yekaizhou/duet

SNP-Assisted SV Calling and Phasing Using ONT

Duet: SNP-Assisted Structural Variant Calling and Phasing Using Oxford Nanopore Sequencing

---

Introduction

Whole genome sequencing (WGS) using the long-read Oxford Nanopore Technologies (ONT) MinION sequencer provides a cost-effective option for structural variant (SV) detection in clinical applications. Despite the advantage of using long reads, however, accurate SV calling and phasing are still challenging. We introduce Duet, an SV detection tool optimized for SV calling and phasing using ONT data. The tool uses novel features integrated from both SV signatures and single-nucleotide polymorphism (SNP) signatures, which can accurately distinguish SV haplotype from a false signal. Duet can perform accurate SV calling, SV genotyping and SV phasing using low-coverage ONT data. It has great scaling performance when sequencing coverage is higher and is adaptable to various clinical applications.

---

Overview

(A) First, ONT long reads are aligned using Minimap2. (B-C) To obtain the per-read phasing information (green or brown) with its confidence level (luminance of the color), SNPs (indicated in circles) are called using Clair3 and then phased using WhatsHap. Based on the phased SNPs, the haplotypes of the reads are determined by WhatsHap. (D) The SV marks on each read are detected by cuteSV. (E) Three signatures from previous steps are integrated as the signature of the support reads. (F) Duet phases SV and filters out false signals based on the features derived from the signatures in step (E). T1 to T5 are thresholds for each feature.

---

Installation

# install via conda
conda config --add channels bioconda && conda config --add channels conda-forge
conda create -n duet -c bioconda duet -y && conda activate duet

# or you can install from github
conda config --add channels bioconda && conda config --add channels conda-forge
conda create -n duet -c bioconda clair3=0.1.12 sniffles=2.0.7 cutesv=2.0.2 svim=1.4.2 bcftools=1.8 -y && conda activate duet
git clone https://github.com/yekaizhou/duet.git
cd duet && pip install .

duet -h

---

Dependencies

• Clair3 = 0.1.12
• SVIM = 1.4.2
• cuteSV = 2.0.2
• Sniffles = 2.0.7
• bcftools = 1.8

---

Usage

duet <ALN.bam>     # along with indexed .bai file in the same directory \
     <REF.fa>      # along with indexed .fai file in the same directory \
     <OUTPUT_DIR>  # working and output directory
     [Optional Parameters]

Optional Parameter	Description	Default
--threads, -t	number of threads to use	4
--include_all_ctgs, -a	call variants on all contigs, otherwise chr{1..22,X,Y}	false
--sv_min_size, -s	minimum SV size to be reported	50
--min_allele_frequency, -m	minimum allele frequency required to call a candidate SNP	0.25
--min_support_read, -r	minimum number of reads that support a SV to be reported	2
--sv_caller, -b	choose the base SV caller from either cuteSV ("cutesv"), Sniffles (sniffles), or SVIM ("svim")	cutesv
--cluster_max_distance, -c	maximum span-position distance between SV marks in a cluster to call a SV candidates when using SVIM as the base SV caller	0.9

---

Output

Chromosome	Position	ID	Alteration	Length	Haplotype	Phase Set
1	724801	Duet.1	INS	1963	1|1	639244
1	818028	Duet.2	DEL	-84	0|1	639244
1	965596	Duet.3	DEL	-353	1|0	639244
...	...	...	...	...	...	...
16	88087453	Duet.6700	INS	142	0|1	85189772
16	88196792	Duet.6701	DEL	-131	1|1	85189772
...	...	...	...	...	...	...

These SV phasing results will be arranged into phased_sv.vcf. Results from intermediate steps (SNP calling, SNP phasing, SV calling) will also be written to corresponding folders in the same working directory, for the reference and utilization of the users.

---

Quick demo

After Installation, Duet can be used for phased SV detection. Here we demonstrate the utility of Duet using a lite example: chromosome 21 in the HG00733 human sample, aligning to the hg19 reference genome.

mkdir duet_demo && cd duet_demo
wget 'http://www.bio8.cs.hku.hk/duet_demo_data.tar.gz'
tar -zxvf duet_demo_data.tar.gz && rm duet_demo_data.tar.gz
duet duet_demo_data/HG00733_hg19_chr21.bam duet_demo_data/hg19_chr21.fa results

less results/phased_sv.vcf

---

Citation

Zhou, Y., Leung, A.WS., Ahmed, S.S. et al. Duet: SNP-assisted structural variant calling and phasing using Oxford nanopore sequencing. BMC Bioinformatics 23, 465 (2022). https://doi.org/10.1186/s12859-022-05025-x

---

Contact

For any problems or suggestions, please post on Github Issue or send to [email protected].

duet's Issues

bug: np.int() no longer exists.

AttributeError: module 'numpy' has no attribute 'int'.
np.int was a deprecated alias for the builtin int. To avoid this error in existing code, use int by itself. Doing this will not modify any behavior and is safe. When replacing np.int, you may wish to use e.g. np.int64 or np.int32 to specify the precision. If you wish to review your current use, check the release note link for additional information.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

This looks like a pretty simple fix-- just change np.int to int and it should fix it for current versions of numpy. You could also consider np.int64 (considering the number of bases is greater than 2.7 billion, the largest directly representable number in int32).

