BVSim: A Benchmarking Variation Simulator Mimicking Human Variation Spectrum
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BVSim: A Benchmarking Variation Simulator Mimicking Human Variation Spectrum

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Table of Contents

Getting Started

To get started with BVSim, follow these steps to install and run the simulator:

# Create an envrionment called BVSim and install the dependencies
conda create -n BVSim python=3.11 numpy pandas biopython scipy seaborn psutil
conda activate BVSim
# Run the following to install pysam or use the latest guide
conda config --add channels defaults
conda config --add channels conda-forge
conda config --add channels bioconda
conda install pysam
# Installzation
## Clone the repository in your home path
cd your_home_path
git clone https://github.com/YongyiLuo98/BVSim.git
## Navigate to the ~/BVSim/main directory and install the package
pip install your_home_path/BVSim/main/.

# Verify the installation in your home path
cd your_home_path
python -m BVSim --help
python -m BVSim -h

## Run a toy example with a specified reference in the cloned folder
conda activate BVSim
python -m BVSim -ref 'your_home_path/BVSim/empirical/sub_hg19_chr1.fasta' -seed 0 -rep 0 -write -snp 2000
## If you prefer using the default reference, simply execute
cd your_home_path
python -m BVSim


# Generate variations with specific parameters
cd your_home_path
python -m BVSim -seed 1 -rep 1 -snp 2000

# To write out the relative positions, use the following command
python your_home_path/BVSim/main/write_SV.py your_home_path/BVSim/save/ BV_1_con0_chr1_SVtable.csv BV_1_con0_chr1_tem_ins_dic.npy

# Create a block intervals BED file
cd your_home_path
echo -e "0\t1000\n3000\t4000" > block_intervals.bed

# Run the simulator with block regions
cd your_home_path
python -m BVSim -seed 1 -rep 1 -write -snp 2000 -block_region_bed_url block_intervals.bed

Installation

Create an envrionment called BVSim and install the dependencies

To start with, you need to install the dependent packages in an environment, for example called BVSim.

# Create an envrionment called BVSim and install the dependencies
conda create -n BVSim python=3.11 numpy pandas biopython scipy seaborn psutil
conda activate BVSim
# Run the following to install pysam or use the latest guide
conda config --add channels defaults
conda config --add channels conda-forge
conda config --add channels bioconda
conda install pysam

Clone the Repository

Next, you need to clone the BVSim repository to your local machine. Execute the following command in your home directory:

cd your_home_path
git clone https://github.com/YongyiLuo98/BVSim.git

Navigate to the Main Directory and Install the Package

Next, navigate to the .../BVSim/main/ directory to install the package:

pip install your_home_path/BVSim/main/.

Verify the Installation

After installation, you can verify it from your home directory. Execute the following commands:

cd
python -m BVSim --help
python -m BVSim -h

Note: You can only call BVSim in the cloned repository directory, while the installation must take place in the BVSim/main/ directory.

Toy Example (Uniform mode):

conda activate BVSim
python -m BVSim -ref 'your_home_path/BVSim/empirical/sub_hg19_chr1.fasta' -seed 0 -rep 0 -write -snp 2000

or you can use the default reference to test the installation by type the following in your home path. If you do not give a saving path, the outputs will go to "your_home_path\BVSim\save".

cd your_home_path
python -m BVSim

Functions and Parameters

Five modes: uniform, uniform parallel, csv, wave, wave_region

Shared Parameters

The BVSim package provides several functions (modes) and parameters for simulating genetic variations. Here is a table that introduces all the functions and different parameters:

Parameter Type Description Default
-ref str Input reference file '.../BVSim/empirical/sub_hg19_chr1.fasta'
-save str Saving path .../BVSim/save/
-seed int Seed for random number generator 999
-times int Number of times 10
-rep int Replication ID 5
-sv_trans int Number of trans SV 5
-sv_inver int Number of inversion SV 5
-sv_dup int Number of tandem duplication 5
-sv_del int Number of SV deletion 5
-sv_ins int Number of SV insertion 5
-snp float SNV number or probability 5
-snv_del float SNV deletion number or probability 5
-snv_ins float SNV insertion number or probability 5
-notblockN bool Do not Block N positions False
-write bool Write full results False
-delmin int Minimum deletion length 50
-delmax int Maximum deletion length 60
-insmin int Minimum insertion length 50
-insmax int Maximum insertion length 450
-dupmin int Minimum duplication length 50
-dupmax int Maximum duplication length 450
-invmin int Minimum inversion length 50
-invmax int Maximum inversion length 450
-dupmin int Minimum duplication length 50
-dupmax int Maximum duplication length 450
-transmin int Minimum translocation length 50
-transmax int Maximum translocation length 450
-block_region_bed_url str local path of the block region BED file None

