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BaseVar: Call variants from ultra low-pass WGS data

BaseVar is a specialized tool for variant calling from ultra low-depth (<1x) sequencing data, with particular focus on non-invasive prenatal testing (NIPT) and large-scale population genomics. Leveraging maximum likelihood and likelihood ratio models, BaseVar accurately identifies polymorphisms at genomic positions and estimates allele frequencies across thousands of samples simultaneously. For the mathematical foundations, refer to the BaseVar publication in Cell Genomics.

BaseVar is fully implemented in C++17 and delivers over 10× the speed of its original Python counterpart, while using dramatically less memory. With -B 200 and one thread, memory usage is typically 3–4 GB per thread — compared to 20+ GB in the Python version.

Citation

If you use BaseVar in your research work, please cite the following paper:

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

Installation

Option 1 — Download pre-built binary (Recommended, no compilation needed)

https://github.com/ShujiaHuang/BaseVar2/releases/latest/download/basevar-linux-static

Pre-built static binaries are available on the GitHub Releases page.

Platform Download Notes
Linux (x86_64) basevar-linux-static Requires glibc ≥ 2.35 (see below)
macOS (arm64 / Intel) basevar-macos-static Requires macOS 12+

System requirements for basevar-linux-static

The Linux binary is a partial-static build (built on Ubuntu 22.04 / glibc 2.35 in CI). It bundles libstdc++, libgcc, htslib, zlib, bzip2, xz and openssl statically; only the system C library (glibc) is linked dynamically — and glibc symbol versions are forward-compatible only, so the binary requires the host glibc to be ≥ 2.35.

Confirmed compatible distributions (glibc ≥ 2.35):

Distribution glibc basevar-linux-static
Ubuntu 22.04 LTS 2.35 ✅
Ubuntu 24.04 LTS 2.39 ✅
Debian 12 (bookworm) 2.36 ✅
Fedora 36+ 2.35+ ✅
openSUSE Tumbleweed rolling ✅

Distributions where basevar-linux-static will NOT run (glibc too old — please compile from source instead):

Distribution glibc basevar-linux-static
CentOS 7 / RHEL 7 2.17 ❌
CentOS 8 / RHEL 8 / Rocky 8 / Alma 8 2.28 ❌
CentOS 9 / RHEL 9 / Rocky 9 / Alma 9 2.34 ❌
Ubuntu 18.04 / 20.04 2.27 / 2.31 ❌
Debian 10 / 11 2.28 / 2.31 ❌

Quick check on your machine:

# If the printed glibc version is >= 2.35, basevar-linux-static will run.
ldd --version | head -1

A typical incompatibility error looks like:

./basevar-linux-static: /lib64/libc.so.6: version `GLIBC_2.35' not found (required by ./basevar-linux-static)

If you see this — or you are on CentOS / RHEL / Rocky / AlmaLinux / older Ubuntu / older Debian — please use Option 2: compile from source. The build is straightforward and takes only a few minutes.

# Linux
wget https://github.com/ShujiaHuang/BaseVar2/releases/download/v2.3.1/basevar-linux-static
chmod +x basevar-linux-static
./basevar-linux-static --help
# macOS
curl -LO https://github.com/ShujiaHuang/BaseVar2/releases/download/v2.3.1/basevar-macos-static
chmod +x basevar-macos-static
./basevar-macos-static --help

Option 2 — Compile from source

Requires: C++17 compiler (GCC 7+ or Apple Clang 10+), CMake ≥ 3.12, and system libraries: zlib, bzip2, xz-utils, libcurl.