SNP phasing not running

There seems to be a Syntax error in snp_phasing when running duet on a single chromosome.
The command am running: duet -t 8 -a -b sniffles 01_minimap2_mapping/sample1.bam /data/references/human_references/chr6_hg38.fasta 02_variants/duet_sniffles/sample1

The error:

15507/15507 alignments processed (100%, 7147/s); 1/1 tasks done; parallel 0/8; 0 SVs.
Took 2.17s.

Done.
Wrote 0 called SVs to 02_variants/duet_sniffles/sample1/sv_calling/variants.vcf (single-sample, sorted)
13:37:21 [INFO] ************************* SV CALLING COMPLETED IN 2.324s *************************
13:37:21 [INFO] ************************* SNP PHASING STARTED *************************
mkdir: cannot create directory ‘02_variants/duet_sniffles/sample1/snp_phasing/’: File exists
02_variants/duet_sniffles/sample1/snp_phasing/parallel_wh.sh: 1: Syntax error: "(" unexpected
13:37:21 [INFO] ************************* SNP PHASING COMPLETED IN 0.021s *************************
13:37:21 [INFO] ************************* SV PHASING STARTED *************************
13:37:21 [INFO] create output .vcf file
13:37:21 [INFO] extract SNP signatures
13:37:22 [INFO]   signatures extracted from chr6
13:37:22 [INFO] extract SV signatures
13:37:22 [INFO]   no signature from chr6
13:37:22 [INFO] integrate read weight information
13:37:22 [INFO] calculate read weight statistics
13:37:22 [INFO] predict SV haplotypes in the callset
13:37:22 [INFO] write phased callset into .vcf file
13:37:22 [INFO] ************************* SV PHASING COMPLETED IN 0.79s *************************
13:37:22 [INFO] ************************* DUET FINISHED IN 27.737s *************************
13:37:22 [INFO] OUTPUT .VCF FILE AT 02_variants/duet_sniffles/sample1/phased_sv.vcf

Running bash parallel_wh.sh completes without any issues.

pinned bcftools version libcrypto.so.1.0.0: issue

Am not sure but it seems you have pinned bcftools v1.54 but this raises issues with libcrypto.so.1.0.0

Compatibility with SURVIVOR

Hello,

I'm trying to run SURVIVOR on the Duet output (generated using cuteSV) but it doesn't give me an output unless I run the same file against itself.

Is Duet supposed to be compatible with SURVIVOR? Any suggestions to get it running?

Thanks,
Melissa

SNP calling not working

Hi,

I'm trying to run duet but the SNP calling isn't working.

Thanks,
Melissa

[INFO] Check environment variables
[INFO] --include_all_ctgs not enabled, use chr{1..22,X,Y} and {1..22,X,Y} by default
[INFO] Call variant in contigs: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y
[INFO] Chunk number for each contig: 50 49 40 39 37 35 32 30 29 28 28 27 24 22 21 19 17 16 12 13 10 11 32 12
[INFO] 1/7 Call variants using pileup model
Calling variants ...
Traceback (most recent call last):
File "/Project/tools/conda/duet/bin/scripts/../clair3.py", line 94, in
main()
File "/Project/tools/conda/duet/bin/scripts/../clair3.py", line 88, in main
submodule.main()
File "/Project/tools/conda/duet/bin/clair3/CallVariantsFromCffi.py", line 347, in main
Run(args)
File "/Project/tools/conda/duet/bin/clair3/CallVariantsFromCffi.py", line 61, in Run
call_variants_from_cffi(args=args, output_config=output_config, output_utilities=output_utilities)
File "/Project/tools/conda/duet/bin/clair3/CallVariantsFromCffi.py", line 117, in call_variants_from_cffi
tensor, all_position, all_alt_info = CT(args)
File "/Project/tools/conda/duet/bin/preprocess/CreateTensorPileupFromCffi.py", line 329, in CreateTensorPileup
chunk_result, all_alt_info_list, gvcf_output = pileup_counts_clair3(region,
File "/Project/tools/conda/duet/bin/preprocess/CreateTensorPileupFromCffi.py", line 84, in pileup_counts_clair3
chunk_results, all_alt_info_list, gvcf_output = __enforce_pileup_chunk_contiguity(results)
File "/Project/tools/conda/duet/bin/preprocess/CreateTensorPileupFromCffi.py", line 194, in __enforce_pileup_chunk_contiguity
for counts, positions, alt_info_list, gvcf_output in pileups:
File "/Project/tools/conda/duet/lib/python3.9/concurrent/futures/_base.py", line 600, in result_iterator
yield fs.pop().result()
File "/Project/tools/conda/duet/lib/python3.9/concurrent/futures/_base.py", line 440, in result
return self.__get_result()
File "/Project/tools/conda/duet/lib/python3.9/concurrent/futures/_base.py", line 389, in __get_result
raise self._exception
File "/Project/tools/conda/duet/lib/python3.9/concurrent/futures/thread.py", line 52, in run
result = self.fn(*self.args, **self.kwargs)
File "/Project/tools/conda/duet/bin/preprocess/CreateTensorPileupFromCffi.py", line 63, in _process_region
np_counts, positions, alt_info_string_list, gvcf_output = _plp_data_to_numpy(
File "/Project/tools/conda/duet/bin/preprocess/CreateTensorPileupFromCffi.py", line 141, in _plp_data_to_numpy
size_sizet = np.dtype(np.int).itemsize
File "/Project/tools/conda/duet/lib/python3.9/site-packages/numpy/init.py", line 305, in getattr
raise AttributeError(former_attrs[attr])
AttributeError: module 'numpy' has no attribute 'int'.
np.int was a deprecated alias for the builtin int. To avoid this error in existing code, use int by itself. Doing this will not modify any behavior and is safe. When replacing np.int, you may wish to use e.g. np.int64 or np.int32 to specify the precision. If you wish to review your current use, check the release note link for additional information.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