Output Naming Conventions

When you run the simulation tool, the output files are named based on the sequence name you input or the parameter rep you set (repetition number). Below is a summary of the output files you can expect:

  1. FASTA File:
    The output FASTA file will be named as follows:
BV__seq_.fasta

This file contains the simulated sequence.

  1. VCF File:
    The VCF file will be named:
BV__seq_.vcf

This file stores the simulated variations.

  1. SV Table:
    The SV table will have different naming conventions depending on whether you choose to include relative positions:
  • If you include relative positions (by using the -write flag): 
    BV__seq__SVtable_full.csv
  • If you do not include relative positions: 
    BV__seq__SVtable.csv
  1. Numpy File:
    The numpy file that records all inserted segments we need to update the relative positions will be named:
BV__seq__tem_ins_dic.npy

Write the Relative Positions of Simulated Variations

If you choose not to generate the relative positions during the initial simulation run (i.e., you do not include the -write flag), the columns for relative positions in the SV table will be empty. However, you can still update these relative positions later using the saved intermediate files.

Steps to Write Relative Positions After Simulation
  1. Run the Initial Simulation:
    For example, you can execute:
cd your_home_path
python -m BVSim -seed 1 -rep 1 -snp 2000

In this case you generated default number of elementary SVs and micro indels, as well as 20000 SNPs saved in the default directory with BV_1_seq_chr1_SVtable.csv, BV_1_seq_chr1_tem_ins_dic.npy.

  1. Update Relative Positions: You can then run the following command to generate a table with the relative positions:
python your_home_path/BVSim/main/write_SV.py your_home_path/BVSim/save/ BV_1_seq_chr1_SVtable.csv BV_1_seq_chr1_tem_ins_dic.npy

This command will create a file called called full_BV_1_seq_chr1_SVtable.csv in the same directory, which will contain the relative positions for all variations with respect to the consensus sequence. By following this naming convention and steps, you can easily manage and update your output files as needed.

User-defined Block Regions with No Variations

The input of the '-block_region_bed_url' should be two columns of positions(start;end) without headers seperated by '\t'. To create a bed file, you can refer to the following example. In this case, positions from 0 to 999, from 3000 to 3999 cannot have any variation, so called blocked.

Toy Example:

cd your_home_path
echo -e "0\t1000\n3000\t4000" > block_intervals.bed
# uniform.py
cd your_home_path
python -m BVSim -seed 1 -rep 1 -write -snp 2000 -block_region_bed_url block_intervals.bed

Uniform Mode

If you do not call any of the following parameters (-csv, -cores, -len_bins, -wave), the simulation will be generated one by one uniformly.

Toy Example (Uniform mode):

conda activate BVSim
python -m BVSim -ref 'hg19_chr1.fasta' -seed 0 -rep 0 -write -snp 2000

Complex SV Mode

Add -csv to your command, 18 types of Complex Structure Variations can be generated.

  • ID1: Tandem Inverted Duplication (TanInvDup)
  • ID2: Dispersed Inverted Duplication (DisInvDup)
  • ID3: Dispersed Duplication (DisDup)
  • ID4: Inversion with 5’ or 3’ Flanking Deletion (DEL+INV/INV+DEL)
  • ID5: 5’ Deletion and Dispersed Inverted Duplication (DEL+DisInvDup)
  • ID6: 5’ Deletion and Dispersed Duplication (DEL+DisDup)
  • ID7: Tandem Duplication and 3’ Deletion (TanDup+DEL)
  • ID8: Tandem Inverted Duplication and 3’ Deletion (TanInvDup+DEL)
  • ID9: Tandem Duplication, Deletion and Inversion (TanDup+DEL+INV)
  • ID10: Tandem Inverted Duplication, Deletion and Inversion (TanInvDup+DEL+INV)
  • ID11: Paired-Deletion Inversion (DEL+INV+DEL)
  • ID12: Inversion with 5’ Flanking Duplication (DUP+INV)
  • ID13: Inversion with 3’ Flanking Duplication (INV+DUP)
  • ID14: Paired-Duplication Inversion (DUP+INV+DUP)
  • ID15: Inversion with 5’ Flanking Duplication and 3’ Flanking Deletion (DUP+INV+DEL)
  • ID16: Inversion with 5’ Flanking Deletion and 3’ Flanking Duplication (DEL+INV+DUP)
  • ID17: Inverted Duplication with Flanking Triplication (DupTripDup-INV)
  • ID18: Insertion with Deletion (INSdel)