Step 1 — Clone the repository (including htslib submodule)

git clone --recursive https://github.com/ShujiaHuang/basevar2.git
cd basevar2

If you forgot --recursive, run: git submodule update --init --recursive

Step 2 — Build with CMake (standard dynamic build)

cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build

The executable bin/basevar will be produced. Verify with:

./bin/basevar --help

Step 3 (Optional) — Build a static binary locally

macOS (requires Homebrew):

brew install zlib bzip2 xz
cmake -B build-static -DSTATIC_BUILD=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build-static

Linux (portable static via Ubuntu/glibc — same approach used in CI):

sudo apt-get install -y build-essential cmake autoconf automake \
    zlib1g-dev libbz2-dev liblzma-dev libssl-dev
cmake -B build-static -DSTATIC_BUILD=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build-static

This bundles libstdc++, libgcc, htslib, and the compression libs statically; glibc remains dynamic. The resulting binary runs on the build host and on any other host with the same-or-newer glibc.

Option 3 — Manual g++ compilation (fallback)

First, build htslib:

cd htslib && autoreconf -i && ./configure && make && cd ..

Then compile manually:

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

macOS:

cd bin/
g++ -O3 -fPIC ../src/*.cpp ../src/io/*.cpp ../htslib/libhts.a \
    -I ../htslib -lz -lbz2 -lm -llzma -lpthread -lcurl -o basevar

Note: If you encounter a test/test_khash.c compilation error during make in htslib, you can safely ignore it — the required libhts.a archive is still produced correctly.

Commands overview

Usage: basevar <command> [options]

Commands:
  caller    Call variants and estimate allele frequencies
  pipeline  Generate per-region `basevar caller` commands for whole-genome calling
  concat    Concatenate per-region VCF files into a whole-genome VCF
  subsam    Extract a subset of samples from a VCF file
  motif     Count cfDNA end-motif (k-mer) frequencies from BAM/CRAM

basevar caller — Variant calling

Full parameter reference

About: Call variants and estimate allele frequency by BaseVar.
Usage: basevar caller [options] <-f Fasta> <-o output_file> [-L bam.list/cram.list] in1.bam [in2.bam ...] ...

Required arguments:
  -f, --reference FILE         Input reference FASTA file.
  -o, --output    FILE         Output VCF file (supports .vcf.gz).

Optional options:
  -L, --align-file-list=FILE   BAM/CRAM files list, one file per row.
  -r, --regions=REG[,...]      Restrict calling to these regions (comma-separated).
                               Formats: chr  |  chr:start  |  chr:start-end
                               Example: chr1,chr2:1000000,chr3:5000000-10000000
  -G, --pop-group=FILE         Calculate allele frequency per population group.

  -m, --min-af=float           Prior MAF threshold; positions below this are skipped.
                               Default: min(0.001, 100/num_samples). Usually auto-set.
  -Q, --min-BQ INT             Minimum base quality [10]
  -q, --mapq=INT               Minimum mapping quality [5]
  -B, --batch-count=INT        Samples per batch file [500]
  -t, --thread=INT             Number of threads [14]

  --filename-has-samplename    If BAM/CRAM filenames start with the sample ID
                               (e.g. SampleID.bam), set this flag to skip reading
                               the BAM header for sample names — saves significant time.
  --smart-rerun                Skip completed batch files and resume an interrupted run.
  -h, --help                   Show this help message and exit.

Usage examples

Minimal call from a list of BAM files (or CRAM files):

basevar caller \
    -f reference.fasta \
    -o output.vcf.gz \
    -L bamfile.list

Recommended call with quality filters and sample name optimization:

basevar caller \
    -f reference.fasta \
    -o output.vcf.gz \
    -Q 20 -q 30 -B 500 -t 24 \
    --filename-has-samplename \
    -L bamfile.list

Call a specific region:

basevar caller \
    -f reference.fasta \
    -o chr11_region.vcf.gz \
    -Q 20 -q 30 -B 500 -t 24 \
    --filename-has-samplename \
    -r chr11:5246595-5248428 \
    -L bamfile.list

Call multiple disjoint regions in one run:

basevar caller \
    -f reference.fasta \
    -o multi_region.vcf.gz \
    -Q 20 -q 30 -B 500 -t 24 \
    --regions chr11:5246595-5248428,chr17:41197764-41276135 \
    -L bamfile.list

Include BAM files directly on the command line:

basevar caller \
    -f reference.fasta \
    -o output.vcf.gz \
    -Q 20 -q 30 -B 500 \
    --filename-has-samplename \
    -L bamfile.list \
    sample1.cram sample2.bam sample3.bam

Per-population allele frequency calculation:

basevar caller \
    -f reference.fasta \
    -o output.vcf.gz \
    -Q 20 -q 30 -B 500 -t 24 \
    --filename-has-samplename \
    --pop-group sample_group.info \
    -L bamfile.list

See the example sample_group.info file for the expected format.