Combining calls from multiple samples

Hello,
Is there a preferred strategy to combine phased calls from multiple samples or genotype and phase existing SVs?
All the best,
Agnieszka

[Enhancement] Sniffles as extra sv_caller

Hi author team,

I believe it is of interest to add more sv_caller into duet

File sv_calling.py, simply inserting:

elif caller == 'sniffles':
        os.system('sniffles --input ' + aln_path + ' --reference ' + ref_path + ' --vcf ' + sv_calling_home + 'variants.vcf ' + '--output-rnames' + ' --snf' + sv_calling_home + 'variants.snf ' +  ' -t ' + str(thread) + ' --minsvlen ' + str(svlen_thres) + ' --minsupport ' + str(supp_thres))

Obviously, you will need sniffles 2.0.6 install, easily done with conda install -c bioconda sniffles=2.0.6. I've made a fork in my local cluster & can make a PR if this is worthwhile considering.

With Sniffles it is possible to merge the snf file across multiple samples & do joint calling for maximum discovery of SVs in a big cohort (similar with Clair3 GVCF options). But as Duet is developed as an start-to-end pipeline, it is perhaps tricky to deploy in such cases.

---

By the way, cute SV has several recent releases - latest version 2.0.2 so you might want to check that out & update accordingly😃

Best,

Tuan

Feature request: control for clair3 parameters

I have been trying your tool on my data and noticed that there is no control for clair3 functions.
By default, Claire only includes the main chromosomes but, from my encounters, the alternative contigs are also important to some people studying e.g. HLA and KIR.

Here is an example of mapping reads to the human genome reference GRCh38 <"https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_full_analysis_set.fna.gz">

samtools idxstats BTB.bam | awk '$3>100 {print $0}'| sort -k3 -n | sed -E '1 i\chr\tlen\t\#mapped\t\#unmapped' | column -t
chr                   len        #mapped  #unmapped
chr19_KI270888v1_alt  155532     109      0
chr19_GL949748v2_alt  1064304    124      0
chr18                 80373285   160      0
chr19_KI270929v1_alt  186203     169      0
chrX                  156040895  175      0
chr15                 101991189  179      0
chr21                 46709983   185      0
chr19_GL949753v2_alt  796479     193      0
chr16                 90338345   197      0
chr22                 50818468   204      0
chr20                 64444167   207      0
chr19_GL949750v2_alt  1066390    211      0
chr17                 83257441   220      0
chr14                 107043718  244      0
chr13                 114364328  245      0
chr19_GL949749v2_alt  1091841    275      0
chr8                  145138636  285      0
chr12                 133275309  325      0
chr11                 135086622  331      0
chr9                  138394717  339      0
chr5                  181538259  364      0
chr6                  170805979  385      0
chr19_KI270920v1_alt  198005     392      0
chr3                  198295559  444      0
chr2                  242193529  603      0
chr1                  248956422  676      0
chr10                 133797422  757      0
chr19_GL000209v2_alt  177381     853      0
chr19_GL949747v2_alt  729520     948      0
chr19_KI270882v1_alt  248807     958      0
chr19_KI270916v1_alt  184516     967      0
chr19_KI270921v1_alt  282224     1009     0
chr19_KI270923v1_alt  189352     1083     0
chr4                  190214555  1101     0
chr19_KI270890v1_alt  184499     1190     0
chr19_GL949751v2_alt  1002683    1346     0
chr19_KI270886v1_alt  204239     1400     0
chr19_KI270889v1_alt  170698     1507     0
chr7                  159345973  1566     0
chr19_KI270885v1_alt  171027     1732     0
chr19_KI270884v1_alt  157053     1740     0
chr19_KI270918v1_alt  123111     2187     0
chr19_KI270887v1_alt  209512     2528     0
chr19_KI270922v1_alt  187935     2598     0
chr19_KI270938v1_alt  1066800    3104     0
chr19_KI270917v1_alt  190932     3197     0
chr19_GL949752v1_alt  987100     3224     0
chr19_KI270915v1_alt  170665     3668     0
chr19_KI270931v1_alt  170148     3776     0
chr19_KI270891v1_alt  170680     3834     0
chr19_KI270919v1_alt  170701     4387     0
chr19                 58617616   4679     0
chr19_KI270930v1_alt  200773     4792     0
chr19_KI270933v1_alt  170537     5592     0
chr19_KI270914v1_alt  205194     6336     0
chr19_KI270932v1_alt  215732     7106     0
chr19_KI270883v1_alt  170399     9526     0