Toy Example (CSV mode):

cd your_home_path
python -m BVSim -ref 'your_home_path/BVSim/empirical/sub_hg19_chr1.fasta' -save your_saving_url -seed 1 -rep 1 -csv -write -snp 2000

Parameters for CSV Mode

The lengths of the CSVs follow different Gaussian distributions with modifiable means (-mu) and standard deviations (-sigma).

Parameter Type Description Default
-csv_num int Number for each type of CSV, superior to -csv_total_num 0
-csv_total_num int Total number for CSV, assign number of each type by empirical weights 0
-num_ID1_csv to -num_ID18_csv int Number of respective CSV types 5
-mu_ID1 to -mu_ID18 int Mean of Gaussian distribution of CSV length 1000
-sigma_ID1 to -sigma_ID18 int Standard deviation of Gaussian distribution of CSV length 100

Uniform Parallel Mode

Add -cores, -len_bins to your command, and write a .job file (task01.job) as follows (-c 5 means 5 cores, should be the same as -cores 5), parallel simulation will be allowed.

Toy Example (Uniform-parallel mode): task01.job

#!/bin/bash
#SBATCH -J uniform_parallel
#SBATCH -N 1 -c 5
#SBATCH --output=output.txt
#SBATCH --error=err.txt

source /opt/share/etc/miniconda3-py39.sh
conda activate BVSim
cd your_home_path
python -m BVSim -ref your_home_path/hg19/hg19_chr21.fasta -save your_home_path/test_data/BVSim/task03/ -cores 5 -len_bins 500000 -rep 3 -snp 200 -snv_del 200 -snv_ins 200 -write
conda deactivate

Submit the job file by:

sbatch task01.job

Parameters for Uniform parallel Mode

Parameter Type Description Default
-cores int Number of kernels for parallel processing 1
-len_bins int Length of bins for parallel processing 50000

Wave Mode

In Wave mode, users can provide a .bed file generated from an empirical .vcf file (for example, from HG002) or multiple BED files derived from samples of a selected population (such as the 15 Cell samples). This functionality allows you to generate non-uniform insertions and deletions with various options.

User-defined Sample(s) and Input BED File Requirements

The BED file must adhere to the following requirements:

  • First Column: Location (genomic position)
  • Second Column: DEL/INS label (indicating if the variation is a deletion or insertion)
  • Third Column: Length (absolute value of the variation)

Each column should be separated by a tab character (\t) and must not include headers. Additionally, each BED file should represent variations on the same sequence.

Generate a BED File for a Single Sample

To generate a single input BED file from the HG002 .vcf file of chromosome 21, you can use the following commands in your terminal:

# Download the VCF file and its index
wget https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/AshkenazimTrio/HG002_NA24385_son/NIST_SV_v0.6/HG002_SVs_Tier1_v0.6.vcf.gz 
wget https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/AshkenazimTrio/HG002_NA24385_son/NIST_SV_v0.6/HG002_SVs_Tier1_v0.6.vcf.gz.tbi

# Activate the bcftools environment
conda activate bcftools

# Generate the BED file using bcftools and awk
bcftools view -H -r 21 -i 'SVTYPE="INS" || SVTYPE="DEL"' /home/adduser/data/test_data/TGS/hg002/HG002_SVs_Tier1_v0.6.vcf.gz | \
awk -v OFS='\t' '{
    split($8, a, ";");
    for (i in a) {
        if (a[i] ~ /^SVTYPE/) {
            split(a[i], b, "=");
            svtype = b[2];
        }
        else if (a[i] ~ /^SVLEN/) {
            split(a[i], c, "=");
            svlen = c[2];
            if (svlen < 0) svlen = -svlen;  # Extract the absolute value of SV length
        }
    }
    print $2, svtype, svlen;  # Print the location, SV type, and absolute SV length
}' > /home/adduser/data/test_data/TGS/hg002/chr21_SV_Tier1.bed
Job Submission for Single Sample (BED Format)

