Dorado

Dorado is a high-performance, easy-to-use, open source basecaller for Oxford Nanopore reads.

Features

One executable with sensible defaults, automatic hardware detection and configuration.
Runs on Apple silicon (M1/2 family) and Nvidia GPUs including multi-GPU with linear scaling.
Modified basecalling (Remora models).
Duplex basecalling.
POD5 support for highest basecalling performance.
Based on libtorch, the C++ API for pytorch.
Multiple custom optimisations in CUDA and Metal for maximising inference performance.

If you encounter any problems building or running Dorado please report an issue.

Installation

Platforms

Dorado has been tested on the following systems:

Platform	GPU/CPU
Windows	(G)V100, A100
Apple	M1, M1 Pro, M1 Max, M1 Ultra
Linux	(G)V100, A100

Systems not listed above but which have Nvidia GPUs with >=8GB VRAM and architecture from Volta onwards have not been widely tested but are expected to work. If you encounter problems with running on your system please report an issue

Roadmap

Dorado is still in alpha stage and not feature-complete, the following features form the core of our roadmap:

DNA Barcode multiplexing
Alignment (output aligned BAMs).
Python API

Performance tips

For optimal performance Dorado requires POD5 file input. Please convert your Fast5 files before basecalling.
Dorado will automatically detect your GPUs' free memory and select an appropriate batch size.
Dorado will automatically run in multi-GPU ('cuda:all') mode. If you have a hetrogenous collection of GPUs select the faster GPUs using the --device flag (e.g --device "cuda:0,2). Not doing this will have a detrimental impact on performance.

Running

To run Dorado, download a model and point it to POD5 files (Fast5 files are supported but will not be as performant).

$ dorado download --model dna_r10.4.1_e8.2_400bps_hac@v4.1.0
$ dorado basecaller dna_r10.4.1_e8.2_400bps_hac@v4.1.0 pod5s/ > calls.sam

To call modifications simply add --modified-bases.

$ dorado basecaller dna_r10.4.1_e8.2_400bps_hac@v4.1.0 pod5s/ --modified-bases 5mCG_5hmCG > calls.sam

For unaligned BAM output, dorado output can be piped to BAM using samtoools:

$ dorado basecaller dna_r10.4.1_e8.2_400bps_hac@v4.1.0 pod5s/ | samtools view -Sh > calls.bam

Stereo Duplex Calling:

$ dorado duplex dna_r10.4.1_e8.2_400bps_sup@v4.1.0 pod5s/ --pairs pairs.txt > duplex.sam

See duplex-tools for creating a pairs.txt.

Available basecalling models

To download all available dorado models run:

$ dorado download --model all

The latest simplex models are v4.1.0:

dna_r10.4.1_e8.2_260bps_fast@v4.1.0
dna_r10.4.1_e8.2_260bps_hac@v4.1.0
dna_r10.4.1_e8.2_260bps_sup@v4.1.0
dna_r10.4.1_e8.2_400bps_fast@v4.1.0
dna_r10.4.1_e8.2_400bps_hac@v4.1.0
dna_r10.4.1_e8.2_400bps_sup@v4.1.0

The following models are also available:

dna_r10.4.1_e8.2_260bps_fast@v4.0.0
dna_r10.4.1_e8.2_260bps_hac@v4.0.0
dna_r10.4.1_e8.2_260bps_sup@v4.0.0
dna_r10.4.1_e8.2_400bps_fast@v4.0.0
dna_r10.4.1_e8.2_400bps_hac@v4.0.0
dna_r10.4.1_e8.2_400bps_sup@v4.0.0
dna_r10.4.1_e8.2_260bps_fast@v3.5.2
dna_r10.4.1_e8.2_260bps_hac@v3.5.2
dna_r10.4.1_e8.2_260bps_sup@v3.5.2
dna_r10.4.1_e8.2_400bps_fast@v3.5.2
dna_r10.4.1_e8.2_400bps_hac@v3.5.2
dna_r10.4.1_e8.2_400bps_sup@v3.5.2
dna_r9.4.1_e8_fast@v3.4
dna_r9.4.1_e8_hac@v3.3
dna_r9.4.1_e8_sup@v3.3

Developer quickstart

Linux dependencies

The following packages are necessary to build dorado in a barebones environment (e.g. the official ubuntu:jammy docker image)

$ apt-get update && apt-get install -y --no-install-recommends \
        curl \
        git \
        ca-certificates \
        build-essential \
        nvidia-cuda-toolkit \
        libhdf5-dev \
        libssl-dev \
        libzstd-dev \
        cmake \
        autoconf \
        automake

Clone and build

$ git clone https://github.com/nanoporetech/dorado.git dorado
$ cd dorado
$ cmake -S . -B cmake-build
$ cmake --build cmake-build --config Release -j
$ ctest --test-dir cmake-build

The -j flag will use all available threads to build dorado and usage is around 1-2GB per thread. If you are constrained by the amount of available memory on your system you can lower the number of threads i.e. -j 4.

After building you can run dorado from the build directory ./cmake-build/bin/dorado or install it somewhere else on your system i.e. /opt (note: you will need the relevant permissions for the target installation directory).

$ cmake --install cmake-build --prefix /opt

Pre commit

The project uses pre-commit to ensure code is consistently formatted, you can set this up using pip:

$ pip install pre-commit
$ pre-commit install

Licence and Copyright

Dorado is distributed under the terms of the Oxford Nanopore Technologies, Ltd. Public License, v. 1.0. If a copy of the License was not distributed with this file, You can obtain one at http://nanoporetech.com

oracle5th / dorado