For my installation, I have edited the snp_calling.py script in my conda env to include --include_all_ctgs

 nano ./miniconda3/envs/duet_sv/lib/python3.6/site-packages/duet/snp_calling.py
  GNU nano 6.2                                                                    ./miniconda3/envs/duet_sv/lib/python3.6/site-packages/duet/snp_calling.py
# coding=utf-8

import logging
import os
import time

def snp_calling(home, ref_path, aln_path, maf, thread):
    lines = '*************************'
    logging.info(lines + ' SNP CALLING STARTED ' + lines)
    starttime = time.time()
    snp_calling_home = home + '/snp_calling/'
    os.system('mkdir ' + snp_calling_home)
    os.system('run_clair3.sh -b ' + aln_path + ' -f ' + ref_path + \
              ' -m "${CONDA_PREFIX}/bin/models/ont" -t ' + str(thread) + ' -p ont -o ' + snp_calling_home + \
              ' --snp_min_af=' + str(maf) + ' --pileup_only --call_snp_only --include_all_ctgs')
    logging.info(lines + ' SNP CALLING COMPLETED IN ' + str(round(time.time() - starttime, 3)) + 's ' + lines)

It will be great if you can extend input options so as to enable some control for the underlying tools such as Clair3.

Feature enhancement: ability to provide sample name for VCF

It's probably a misguided proposal but is there a way that users can provide a sample_name to be used in place of SAMPLE in the vcf files? This becomes handy when merging/concatenating vcf files. I know the files can be preprocessed with sed or similar (cat sample.vcf | sed -e 's/SAMPLE/$filename/'), but if the variant-calling tools accept a sample name options, duet will benefit from that.

Request: A docker image with duet

Hi

While this program looks quite promising, we're encountering several problems trying to install the required software to make duet run.

Would it be possible to provide a docker image with duet and its runtime environment?

-Harald

Increase support for species with. different number of chromosome

Hi team,

I did check out the Duet preprint, very interesting approach

I've checked out the current algo and seems like the phasing steps were done with 24 chromosomes ?

Is it possible to make some flags to address this ?

Many thanks,

Tuan

Could not load dynamic library 'libcudart.so.11.0'; dlerror:

您好，我发现在程序没有停止运行的情况下，遇到以下一些(截取了部分) error，在运行 log 中通常标记为 W，请问这些报错是为什么呢？是否会影响结果，谢谢！

Total processed positions in Chr2h00 (chunk 5/6) : 65085
Total time elapsed: 205.46 s
2022-12-09 16:44:06.651759: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-12-09 16:44:07.597312: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-12-09 16:44:07.597403: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2022-12-09 16:44:09.659883: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-09 16:44:09.660136: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-09 16:44:09.660169: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2022-12-09 16:44:14.087976: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2022-12-09 16:44:14.088681: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)
2022-12-09 16:44:14.088953: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (wxw-Super-Server): /proc/driver/nvidia/version does not exist
2022-12-09 16:44:14.091689: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Calling variants ...
Total processed positions in Chr2h00 (chunk 6/6) : 65146
Total time elapsed: 209.73 s
2022-12-09 16:43:33.529776: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-12-09 16:43:33.897579: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-12-09 16:43:33.897709: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2022-12-09 16:43:38.317267: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-09 16:43:38.318458: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-09 16:43:38.318650: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2022-12-09 16:43:43.359557: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2022-12-09 16:43:43.359668: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)
2022-12-09 16:43:43.359720: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (wxw-Super-Server): /proc/driver/nvidia/version does not exist
2022-12-09 16:43:43.360174: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Calling variants ...
Total processed positions in Chr2h18 (chunk 2/4) : 63390
Total time elapsed: 241.85 s
2022-12-09 16:43:48.758377: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-12-09 16:43:50.615858: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-12-09 16:43:50.616531: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2022-12-09 16:43:52.739768: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-09 16:43:52.740052: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-09 16:43:52.740090: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2022-12-09 16:43:57.225463: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2022-12-09 16:43:57.226372: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)
2022-12-09 16:43:57.226688: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (wxw-Super-Server): /proc/driver/nvidia/version does not exist
2022-12-09 16:43:57.230685: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Calling variants ...
Total processed positions in Chr2h18 (chunk 4/4) : 69829
Total time elapsed: 238.72 s

real	24m31.352s
user	668m49.375s
sys	37m54.743s

error when run the duet demo data

Hi Dr. Zhou,
Thanks for develop duet, I've successfully installed duet according to your instruction:

git clone https://github.com/yekaizhou/duet.git
cd duet && pip3 install .