To utilize this single BED file, users should call '-indel_input_bed' in the command. Below is the example of a SLURM job script that you can use to run the Wave mode simulation with single empirical data:

#!/bin/bash
#SBATCH -J full_chr21_parallel
#SBATCH -N 1 -c 5
#SBATCH --output=output_chr21_wave.txt
#SBATCH --error=err_chr21_wave.txt

source /opt/share/etc/miniconda3-py39.sh
conda activate BVSim
cd your_home_path
python -m BVSim -ref your_home_path/hg19/hg19_chr21.fasta -save your_home_path/test_data/BVSim -seed 0 -rep 2 -cores 5 -len_bins 500000 -wave -indel_input_bed your_home_path/hg002/chr21_SV_Tier1_2.bed -mode empirical -snp 2000 -snv_del 1000 -snv_ins 100 -write
conda deactivate

Submit the job file by:

sbatch task02_single.job

Generating BED Files for Multiple Samples

In this section, we will outline the steps to generate .bed files for multiple cell samples from the original Excel spreadsheet, using the 15 Cell samples as an example.

Step 1: Download the Original Excel File

First, download the Excel file containing the cell samples data:

import os

# Download the Excel file
os.system('wget https://ars.els-cdn.com/content/image/1-s2.0-S0092867418316337-mmc1.xlsx')
Step 2: Load and View the Data

Next, load the Excel file into a Pandas DataFrame and view the first few rows:

import pandas as pd

# Read the Excel file into a DataFrame
file_path = '1-s2.0-S0092867418316337-mmc1.xlsx'
df = pd.read_excel(file_path, sheet_name=0)  # Choose the correct sheet based on the file

# Display the first 5 rows of the DataFrame
print(df.head(5))
Step 3: Filter the Data

Extract the required columns and rename the first column:

# Extract the necessary columns
columns_to_keep = ['#CHROM', 'POS', 'END', 'ID', 'SVTYPE', 'SVLEN', 'MERGE_SAMPLES']
cell_df = df[columns_to_keep]

# List of all sample strings
samples = ['CHM1', 'CHM13', 'HG00514', 'HG00733', 'NA19240', 'HG02818', 'NA19434', 'HG01352', 'HG02059', 'NA12878', 'HG04217', 'HG02106', 'HG00268', 'AK1', 'HX1']
# selected population: the African population
AFR_samples = ['NA19240', 'HG02818', 'NA19434']

# Specify the columns to save in the BED file
columns_to_save = ['POS', 'SVTYPE', 'SVLEN']

# Extract rows where CHROM equals 'chr21'
chr21_df = cell_df[cell_df['CHROM'] == 'chr21']

# Display the first 10 rows for verification
print(chr21_df.head(10))

# Generate BED files for each sample in the AFR_samples list
for sample in AFR_samples:
    # Create a new DataFrame containing only rows where 'MERGE_SAMPLES' contains the current sample
    sample_df = chr21_df[chr21_df['MERGE_SAMPLES'].str.contains(sample)]

    # Specify the path for the new BED file
    bed_file_path = f'.../BVSim/empirical/{sample}_chr21.bed'

    # Save the specified columns to a BED file
    sample_df[columns_to_save].to_csv(bed_file_path, sep='\t', header=False, index=False)

Job Submission for Multiple Samples (BED Format)

We provide an example of a Job submission script using SLURM for running the Wave mode with BVSim. This script utilizes the generated multiple sample BED files. Below is the example of a SLURM job script that you can use to run the Wave mode simulation with multiple samples:

#!/bin/bash
#SBATCH -J wave
#SBATCH -N 1 -c 5
#SBATCH --output=/home/project18/code/BVSim_code/wave2_out.txt
#SBATCH --error=/home/project18/code/BVSim_code/wave2_err.txt

source /opt/share/etc/miniconda3-py39.sh
conda activate BVSim
cd /home/project18/

python -m BVSim -ref your_home_path/hg38/chr21.fasta \
-save your_home_path/BVSim/task01/ -seed 0 -rep 1 -cores 5 \
-len_bins 500000 -wave -mode empirical -snp 2000 -snv_del 1000 -snv_ins 100 \
-write -file_list NA19240_chr21 HG02818_chr21 NA19434_chr21

conda deactivate

Important Note on File Placement

Ensure that both the single sample and multiple sample BED files are placed in the .../BVSim/empirical/ directory. This organization simplifies the command structure, allowing you to specify only the base names of the files (without extensions) directly in the -file_list option, as demonstrated in the script above.

