HERRO

HERRO (Haplotype-aware ERRor cOrrection) is a highly accurate, haplotype-aware, deep-learning tool for error correction of Nanopore R10.4.1, Kit 14 reads (length of ≥ 10000bp is recommended). An experimental model for R9.4.1 data is also provided for download.

Requirements

• Linux OS (tested on RHEL 8.6 and Ubuntu 22.04)
• Zstandard
• Python (and conda) for data preprocessing

Compile from source

• libtorch 2.0.*
• rustup

Installation

0. Clone the repository

git clone https://github.com/dominikstanojevic/herro.git
cd herro

1. Create conda environment

conda env create --file scripts/herro-env.yml

2. Build herro binary (singularity or compile from source)

   a. Download singularity image:

   1. Download the image

   wget -O herro.sif https://zenodo.org/records/13802680/files/herro.sif?download=1

   b. Build singularity image (requires sudo)

   sudo singularity build herro.sif herro-singularity.def

   Run the tool (see Usage) with: singularity run --nv --bind <host_path>:<dest_path> herro.sif inference <args>

   c. Compile

   When compiling from source, ensure that libtorch and rustup are downloaded and installed.

   export LIBTORCH=<libtorch_path>
   export LD_LIBRARY_PATH=$LIBTORCH/lib:$LD_LIBRARY_PATH
   RUSTFLAGS="-Ctarget-cpu=native" cargo build -q --release

   Path to the resulting binary: target/release/herro

   Building the Singularity image or compiling from source takes just a few minutes on a standard machine.

Model Download

1. Download model:

For R10.4.1 data,

wget -O model_R10_v0.1.pt https://zenodo.org/records/12683277/files/model_v0.1.pt?download=1

For R9.4.1 data (experimental),

wget -O model_R9_v0.1.pt https://zenodo.org/records/12683277/files/model_R9_v0.1.pt?download=1

Models can also be found on Zenodo: https://zenodo.org/records/12683277

Usage

1. Preprocess reads

scripts/preprocess.sh <input_fastq> <output_prefix> <number_of_threads> <parts_to_split_job_into>

Note: Porechop loads all reads into memory, so the input may need to be split into multiple parts. Set <parts_to_split_job_into> to 1 if splitting is not needed. In Dorado v0.5, adapter trimming was added, so adapter trimming and splitting using Porechop and duplex tools will probably be removed in the future.

2. minimap2 alignment and batching

Although minimap2 can be run from the herro binary (omit --read-alns or use --write-alns to store batched alignments for future use), the preferred method is to initially run minimap2 and then utilize it to generate alignment batches. These batches will be used as input for the herro binary.

scripts/create_batched_alignments.sh <output_from_reads_preprocessing> <read_ids> <num_of_threads> <directory_for_batches_of_alignments> 

Note: Read ids can be obtained with seqkit: seqkit seq -ni <reads> > <read_ids>

3. Error-correction

herro inference --read-alns <directory_alignment_batches> -t <feat_gen_threads_per_device> -d <gpus> -m <model_path> -b <batch_size> <preprocessed_reads> <fasta_output> 

Note: GPUs are specified using their IDs. For example, if the value of the parameter -d is set to 0,1,3, herro will use the first, second, and fourth GPU cards. Parameter -t is given per device - e.g., if -t is set to 8 and 3 GPUs are used, herro will create 24 feature generation theads in total. Recommended batch size is 64 for GPUs with 40 GB (possibly also for 32 GB) of VRAM and 128 for GPUs with 80 GB of VRAM.

Demo data

Instructions for downloading and running the demo data can be found here.

Citing HERRO

Stanojevic, D., Lin, D., Florez De Sessions, P., & Sikic, M. (2024). Telomere-to-telomere phased genome assembly using error-corrected Simplex nanopore reads. bioRxiv, 2024-05. doi:10.1101/2024.05.18.594796

Results on HG002 data

HG002 data was assembled using hifiasm and compared to HiFi reads. Results for uncorrected reads are not given since they produce poor assembly. Currently, data is not publicly available.

Assembly results and comparison with Hifi reads and uncorrected UL are given in the table below. Assemblies were perform using:

1. Hifi reads/Duplex ONT reads/Corrected UL reads
2. Uncorrected Ultra-long ONT reads as UL reads
3. Parental Illumina data

Hifiasm command used for all experiments:

hifiasm -o <output_prefix> -t <num_threads> --ul <UL_reads> --ul-cut 10000 -1 <parent1_yak> -2 <parent2_yak> <HiFi/Duplex/Corrected UL reads>

Results

Results on Error-corrected HG002 experimental, high-accuracy, UL data

Experimental high-accuracy, UL HG002 error-corrected reads can be downloaded as below. Raw data used for the error-correction can be found here. Assemblies were done in the same way as in the previous section.

Download

1. Download error-corrected reads:

wget -O HG002.experimentalUL.corrected.fasta.gz https://zenodo.org/records/13252378/files/HG002.experimentalUL.corrected.fasta.gz?download=1

Results

Assembly results and comparison with Hifi reads and uncorrected UL are given in the table below. Assemblies were perform using:

1. Hifi/HQ Uncorrected UL/Corrected UL reads
2. HQ Uncorrected Ultra-long ONT reads as UL reads
3. Parental Illumina data

Acknowledgements

This work has been supported by AI Singapore 100 Experiments (100E) Programme under the project AI-driven De Novo Diploid Assembler (AISG2-100E-2021-076) in collaboration with Agency for Science, Technology and Research (A*STAR), and Oxford Nanopore Technologies plc. (ONT).