But when I run on the demo data, it comes up with an error:

$duet duet_demo_data/HG00733_hg19_chr21.bam duet_demo_data/hg19_chr21.fa results
10:10:19 [INFO] ************************* DUET STARTED *************************
10:10:19 [INFO] ************************* SNP CALLING STARTED *************************
sh: line 1: run_clair3.sh: command not found
10:10:19 [INFO] ************************* SNP CALLING COMPLETED IN 0.007s *************************
10:10:19 [INFO] ************************* SV CALLING STARTED *************************
2022-12-09 10:10:19,723 [INFO] Running /usr/local/bin/cuteSV --genotype --report_readid duet_demo_data/HG00733_hg19_chr21.bam duet_demo_data/hg19_chr21.fa results/sv_calling/variants.vcf results/sv_calling/ -t 4 -s 2 -l 50
2022-12-09 10:10:19,724 [INFO] The total number of chromsomes: 86
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2022-12-09 10:10:19,799 [INFO] Skip 20:60000000-63025520.
2022-12-09 10:10:20,179 [INFO] Finished 21:0-10000000.
2022-12-09 10:10:24,869 [INFO] Finished 21:10000000-20000000.
2022-12-09 10:10:24,879 [INFO] Skip 22:0-10000000.
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2022-12-09 10:10:24,881 [INFO] Skip 22:30000000-40000000.
2022-12-09 10:10:24,882 [INFO] Skip 22:40000000-50000000.
2022-12-09 10:10:24,882 [INFO] Skip 22:50000000-51304566.
2022-12-09 10:10:24,883 [INFO] Skip X:0-10000000.
2022-12-09 10:10:24,883 [INFO] Skip X:10000000-20000000.
2022-12-09 10:10:24,884 [INFO] Skip X:20000000-30000000.
2022-12-09 10:10:24,885 [INFO] Skip X:30000000-40000000.
2022-12-09 10:10:24,885 [INFO] Skip X:40000000-50000000.
2022-12-09 10:10:24,886 [INFO] Skip X:50000000-60000000.
2022-12-09 10:10:24,886 [INFO] Skip X:60000000-70000000.
2022-12-09 10:10:24,887 [INFO] Skip X:70000000-80000000.
2022-12-09 10:10:24,888 [INFO] Skip X:80000000-90000000.
2022-12-09 10:10:24,888 [INFO] Skip X:90000000-100000000.
2022-12-09 10:10:24,889 [INFO] Skip X:100000000-110000000.
2022-12-09 10:10:24,889 [INFO] Skip X:110000000-120000000.
2022-12-09 10:10:24,890 [INFO] Skip X:120000000-130000000.
2022-12-09 10:10:24,891 [INFO] Skip X:130000000-140000000.
2022-12-09 10:10:24,891 [INFO] Skip X:140000000-150000000.
2022-12-09 10:10:24,892 [INFO] Skip X:150000000-155270560.
2022-12-09 10:10:24,892 [INFO] Skip Y:0-10000000.
2022-12-09 10:10:24,893 [INFO] Skip Y:10000000-20000000.
2022-12-09 10:10:24,894 [INFO] Skip Y:20000000-30000000.
2022-12-09 10:10:24,894 [INFO] Skip Y:30000000-40000000.
2022-12-09 10:10:24,895 [INFO] Skip Y:40000000-50000000.
2022-12-09 10:10:24,896 [INFO] Skip Y:50000000-59373566.
2022-12-09 10:10:24,896 [INFO] Skip MT:0-16569.
2022-12-09 10:10:24,897 [INFO] Skip GL000207.1:0-4262.
2022-12-09 10:10:24,897 [INFO] Skip GL000226.1:0-15008.
2022-12-09 10:10:24,898 [INFO] Skip GL000229.1:0-19913.
2022-12-09 10:10:24,899 [INFO] Skip GL000231.1:0-27386.
2022-12-09 10:10:24,899 [INFO] Skip GL000210.1:0-27682.
2022-12-09 10:10:24,900 [INFO] Skip GL000239.1:0-33824.
2022-12-09 10:10:24,900 [INFO] Skip GL000235.1:0-34474.
2022-12-09 10:10:24,901 [INFO] Skip GL000201.1:0-36148.
2022-12-09 10:10:24,902 [INFO] Skip GL000247.1:0-36422.
2022-12-09 10:10:24,902 [INFO] Skip GL000245.1:0-36651.
2022-12-09 10:10:24,903 [INFO] Skip GL000197.1:0-37175.
2022-12-09 10:10:24,903 [INFO] Skip GL000203.1:0-37498.
2022-12-09 10:10:24,904 [INFO] Skip GL000246.1:0-38154.
2022-12-09 10:10:24,905 [INFO] Skip GL000249.1:0-38502.