Parameters for Wave Mode

Parameter Type Description Default
-cores int Number of kernels for parallel processing 1
-len_bins int Length of bins for parallel processing 50000
-wave bool Run Wave.py script False
-mode str Mode for calculating probabilities 'probability'
-sum bool Total indel SV equals sum of the input bed False
-indel_input_bed str Input single BED file None
-file_list str Input list of multiple BED files None
Mode and Sum Parameters

The -mode parameter determines how the simulation calculates probabilities for insertions and deletions. It accepts two values:

  • 'probability': In this mode, probabilities for insertions and deletions are derived from the empirical data provided in the input BED files. The total number of variations can be defined by the -sum parameter. If -sum is set to True, the total number of insertions or deletions will be the maximum of the calculated empirical total or the specified values in -sv_ins or -sv_del. This allows for flexibility in controlling the total number of SVs in the simulation.

  • 'empirical': When set to this mode, the simulation directly uses the empirical values from the input data without any probability calculations. The total number of variations will be the sum of the provided empirical data.

The -sum parameter, when enabled, alters the total number of insertions and deletions based on the specified empirical data. If disabled, the simulation uses the fixed total values defined in -sv_ins and -sv_del, regardless of the empirical input.

Wave Region Mode

In Wave region mode, you can specify different INDEL probabilities using a BED file defined by region_bed_url. For example, if you want to increase the insertion and deletion probabilities in the tandem repeat (TR) regions of hg19, you can follow these steps.

Extract User-defined Regions (e.g. TR region) and Generate the BED File

First, extract the TR regions' positions from UCSC and create a BED file with two columns (start; end) separated by a tab character (\t).

You can generate the BED file using the following commands:

# Download the TR regions data
wget https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/references/GRCh37/resources/hg19.simpleRepeat.bed.gz 
wget https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/references/GRCh37/resources/hg19.simpleRepeat.bed.gz.tbi

# Extract the relevant columns and create the BED file
zcat hg19.simpleRepeat.bed.gz | awk 'BEGIN{OFS="\t"} {print $1, $2, $3}' > your_home_path/hg002/windows_TR.bed

# Merge overlapping intervals and remove duplicates
bedtools sort -i your_home_path/hg002/windows_TR.bed | bedtools merge -i stdin | awk 'BEGIN{OFS="\t"} {$4="TR"; print}' | uniq > your_home_path/hg002/windows_TR_unique.bed

# Filter for chromosome 21
awk '$1 == "chr21"' your_home_path/hg002/windows_TR_unique.bed > your_home_path/hg002/windows_TR_unique_chr21.bed

# Create a final BED file with start and end positions
awk '{print $2 "\t" $3}' your_home_path/hg002/windows_TR_unique_chr21.bed > your_home_path/hg002/chr21_TR_unique.bed

Job Submission for Single Sample (BED Format)

In this example, we set the seed to 0 and use a replication ID of 4. The job is configured to utilize 5 cores for parallel processing, with a bin size of 500,000. We will generate 10,000 SNPs, along with 100 micro deletions and 100 micro insertions. The probabilities for these insertions and deletions are specified in the input BED file (-indel_input_bed) using the empirical mode (-mode). Additionally, we have set the probabilities for insertions (-p_ins_region) and deletions (-p_del_region) to approximately 0.6 for the total located in the TR region defined by -region_bed_url.

#!/bin/bash
#SBATCH -J full_chr21_parallel
#SBATCH -N 1 -c 5
#SBATCH --output=output_chr21_wave_region.txt
#SBATCH --error=err_chr21_wave_region.txt

source /opt/share/etc/miniconda3-py39.sh
conda activate BVSim
cd your_home_path
python -m BVSim -ref your_home_path/hg19/hg19_chr21.fasta -save your_home_path/test_data/BVSim -seed 0 -rep 4 -cores 5 -len_bins 500000 -wave_region -indel_input_bed your_home_path/hg002/chr21_SV_Tier1.bed -mode empirical -snp 10000 -snv_del 100 -snv_ins 100 -write -p_del_region 0.6 -p_ins_region 0.6 -region_bed_url your_home_path/hg002/chr21_TR_unique.bed
conda deactivate

