BaseVar is a specialized tool tailored for variant calling using ultra low-depth (<1x) sequencing data, particularly catering to non-invasive prenatal test (NIPT) data in human genetic studies. Leveraging maximum likelihood and likelihood ratio models, BaseVar accurately identifies polymorphisms at genomic positions and calculates allele frequencies. For in-depth mathematical explanations, refer to the comprehensive documentation available here.
Now, BaseVar has been fully implemented by C++. BaseVar showcases significant enhancements over its original Python counterpart. The C++ implementation delivers a computing speed exceeding 10 times that of the Python version, all while demanding substantially less memory. Typically, each thread (-t/--thread) consumes merely 3GB to 4GB of memory when the -B (--batch-count) option is configured to 200, a stark contrast to the Python version's requirement of over 20GB.
Please cite the following papers if you use BaseVar in your published projects or papers.
- Liu, S., Liu, Y., Gu, Y., Lin, X., Zhu, H., Liu, H., Xu, Z., Cheng, S., Lan, X., Li, L., Huang, M., Li, H., Nielsen, R., Davies, RW., Albrechtsen, A., Chen, GB., Qiu, X., Jin, X., Huang, S., (2024). Utilizing non-invasive prenatal test sequencing data for human genetic investigation. Cell Genomics 4(10), 100669 doi:10.1016/j.xgen.2024.100669
BaseVar requires C++17 or above. Compile basevar from source codes step-by-step.
You can install basevar using either of the following two methods.
This is the simplest way of installing basevar by cmake
git clone https://github.com/ShujiaHuang/basevar2.git
cd basevar
mkdir build
cmake ..
make
If you have problems downloading, please try several times.
If everything is smooth, you'll find an exectutable file named basevar in bin/ folder.
Sometimes, you may need to complete the installation manually if the above command fails to install BaseVar.
1. Download BaseVar from github
BaseVar is hosted on Github and can be downloaded with the following command:
git clone https://github.com/ShujiaHuang/basevar2.git
WARNING: Please try several times if fail to clone the data causing by the network problem.
2. Navigate into htslib folder and run the following commands
After cloing, navigate into the basevar folder and execute the following:
cd htslib
autoreconf -i
./configure
make
Note: If you encounter an error message similar to the following during the compilation of htslib, you can safely disregard it as the code should continue to function properly:
test/test_khash.c: In function 'write_stats_str2int':
test/test_khash.c:53:9: warning: implicit declaration of function 'kh_stats' [-Wimplicit-function-declaration]
53 | if (kh_stats(str2int, h, &empty, &deleted, &hist_size, &hist) == 0) {
| ^~~~~~~~
test/test_khash.c:53:18: error: 'str2int' undeclared (first use in this function)
53 | if (kh_stats(str2int, h, &empty, &deleted, &hist_size, &hist) == 0) {
| ^~~~~~~
test/test_khash.c:53:18: note: each undeclared identifier is reported only once for each function it appears in
make: *** [test/test_khash.o] Error 1Feel free to proceed with your installation tasks despite encountering this error during the compilation process.
3. Go back to the upper directory and install basevar by running the commands below
Navigate into bin/ folder firstly and execute the following commands:
Manually install in Linux
cd bin/
g++ -O3 -fPIC ../src/*.cpp ../src/io/*.cpp ../htslib/libhts.a -I ../htslib -lz -lbz2 -lm -llzma -lpthread -lcurl -lssl -lcrypto -o basevar
Manually install in MacOS
cd bin/
g++ -O3 -fPIC ../src/*.cpp ../src/io/*.cpp ../htslib/libhts.a -I ../htslib -lz -lbz2 -lm -llzma -lpthread -lcurl -o basevar
To review each of the parameters, you can type basevar caller -h in the Linux/MacOS Terminal.
$ /path/to/basevar caller -h
About: Call variants and estimate allele frequency by BaseVar.
Usage: basevar caller [options] <-f Fasta> <--output-vcf output_file> [-L bam.list] in1.bam [in2.bam ...] ...
Required arguments:
-f, --reference FILE Input reference fasta file.
-o, --output FILE Output VCF file.
Optional options:
-L, --align-file-list=FILE BAM/CRAM files list, one file per row.
-r, --regions=REG[,...] Skip positions which not in these regions. This parameter could be a list
of comma deleimited genome regions(e.g.: chr:start-end).
-G, --pop-group=FILE Calculating the allele frequency for specific population.
-m, --min-af=float Setting prior precision of MAF and skip ineffective caller positions,
a typical approach involves setting it to min(0.001000, 100/x), where x
represents the number of input BAM files min(0.001000, 100/x). In most
cases, users need not be overly concerned about this parameter, as it
is generally handled automatically by the program.
-Q, --min-BQ INT Skip bases with base quality < INT [10]
-q, --mapq=INT Skip reads with mapping quality < INT [5]
-B, --batch-count=INT INT simples per batchfile. [500]
-t, --thread=INT Number of threads. [14]
--filename-has-samplename If the prefix name of bamfiles/cramfiles start with 'Sample ID', something like 'SampleID.bam', set this
argrument could save a lot of time during get the sample id from BAMfile.
--smart-rerun Rerun process by checking batchfiles.
-h, --help Show this help message and exit.
Example usage:
[1]. basevar caller -f reference.fasta -o output.vcf.gz -Q 20 -q 30 -B 500 --filename-has-samplename -L bam.list
[2]. basevar caller -f reference.fasta -o output.vcf.gz -Q 20 -q 30 -B 500 --filename-has-samplename -L bam.list sample1.bam sample2.bam
[3]. basevar caller -f reference.fasta -o output.vcf.gz -Q 20 -q 30 -B 500 --filename-has-samplename -L bam.list -r chr1 sample1.bam sample2.bam
This command will provide detailed information about parameters of basevar.
basevar caller -f reference.fasta \
-Q 20 -q 30 -B 500 -t 24 \
--pop-group=sample_group.info \
--regions=chr11:5246595-5248428,chr17:41197764-41276135 \
--output test.vcf.gz 00alzqq6jw.bam 09t3r9n2rg.bam 0fkpl1p55b.bam ...The format of sample_group.info could be found here.
basevar caller -f reference.fasta \
-Q 20 -q 30 -B 500 -t 24 \
-L bamfile.list \
--regions=chr11:5246595-5248428,chr17:41197764-41276135 \
--pop-group=sample_group.info \
--output test.vcf.gz For stramlinened variant calling across the entire genome, you can use the pipeline generator create_pipeline.py, which distributes the computational tasks based on the --delta parameter across a specific chromosome defined by the -c parameter.
python create_pipeline.py -Q 20 -q 30 -f reference.fa --ref_fai reference_fa.fai -c chr20 --delta 5000000 -t 24 -L bamfile.list -o outdir > basevar.chr20.shBaseVar is under active development. Obtain the newest version by pulling the newest version and compilling again.