2022-12-09 10:10:24,905 [INFO] Skip GL000196.1:0-38914.
2022-12-09 10:10:24,906 [INFO] Skip GL000248.1:0-39786.
2022-12-09 10:10:24,907 [INFO] Skip GL000244.1:0-39929.
2022-12-09 10:10:24,907 [INFO] Skip GL000238.1:0-39939.
2022-12-09 10:10:24,908 [INFO] Skip GL000202.1:0-40103.
2022-12-09 10:10:24,908 [INFO] Skip GL000234.1:0-40531.
2022-12-09 10:10:24,909 [INFO] Skip GL000232.1:0-40652.
2022-12-09 10:10:24,910 [INFO] Skip GL000206.1:0-41001.
2022-12-09 10:10:24,910 [INFO] Skip GL000240.1:0-41933.
2022-12-09 10:10:24,911 [INFO] Skip GL000236.1:0-41934.
2022-12-09 10:10:24,911 [INFO] Skip GL000241.1:0-42152.
2022-12-09 10:10:24,912 [INFO] Skip GL000243.1:0-43341.
2022-12-09 10:10:24,913 [INFO] Skip GL000242.1:0-43523.
2022-12-09 10:10:24,913 [INFO] Skip GL000230.1:0-43691.
2022-12-09 10:10:24,914 [INFO] Skip GL000237.1:0-45867.
2022-12-09 10:10:24,914 [INFO] Skip GL000233.1:0-45941.
2022-12-09 10:10:24,915 [INFO] Skip GL000204.1:0-81310.
2022-12-09 10:10:24,916 [INFO] Skip GL000198.1:0-90085.
2022-12-09 10:10:24,916 [INFO] Skip GL000208.1:0-92689.
2022-12-09 10:10:24,917 [INFO] Skip GL000191.1:0-106433.
2022-12-09 10:10:24,917 [INFO] Skip GL000227.1:0-128374.
2022-12-09 10:10:24,918 [INFO] Skip GL000228.1:0-129120.
2022-12-09 10:10:24,919 [INFO] Skip GL000214.1:0-137718.
2022-12-09 10:10:24,919 [INFO] Skip GL000221.1:0-155397.
2022-12-09 10:10:24,920 [INFO] Skip GL000209.1:0-159169.
2022-12-09 10:10:24,921 [INFO] Skip GL000218.1:0-161147.
2022-12-09 10:10:24,921 [INFO] Skip GL000220.1:0-161802.
2022-12-09 10:10:24,922 [INFO] Skip GL000213.1:0-164239.
2022-12-09 10:10:24,922 [INFO] Skip GL000211.1:0-166566.
2022-12-09 10:10:24,923 [INFO] Skip GL000199.1:0-169874.
2022-12-09 10:10:24,924 [INFO] Skip GL000217.1:0-172149.
2022-12-09 10:10:24,924 [INFO] Skip GL000216.1:0-172294.
2022-12-09 10:10:24,925 [INFO] Skip GL000215.1:0-172545.
2022-12-09 10:10:24,925 [INFO] Skip GL000205.1:0-174588.
2022-12-09 10:10:24,926 [INFO] Skip GL000219.1:0-179198.
2022-12-09 10:10:24,927 [INFO] Skip GL000224.1:0-179693.
2022-12-09 10:10:24,927 [INFO] Skip GL000223.1:0-180455.
2022-12-09 10:10:24,928 [INFO] Skip GL000195.1:0-182896.
2022-12-09 10:10:24,928 [INFO] Skip GL000212.1:0-186858.
2022-12-09 10:10:24,929 [INFO] Skip GL000222.1:0-186861.
2022-12-09 10:10:24,930 [INFO] Skip GL000200.1:0-187035.
2022-12-09 10:10:24,930 [INFO] Skip GL000193.1:0-189789.
2022-12-09 10:10:24,931 [INFO] Skip GL000194.1:0-191469.
2022-12-09 10:10:24,932 [INFO] Skip GL000225.1:0-211173.
2022-12-09 10:10:24,932 [INFO] Skip GL000192.1:0-547496.
2022-12-09 10:10:24,933 [INFO] Skip NC_007605:0-171823.
2022-12-09 10:10:24,933 [INFO] Skip hs37d5:0-10000000.
2022-12-09 10:10:24,934 [INFO] Skip hs37d5:10000000-20000000.
2022-12-09 10:10:24,935 [INFO] Skip hs37d5:20000000-30000000.
2022-12-09 10:10:24,935 [INFO] Skip hs37d5:30000000-35477943.
2022-12-09 10:10:25,647 [INFO] Finished 21:20000000-30000000.
2022-12-09 10:10:25,934 [INFO] Finished 21:40000000-48129895.
2022-12-09 10:10:26,028 [INFO] Finished 21:30000000-40000000.
2022-12-09 10:10:26,042 [INFO] Rebuilding signatures of structural variants.
2022-12-09 10:10:26,233 [INFO] Clustering structural variants.
2022-12-09 10:10:26,257 [INFO] Finished hs37d5:INV.
2022-12-09 10:10:26,342 [INFO] Finished 21:INV.
2022-12-09 10:10:26,351 [INFO] Finished X:DUP.
2022-12-09 10:10:26,359 [INFO] Finished hs37d5:DUP.