Submit the job file using the following command:

sbatch task03.job

Parameters for Wave Region Mode

The table below summarizes the parameters available for Wave region mode:

Parameter Type Description Default
-cores int Number of kernels for parallel processing 1
-len_bins int Length of bins for parallel processing 50000
-wave bool Run Wave.py script False
-mode str Mode for calculating probabilities 'probability'
-sum bool Total number of insertions and deletions equals sum of the input bed False
-indel_input_bed str Input single BED file None
-file_list str Input list of multiple BED files None
-wave_region bool Run Wave_TR.py script False
-p_del_region float Probability of SV DEL in the user-defined region for deletion 0.5
-p_ins_region float Probability of SV INS in the user-defined region for insertion 0.5
-region_bed_url str Path of the BED file for the user-defined region 'your_home_path/hg002/chr21_TR_unique.bed'

Human Genome

For the human genome, we derive the length distributions of SVs from HG002 and the 15 representative samples. For SNPs, we embed a learned substitution transition matrix from the dbSNP database. With a user-specified bin size, BVSim learns the distribution of SV positions per interval. It can model the SVs per interval as a multinomial distribution parameterized by the observed frequencies in HG002 (GRCh37/hg19 as reference) or sample the SV numbers per interval from a Gaussian distribution with the mean and standard deviation computed across the 15 samples (GRCh38/hg38 as reference). Calling ‘-hg19’ or ‘-hg38’ and specifying the chromosome name can activate the above procedures automatically for the human genome.

In the following example, we use 5 cores and 500,000 as length of the intervals. The reference is chromosome 21 of hg19, so we call "-hg19 chr21" in the command line to utilize the default procedure. In addition, we generated 1,000 SNPs, 99 duplications, 7 inversions, 280 deletions, and 202 insertions. The ratio of deletions/insertions in the tandem repeat regions with respect to the total number is 0.810/0.828. We also set the minimum and maximum lengths of some SVs.

Toy example (-hg19)

#!/bin/bash
#SBATCH -J 0_hg19_chr21
#SBATCH -N 1 -c 5
#SBATCH --output=output.txt
#SBATCH --error=err.txt

source /opt/share/etc/miniconda3-py39.sh
conda activate BVSim
cd your_home_path
python -m BVSim -ref your_home_path/hg19/hg19_chr21.fasta -save your_home_path/test_data/BVSim/ -seed 0 -rep 0 -cores 5 -len_bins 500000 -hg19 chr21 -mode probability -snp 1000 -sv_trans 0 -dup 99 -sv_inver 7 -sv_del 280 -sv_ins 202 -snv_del 0 -snv_ins 0 -p_del_region 0.810 -p_ins_region 0.828 -region_bed_url /home/project18/data/test_data/TGS/hg002/chr21_TR_unique.bed -delmin 50 -delmax 2964912 -insmin 50 -insmax 187524
conda deactivate

Uninstallation for Updates

To update to the latest version of BVSim, you can uninstall and delete the cloned files. Then, try to clone from the new repository and install again.

cd your_home_path
pip uninstall BVSim

Workflow of BVSim

The following figure illustrates the workflow of BVSim, encapsulated within a dashed box, and demonstrates how the output files interact with read simulators, the alignment tool Minimap2, Samtools, and evaluation tools. Workflow of BVSim

Definitions of SVs Simulated by BVSim

Definitions of SVs

SEEK ID: https://workflowhub.eu/workflows/1361?version=1

DOI: 10.48546/workflowhub.workflow.1361.1

Version History

main @ 72a3760 (earliest) Created 10th May 2025 at 14:56 by Zhen Zhang

update VCF format


Frozen main 72a3760
help Creators and Submitter
Creators
  • Yongyi Luo
  • Zhen Zhang
  • Jiandong Shi
  • Jingyu Hao
  • Sheng Lian
  • Taobo Hu
  • Toyotaka Ishibashi
  • Depeng Wang
  • Shu Wang
  • Weichuan Yu
  • Xiaodan Fan
Submitter
Discussion Channel
https://github.com/YongyiLuo98/BVSim/pulls
Citation
Luo, Y., & Zhang, Z. (2025). BVSim: A Benchmarking Variation Simulator Mimicking Human Variation Spectrum. WorkflowHub. https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.1361.1
License
GNU General Public License v3.0 only
Activity

Views: 38   Downloads: 3

Created: 10th May 2025 at 14:56

Last updated: 10th May 2025 at 15:23

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