HapDup
HapDup (haplotype duplicator) is a pipeline to convert a haploid long read assembly into a dual diploid assembly. The reconstructed haplotypes preserve heterozygous structural variants (in addition to small variants) and are locally phased.
Version 0.4
Input requirements
HapDup takes as input a haploid long-read assembly, such as produced with Flye or Shasta. Currenty, ONT reads (Guppy 5+ recommended) and PacBio HiFi reads are supported.
HapDup is currently designed for low-heterozygosity genomes (such as human). The expectation is that the assembly has most of the diploid genome collapsed into a single haplotype. For assemblies with partially resolved haplotypes, alternative alleles could be removed prior to running the pipeline using purge_dups. We expect to add a better support of highly heterozygous genomes in the future.
The first stage is to realign the original long reads on the assembly using minimap2. We recommend to use the latest minimap2 release.
minimap2 -ax map-ont -t 30 assembly.fasta reads.fastq | samtools sort -@ 4 -m 4G > lr_mapping.bam
samtools index -@ 4 assembly_lr_mapping.bam
Quick start using Docker
HapDup is available on the Docker Hub.
If Docker is not installed in your system, you need to set it up first following this guide.
Next steps assume that your assembly.fasta and lr_mapping.bam are in the same directory, which will also be used for HapDup output. If it is not the case, you might need to bind additional directories using the Docker's -v / --volume argument. The number of threads (-t argument) should be adjusted according to the available resources. For PacBio HiFi input, use --rtype hifi instead of --rtype ont.
cd directory_with_assembly_and_alignment
HD_DIR=`pwd`
docker run -v $HD_DIR:$HD_DIR -u `id -u`:`id -g` mkolmogo/hapdup:0.4 \
hapdup --assembly $HD_DIR/assembly.fasta --bam $HD_DIR/lr_mapping.bam --out-dir $HD_DIR/hapdup -t 64 --rtype ont
Quick start using Singularity
Alternatively, you can use Singularity. First, you will need install the client as descibed in the manual. One way to do it is through conda:
conda install singularity
Next steps assume that your assembly.fasta and lr_mapping.bam are in the same directory, which will also be used for HapDup output. If it is not the case, you might need to bind additional directories using the --bind argument. The number of threads (-t argument) should be adjusted according to the available resources. For PacBio HiFi input, use --rtype hifi instead of --rtype ont.
singularity pull docker://mkolmogo/hapdup:0.4
HD_DIR=`pwd`
singularity exec --bind $HD_DIR hapdup_0.4.sif \
hapdup --assembly $HD_DIR/assembly.fasta --bam $HD_DIR/lr_mapping.bam --out-dir $HD_DIR/hapdup -t 64 --rtype ont
Output files
The output directory will contain:
haplotype_{1,2}.fasta- final assembled haplotypesphased_blocks_hp{1,2}.bed- phased blocks coordinates
Haplotypes generated by the pipeline contain homozogous and heterozygous varinats (small and structural). Becuase the pipeline is only using long-read (ONT) data, it does not achieve chromosome-level phasing. Fully-phased blocks are given in the the phased_blocks* files.
Pipeline overview
-
HapDup starts with filtering alignments that are likely originating from the unassembled parts of the genome. Such alignments may later create false haplotypes if not removed (e.g. if reads from a segmental duplication with two copies can create four haplotypes).
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Afterwards, PEPPER is used to call SNPs from the filtered alignment file
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Then we use Margin to phase SNPs and haplotype reads
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We then use Flye to polish the initiall assembly with the reads from each of the two haplotypes independently
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Finally, we find (heterozygous) breakpoints in long-read alignments and apply the corresponding structural changes to the corresponding polished haplotypes. Currently, it allows to recover large heterozygous inversions.
Benchmarks
We evaluated HapDup haplotypes in terms of reconstructed structural variants signatures (heterozygous & homozygous) using the HG002 for which the curated set of SVs is available. We used the recent ONT data basecalled with Guppy 5.
Given HapDup haplotypes, we called SV using dipdiff. We also compare SV set against hifiasm assemblies, even though they were produced from HiFi, rather than ONT reads. Evaluated using truvari with -r 2000 option. GT refers to genotype-considered benchmarks.
| Method | Precision | Recall | F1-score | GT Precision | GT Recall | GT F1-score |
|---|---|---|---|---|---|---|
| Shasta+HapDup | 0.9500 | 0.9551 | 0.9525 | 0.934 | 0.9543 | 0.9405 |
| Sniffles | 0.9294 | 0.9143 | 0.9219 | 0.8284 | 0.9051 | 0.8605 |
| CuteSV | 0.9324 | 0.9428 | 0.9376 | 0.9119 | 0.9416 | 0.9265 |
| hifiasm | 0.9512 | 0.9734 | 0.9622 | 0.9129 | 0.9723 | 0.9417 |
Yak k-mer based evaluations:
| Hap | QV | Switch err | Hamming err |
|---|---|---|---|
| 1 | 35 | 0.0389 | 0.1862 |
| 2 | 35 | 0.0385 | 0.1845 |
Given a minimap2 alignment, HapDup runs in ~400 CPUh and uses ~80 Gb of RAM.
Source installation
If you prefer, you can install from source as follows:
#create a new conda environemnt and activate it
conda create -n hapdup python=3.8
conda activate hapdup
#get HapDup source
git clone https://github.com/fenderglass/hapdup
cd hapdup
git submodule update --init --recursive
#build and install Flye
pushd submodules/Flye/ && python setup.py install && popd
#build and install Margin
pushd submodules/margin/ && mkdir build && cd build && cmake .. && make && cp ./margin $CONDA_PREFIX/bin/ && popd
#build and install PEPPER and its dependencies
pushd submodules/pepper/ && python -m pip install . && popd
To run, ensure that the conda environemnt is activated and then execute:
conda activate hapdup
./hapdup.py --assembly assembly.fasta --bam lr_mapping.bam --out-dir hapdup -t 64 --rtype ont
Acknowledgements
The major parts of the HapDup pipeline are:
Authors
The pipeline was developed at UC Santa Cruz genomics institute, Benedict Paten's lab.
Pipeline code contributors:
- Mikhail Kolmogorov
PEPPER/Margin/Shasta support:
- Kishwar Shafin
- Trevor Pesout
- Paolo Carnevali
Citation
If you use HapDup in your research, the most relevant papers to cite are:
Kishwar Shafin, Trevor Pesout, Pi-Chuan Chang, Maria Nattestad, Alexey Kolesnikov, Sidharth Goel, Gunjan Baid et al. "Haplotype-aware variant calling enables high accuracy in nanopore long-reads using deep neural networks." bioRxiv (2021). doi:10.1101/2021.03.04.433952
Mikhail Kolmogorov, Jeffrey Yuan, Yu Lin and Pavel Pevzner, "Assembly of Long Error-Prone Reads Using Repeat Graphs", Nature Biotechnology, 2019 doi:10.1038/s41587-019-0072-8
License
HapDup is distributed under a BSD license. See the LICENSE file for details. Other software included in this discrubution is released under either MIT or BSD licenses.
How to get help
A preferred way report any problems or ask questions is the issue tracker.
In case you prefer personal communication, please contact Mikhail at [email protected].
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