2022-12-09 10:10:26,360 [INFO] Finished 1-14:TRA/BND.
2022-12-09 10:10:26,360 [INFO] Finished 1-21:TRA/BND.
2022-12-09 10:10:26,361 [INFO] Finished 1-22:TRA/BND.
2022-12-09 10:10:26,361 [INFO] Finished 1-Y:TRA/BND.
2022-12-09 10:10:26,363 [INFO] Finished 1-hs37d5:TRA/BND.
2022-12-09 10:10:26,363 [INFO] Finished 10-21:TRA/BND.
2022-12-09 10:10:26,364 [INFO] Finished 10-8:TRA/BND.
2022-12-09 10:10:26,364 [INFO] Finished 10-Y:TRA/BND.
2022-12-09 10:10:26,365 [INFO] Finished 10-hs37d5:TRA/BND.
2022-12-09 10:10:26,369 [INFO] Finished 21:DUP.
2022-12-09 10:10:26,370 [INFO] Finished 12-2:TRA/BND.
2022-12-09 10:10:26,370 [INFO] Finished 12-20:TRA/BND.
2022-12-09 10:10:26,395 [INFO] Finished 11-21:TRA/BND.
2022-12-09 10:10:26,395 [INFO] Finished 12-4:TRA/BND.
2022-12-09 10:10:26,396 [INFO] Finished 12-6:TRA/BND.
2022-12-09 10:10:26,396 [INFO] Finished 12-7:TRA/BND.
2022-12-09 10:10:26,397 [INFO] Finished 12-hs37d5:TRA/BND.
2022-12-09 10:10:26,398 [INFO] Finished 13-21:TRA/BND.
2022-12-09 10:10:26,398 [INFO] Finished 13-6:TRA/BND.
2022-12-09 10:10:26,399 [INFO] Finished 13-hs37d5:TRA/BND.
2022-12-09 10:10:26,400 [INFO] Finished 14-15:TRA/BND.
2022-12-09 10:10:26,400 [INFO] Finished 14-21:TRA/BND.
2022-12-09 10:10:26,401 [INFO] Finished 15-20:TRA/BND.
2022-12-09 10:10:26,402 [INFO] Finished 12-21:TRA/BND.
2022-12-09 10:10:26,403 [INFO] Finished 15-22:TRA/BND.
2022-12-09 10:10:26,403 [INFO] Finished 15-3:TRA/BND.
2022-12-09 10:10:26,404 [INFO] Finished 16-21:TRA/BND.
2022-12-09 10:10:26,405 [INFO] Finished 17-21:TRA/BND.
2022-12-09 10:10:26,405 [INFO] Finished 17-3:TRA/BND.
2022-12-09 10:10:26,406 [INFO] Finished 17-hs37d5:TRA/BND.
2022-12-09 10:10:26,406 [INFO] Finished 15-21:TRA/BND.
2022-12-09 10:10:26,406 [INFO] Finished 18-21:TRA/BND.
2022-12-09 10:10:26,406 [INFO] Finished 19-21:TRA/BND.
2022-12-09 10:10:26,407 [INFO] Finished 2-9:TRA/BND.
2022-12-09 10:10:26,407 [INFO] Finished 2-GL000212.1:TRA/BND.
2022-12-09 10:10:26,408 [INFO] Finished 2-hs37d5:TRA/BND.
2022-12-09 10:10:26,409 [INFO] Finished 20-21:TRA/BND.
2022-12-09 10:10:26,409 [INFO] Finished 20-hs37d5:TRA/BND.
2022-12-09 10:10:26,442 [INFO] Finished 2-21:TRA/BND.
2022-12-09 10:10:26,443 [INFO] Finished 21-3:TRA/BND.
2022-12-09 10:10:26,443 [INFO] Finished 21-4:TRA/BND.
2022-12-09 10:10:26,444 [INFO] Finished 21-5:TRA/BND.
2022-12-09 10:10:26,444 [INFO] Finished 21-6:TRA/BND.
2022-12-09 10:10:26,528 [INFO] Finished 21-22:TRA/BND.
2022-12-09 10:10:26,528 [INFO] Finished 21-8:TRA/BND.
2022-12-09 10:10:26,558 [INFO] Finished 21-9:TRA/BND.
2022-12-09 10:10:26,558 [INFO] Finished 21-GL000199.1:TRA/BND.
2022-12-09 10:10:26,559 [INFO] Finished 21-GL000210.1:TRA/BND.
2022-12-09 10:10:26,588 [INFO] Finished 21-GL000212.1:TRA/BND.
2022-12-09 10:10:26,615 [INFO] Finished 21-7:TRA/BND.
2022-12-09 10:10:26,622 [INFO] Finished 21-GL000220.1:TRA/BND.
2022-12-09 10:10:26,622 [INFO] Finished 21-GL000226.1:TRA/BND.
2022-12-09 10:10:26,623 [INFO] Finished 21-GL000237.1:TRA/BND.
2022-12-09 10:10:26,623 [INFO] Finished 21-X:TRA/BND.
2022-12-09 10:10:26,636 [INFO] Finished 21-GL000216.1:TRA/BND.
2022-12-09 10:10:26,674 [INFO] Finished 21-Y:TRA/BND.
2022-12-09 10:10:26,675 [INFO] Finished 22-5:TRA/BND.
2022-12-09 10:10:26,676 [INFO] Finished 22-GL000210.1:TRA/BND.
2022-12-09 10:10:26,677 [INFO] Finished 22-hs37d5:TRA/BND.
2022-12-09 10:10:26,678 [INFO] Finished 3-6:TRA/BND.
2022-12-09 10:10:26,678 [INFO] Finished 3-X:TRA/BND.
