CSV-Filter: A Comprehensive Structural Variation Filtering Tool for Single Molecule Real-Time Sequencing

Introduction

Structure Variations (SVs) play an important role in genetic research and precision medicine. However, existing SV detection methods usually contain a substantial number of false positive calls. It is necessary to develop effective filtering approaches. We developed a novel deep learning-based SV filtering tool, CSV-Filter, for both second and third generation sequencing data. In CSV-Filter, we proposed a novel multi-level grayscale image encoding method based on CIGAR strings of the alignment results and employed image augmentation techniques to improve the extraction of SV features. We also utilized self-supervised learning networks for transfer as classification models, and employed mixed-precision operations to accelerate the training process. The experimental results show that the integration of CSV-Filter with popular second-generation and third-generation SV detection tools could considerably reduce false positive SVs, while maintaining true positive SVs almost unchanged. Compared with DeepSVFilter, a SV filtering tool for second-generation sequencing, CSV-Filter can recognize more false positive SVs and supports third-generation sequencing data as an additional feature.

Requirements

CSV-Filter is tested to work under:

• Ubuntu 18.04
• gcc 7.5.0
• CUDA 11.3
• cudnn 8200
• nvidia-smi 470.182.03
• Anoconda 4.10.3
• Python 3.6
• pysam 0.15.4
• pytorch 1.10.2
• pytorch-lightning 1.5.10
• hyperopt 0.2.7
• matplotlib 3.3.4
• numpy 1.19.2
• pudb 2022.1.3
• redis 4.3.6
• samtools 1.5
• scikit-learn 0.24.2
• torchvision 1.10.2
• tensroboard 2.11.2

Quick start

Datasets

Reference

• GRCh37: https://ftp.ensembl.org/pub/release-75/fasta/homo_sapiens/dna/Homo_sapiens.GRCh37.75.dna.primary_assembly.fa.gz
• hs37d5: https://ftp-trace.ncbi.nih.gov/1000genomes/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz

HG002

• Tier1 benchmark SV callset and high-confidence HG002 region: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/NIST_SVs_Integration_v0.6/
• PacBio 70x (CLR): https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/HG002_NA24385_son/PacBio_MtSinai_NIST/
• PacBio CCS 15kb_20kb chemistry2 (HiFi): https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/HG002_NA24385_son/PacBio_CCS_15kb_20kb_chemistry2/reads/
• Oxford Nanopore ultralong (guppy-V3.2.4_2020-01-22): ftp://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/HG002_NA24385_son/Ultralong_OxfordNanopore/guppy-V3.2.4_2020-01-22/HG002_ONT-UL_GIAB_20200122.fastq.gz
• NHGRI_Illumina300X_AJtrio_novoalign_bams: https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_HiSeq_HG002_Homogeneity-10953946/NHGRI_Illumina300X_AJtrio_novoalign_bams/HG002.hs37d5.60x.1.bam

NA12878

• NA12878: Index of /giab/ftp/data/NA12878/NA12878_PacBio_MtSinai (nih.gov)

data used:

• NA12878.sorted.vcf.gz
• NA12878.sorted.vcf.gz.tbi
• sorted_final_merged.bam
• sorted_final_merged.bam.bai
• corrected_reads_gt4kb.fasta

Model that have been trained

Download trained models from Releases · xzyschumacher/CSV-Filter (github.com)

Environment by using anaconda and pip

Install Ubuntu support environment

apt: $ sudo apt-get update

gcc: $ sudo apt-get install gcc

g++: $ sudo apt-get install g++

vim: $ sudo apt-get install vim

Install drivers

show GPU version and recommended drivers:

$ ubuntu-drivers devices

install recommended drivers:

$ sudo apt install nvidia-470

$ reboot

test:

$ nvidia-smi

Install CUDA

CUDA Toolkit Archive：https://developer.nvidia.com/cuda-toolkit-archive

download CUDA Toolkit 11.3.1:

$ wget https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.19.01_linux.run

run: $ sudo sh cuda_11.3.1_465.19.01_linux.run

$ accept

$ Continue

$ Install

Modify environment variables:

export PATH=/usr/local/cuda-11.3/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-11.3/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

refresh environment variables:

$ source ~/.bashrc

test:

$ nvcc -V

run test program:

$ cd ~/NVIDIA_CUDA-11.3_Samples/1_Utilities/bandwidthTest
$ make
$ ./bandwidthTest

Install Anaconda

download Anaconda:

$ wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2021.11-Linux-x86_64.sh

run:$ bash Anaconda3-2021.11-Linux-x86_64.sh

$ yes

default direction: /home/username/anaconda3

Modify environment variables:

export PATH=”/home/username/anaconda3/bin:$PATH”

refresh environment variables:

$ source ~/.bashrc

test:

$ conda --version

Create CSV-Filter environment

create a new environment:

$ conda create -n MSVF python=3.6 -y

activate CSV-Filter environment:

$ conda activate csv-filter

install pytorch/cudatoolkit/torchvision/torchaudio/pytorch:

$ conda install pytorch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2 cudatoolkit==11.3.1 -c pytorch -y

install pytorch-lightning:

$ conda install pytorch-lightning=1.5.10 -c conda-forge -y

install ray:

$ pip install ray[tune]==1.6.0

install redis:

$ conda install redis -y

install scikit-learn:

$ conda install scikit-learn -y

install matplotlib:

$ conda install matplotlib -y

install pudb:

$ pip install pudb

install samtools:

$ conda install samtools -c bioconda

install hyperopt:

$ pip install hyperopt

install pysam:

$ pip install pysam==0.15.4

install parallel:

$ sudo apt install parallel

Usage

Simple train

$ python simple_train.py selected_model
($ python simple_train.py resnet50)

Train

$ cd data

get BAM file:

$ wget https://ftp.ncbi.nlm.nih.gov/giab/ftp/data/NA12878/NA12878_PacBio_MtSinai/sorted_final_merged.bam

parallel index BAM file：

$ parallel samtools index ::: *.bam

get VCF file:

$ wget https://ftp.ncbi.nlm.nih.gov/giab/ftp/data/NA12878/NA12878_PacBio_MtSinai/NA12878.sorted.vcf.gz

uncompress VCF gz file:

$ tar -xzvf NA12878.sorted.vcf.gz

$ cd ../src

vcf data preprocess:

$ python vcf_data_process.py

BAM data preprocess:

$ python bam2depth.py

parallel generate images:

$ python parallel_process_file.py --thread_num thread_num  
($ python parallel_process_file.py --thread_num 16)

check generated images:

$ python process_file_check.py

rearrange generated images:

$ python data_spread.py

train:

$ python train.py

Predict & Filter

predict:

$ python predict.py selected_model
(e.g. $ python predict.py resnet50)

filter:

$ python filter.py selected_model
(e.g. $ python filter.py resnet50)

Switch model to train

In train.py file, modify the data dirction and name of the model.

data_dir = "../data/"
bs = 128
my_label = "resnet50"

In net.py file, modify models need to be trianed.

# load local models
self.resnet_model = torch.load("../models/init_resnet50.pt")
self.resnet_model.eval()

# load models from websites
self.resnet_model = torchvision.models.mobilenet_v2(pretrained=True)
self.resnet_model = torchvision.models.resnet34(pretrained=True)
self.resnet_model = torchvision.models.resnet50(pretrained=True)

# load VICReg: Variance-Invariance-Covariance Regularization For Self-Supervised Learning
self.resnet_model = torch.hub.load('facebookresearch/vicreg:main', 'resnet50')
self.resnet_model = torch.hub.load('facebookresearch/vicreg:main', 'resnet50x2')
self.resnet_model = torch.hub.load('facebookresearch/vicreg:main', 'resnet200x2')

PS: sometimes the output dimension is different, so we need to modify the softmax void:

self.softmax = nn.Sequential(
    # nn.Linear(full_dim[-1], 3),
    # nn.Linear(4096, 3),
    # nn.Linear(2048, 3),
    nn.Linear(1000, 3),
    nn.Softmax(1)
)