.biorxiv.org/content/10.1101/864322v2.abstract
• Publication in Genome Research: https://genome.cshlp.org/content/30/9/1345

Installation

For Ubuntu 16.04

add python 3.7 repo (not on default 16.04 ppa repos)

sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt-get update
sudo apt install python3.7 python3.7-dev python3.7-venv

Linux with python3.7

Create environtment

python3.7 -m venv ./Deeplexicon/

clone git repository

git clone https://github.com/Psy-Fer/deeplexicon.git

source and install requirements CPU

source Deeplexicon/bin/activate
pip install ont_fast5_api h5py==2.10 Keras==2.2.4 Pandas PyTs==0.8.0 Scikit-learn numba==0.53 TensorFlow==1.13.1

Source and install requirements GPU (experimental)

Demultiplexing on GPU will be ~10x faster compared to CPU.

1. Install cuda-10-0 and cuDNN v7.6.

# install cuda-10-0
sudo apt install cuda-10-0
# cuDNN needs to be downloaded & installed manually from https://developer.nvidia.com/rdp/cudnn-archive

2. Install dependencies

mkdir -p ~/src
git clone https://github.com/Psy-Fer/deeplexicon.git

python3 -m venv ~/src/venv/deeplexicon-gpu
source ~/src/venv/deeplexicon-gpu/bin/activate
pip install h5py==2.10 Keras==2.2.4 Pandas PyTs==0.8.0 Scikit-learn numba==0.53 TensorFlow-gpu==1.13.1

3. Enjoy!

source ~/src/venv/deeplexicon-gpu
# single-core version
time ~/src/deeplexicon/deeplexicon_sub.py dmux -g -p Fast5_folder -m ~/src/deeplexicon/models/resnet20-final.h5 > demux-gpu.tsv

# or multi-threaded version
time ~/src/deeplexicon/deeplexicon_multi.py dmux -g --threads 4 -p Fast5_folder -m ~/src/deeplexicon/models/resnet20-final.h5 > demux_multi-gpu.tsv

Docker images

You can find Docker images for CPU and GPU.

time docker run -u $UID:$GID -v /path_to_fast:/data lpryszcz/deeplexicon:1.2.0 deeplexicon_multi.py dmux --threads 2 -p /data -m deeplexicon/models/resnet20-final.h5 > docker.demux2.tsv

# or using GPU version - you'll need to have nvidia-docker and CUDA installed
time docker run --gpus all -u $UID:$GID -v /path_to_fast:/data lpryszcz/deeplexicon:1.2.0-gpu deeplexicon_sub.py dmux -p /data -m deeplexicon/models/resnet20-final.h5 > $d.demux.docker-gpu.tsv

Comparison of runtimes

Version 1.2.0: optimisation of segmentation (10x speed-up) and gpu support.

• v1.1.0 103m (user: 184m)
• v1.2.0 22:43 (user: 142m)
• v1.2.0 gpu 2:49 (user: 2:48)
• v1.2.0 gpu --threads 2 2:05 (user 5:46)

Releases and Usage

Version 1.1.0 (pre-release)

Demultiplex the reads (fast5 input)

python3 deeplexicon.py dmux -p ~/top/fast5/path/ -f multi -m models/resnet20-final.h5 > output.tsv

Split fastq

python3 deeplexicon.py split -i output.tsv -q combined.fastq -o dmux_folder/ -s sample_name

Train a new model

Please see full documentation for further explanation

python deeplexicon.py train --path /fast5/top/path/ --train_truth train.tsv --test_truth test.tsv --val_truth val.tsv

Version 1.0.0 (stable)

Running the software

python3 deeplexicon.py -p ~/top/fast5/path/ -f multi -m models/resnet20-final.h5 > output.tsv

Split fastq

python3 fastq_splitter.py -d output.tsv -q combined.fastq -o dmux_folder/ -s sample_name

Please note, the current algorithm has been trained to demultiplex the 4 barcodes shown above. It will not accurately demultiplex reads if different sequences are used.

How to build barcoded direct RNA sequencing libraries

To build the barcoded libraries, the oligo DNA sequences listed below should be used instead of those coming with the direct RNA sequencing kit (RTA). The barcode is embedded in the oligoA sequence, which will be ligated to the RNA molecule during the library preparation.

These oligos are designed to barcode libraries which have been enriched with oligodT beads (i.e. RNA should have polyA tail to anneal to oligoB). Each oligoA matches an oligoB.

OligoA :

• OligoA_shuffle1: 5'-/5Phos/GGCTTCTTCTTGCTCTTAGGTAGTAGGTTC-3' (same as in ONT RTA):
• OligoA_shuffle2: 5'-/5Phos/GTGATTCTCGTCTTTCTGCGTAGTAGGTTC-3'
• OligoA_shuffle3: 5'-/5Phos/GTACTTTTCTCTTTGCGCGGTAGTAGGTTC-3'
• OligoA_shuffle4: 5'-/5Phos/GGTCTTCGCTCGGTCTTATTTAGTAGGTTC-3'

OligoB:

• OligoB_shuffle1: 5’-GAGGCGAGCGGTCAATTTTCCTAAGAGCAAGAAGAAGCCTTTTTTTTTT-3’ (same as in ONT RTA)
• OligoB_shuffle2: 5’-GAGGCGAGCGGTCAATTTTCGCAGAAAGACGAGAATCACTTTTTTTTTT-3’
• OligoB_shuffle3: 5’-GAGGCGAGCGGTCAATTTTCCGCGCAAAGAGAAAAGTACTTTTTTTTTT-3’
• OligoB_shuffle4: 5’-GAGGCGAGCGGTCAATTTTAATAAGACCGAGCGAAGACCTTTTTTTTTT-3’

Dependencies and versions

Please note, if using GPU mode, DeePlexiCon only works with CUDA v10!!! (it does not work properly with CUDA v11, we are currently working on offering a solution for this, should be released early 2024)

Additional information:

Full library versions used:

absl-py==0.7.1
astor==0.8.0
cycler==0.10.0
gast==0.2.2
google-pasta==0.1.7
grpcio==1.22.0
h5py==2.9.0
joblib==0.13.2
Keras==2.2.4
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
kiwisolver==1.1.0
llvmlite==0.29.0
Markdown==3.1.1
matplotlib==3.1.1
numba==0.45.0
numpy==1.17.0
pandas==0.25.0
protobuf==3.9.1
pyparsing==2.4.2
python-dateutil==2.8.0
pyts==0.8.0
pytz==2019.2
PyYAML==5.1.2
scikit-learn==0.21.3
scipy==1.3.1
six==1.12.0
tensorboard==1.14.0
tensorflow==1.14.0
tensorflow-estimator==1.14.0
termcolor==1.1.0
Werkzeug==0.15.5
wrapt==1.11.2

Citing this work:

If you find this work useful, please cite:

Smith MA*, Ersavas T*, Ferguson JM*, Liu H, Lucas MC, Begik O, Bojarski L, Barton K and Novoa EM. Molecular barcoding of native RNAs using nanopore sequencing and deep learning. Genome Res. 2020;30(9):1345-1353. doi:10.1101/gr.260836.120