2022-12-09 10:10:26,679 [INFO] Finished 4-7:TRA/BND.
2022-12-09 10:10:26,679 [INFO] Finished 4-X:TRA/BND.
2022-12-09 10:10:26,680 [INFO] Finished 5-GL000210.1:TRA/BND.
2022-12-09 10:10:26,680 [INFO] Finished 6-hs37d5:TRA/BND.
2022-12-09 10:10:26,681 [INFO] Finished 7-GL000237.1:TRA/BND.
2022-12-09 10:10:26,681 [INFO] Finished 8-hs37d5:TRA/BND.
2022-12-09 10:10:26,682 [INFO] Finished GL000212.1-hs37d5:TRA/BND.
2022-12-09 10:10:26,683 [INFO] Finished GL000220.1-GL000243.1:TRA/BND.
2022-12-09 10:10:26,683 [INFO] Finished GL000220.1-hs37d5:TRA/BND.
2022-12-09 10:10:26,683 [INFO] Finished GL000237.1-hs37d5:TRA/BND.
2022-12-09 10:10:26,684 [INFO] Finished GL000243.1-hs37d5:TRA/BND.
2022-12-09 10:10:26,684 [INFO] Finished X-hs37d5:TRA/BND.
2022-12-09 10:10:26,685 [INFO] Finished Y-hs37d5:TRA/BND.
2022-12-09 10:10:26,751 [INFO] Finished 21:INS.
2022-12-09 10:10:26,894 [INFO] Finished 21:DEL.
2022-12-09 10:10:27,315 [INFO] Finished 21-hs37d5:TRA/BND.
2022-12-09 10:10:27,322 [INFO] Writing to your output file.
2022-12-09 10:10:27,326 [INFO] Loading reference genome...
Traceback (most recent call last):
  File "/usr/local/bin/cuteSV", line 4, in <module>
    __import__('pkg_resources').run_script('cuteSV==2.0.2', 'cuteSV')
  File "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py", line 651, in run_script
    self.require(requires)[0].run_script(script_name, ns)
  File "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py", line 1448, in run_script
    exec(code, namespace, namespace)
  File "/usr/local/lib/python3.10/dist-packages/cuteSV-2.0.2-py3.10.egg/EGG-INFO/scripts/cuteSV", line 888, in <module>
    run(sys.argv[1:])
  File "/usr/local/lib/python3.10/dist-packages/cuteSV-2.0.2-py3.10.egg/EGG-INFO/scripts/cuteSV", line 884, in run
    main_ctrl(args, argv)
  File "/usr/local/lib/python3.10/dist-packages/cuteSV-2.0.2-py3.10.egg/EGG-INFO/scripts/cuteSV", line 862, in main_ctrl
    generate_output(args, semi_result, contigINFO, argv, ref_g)
  File "/usr/local/lib/python3.10/dist-packages/cuteSV-2.0.2-py3.10.egg/cuteSV/cuteSV_genotype.py", line 287, in generate_output
    REF = str(ref_g[i[0]].seq[int(i[2])]),
KeyError: 'hs37d5'
10:10:27 [INFO] ************************* SV CALLING COMPLETED IN 8.516s *************************
10:10:27 [INFO] ************************* SNP PHASING STARTED *************************
[E::idx_find_and_load] Could not retrieve index file for 'results/snp_calling/pileup.vcf.gz'
Could not load .tbi index of results/snp_calling/pileup.vcf.gz: No such file or directory
Traceback (most recent call last):
  File "/home/disk2/vivi/.local/bin/duet", line 31, in <module>
    main(sys.argv[1:])
  File "/home/disk2/vivi/.local/bin/duet", line 25, in main
    snp_phasing(args.OUTPUT, args.REFERENCE, args.BAM, args.thread)
  File "/home/disk2/vivi/.local/lib/python3.10/site-packages/duet/snp_phasing.py", line 17, in snp_phasing
    ctgs = subprocess.check_output(shlex.split('tabix --list-chroms ' + pileup_vcf_path)).decode('ascii').split('\n')[:-1]
  File "/usr/lib/python3.10/subprocess.py", line 420, in check_output
    return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,
  File "/usr/lib/python3.10/subprocess.py", line 524, in run
    raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['tabix', '--list-chroms', 'results/snp_calling/pileup.vcf.gz']' returned non-zero exit status 1.