Resume an interrupted run:

basevar caller \
    -f reference.fasta \
    -o output.vcf.gz \
    -Q 20 -q 30 -B 500 -t 24 \
    --filename-has-samplename \
    --smart-rerun \
    -L bamfile.list

basevar pipeline — Whole-genome pipeline generator

For whole-genome variant calling, the pipeline subcommand splits the genome into sub-regions and prints one basevar caller command per sub-region to stdout. The resulting shell script can be executed sequentially, in parallel with GNU parallel, or submitted to a job scheduler (SGE / SLURM / PBS).

Since v2.2.0, this functionality is built directly into the basevar binary as a native C++ subcommand. The older scripts/create_pipeline.py Python script is still shipped for backward compatibility and produces identical output.

Pipeline-specific options

Option Description Default
-o, --outdir Output directory for VCF files and logs required
--ref_fai Reference FASTA index file (.fai) required
-d, --delta Size of each sub-region (bp) 2000000
-c, --chrom Restrict to comma-separated chromosome(s) all chromosomes

All other options (-f, -L, -r, -Q, -q, -B, -t, --filename-has-samplename, --pop-group, ...) are passed through verbatim to basevar caller. This means any new caller option works automatically without changes to the pipeline subcommand.

When -r/--regions is supplied, those regions are further split into --delta-sized windows; otherwise every chromosome in the .fai (filtered by --chrom if set) is processed.

Examples

Generate whole-genome pipeline (all chromosomes, 2 Mb windows):

basevar pipeline \
    -o /path/to/outdir \
    --ref_fai reference.fasta.fai \
    -f reference.fasta \
    -L bamfile.list \
    -Q 20 -q 30 -B 500 -t 4 \
    --filename-has-samplename \
    > basevar_wgs.sh

Generate pipeline for a single chromosome (5 Mb windows):

basevar pipeline \
    -o /path/to/outdir \
    --ref_fai reference.fasta.fai \
    -c chr20 -d 5000000 \
    -f reference.fasta \
    -L bamfile.list \
    -Q 20 -q 30 -B 500 -t 4 \
    --filename-has-samplename \
    > basevar.chr20.sh

Generate pipeline for specific regions (split into 1 Mb windows):

basevar pipeline \
    -o /path/to/outdir \
    --ref_fai reference.fasta.fai \
    -d 1000000 \
    -r chr11:5000000-7000000,chr17 \
    -f reference.fasta \
    -L bamfile.list \
    -Q 20 -q 30 -B 500 -t 4 \
    --filename-has-samplename \
    > basevar.targets.sh

Run the generated pipeline:

# Sequential (local):
bash basevar.chr20.sh

# Parallel with GNU parallel:
cat basevar_wgs.sh | parallel -j 8

# Or submit each line as a cluster job (SGE/SLURM example):
while IFS= read -r cmd; do
    echo "$cmd" | qsub -V -cwd -pe smp 4
done < basevar_wgs.sh

After all sub-jobs finish, concatenate the per-region VCFs:

ls /path/to/outdir/*.vcf.gz | sort -V > vcf.list
basevar concat -L vcf.list -o final_output.vcf.gz

The legacy Python script scripts/create_pipeline.py remains available and produces byte-identical output; replace basevar pipeline with basevar=./bin/basevar python scripts/create_pipeline.py to use it instead.

basevar concat — Concatenate VCF files

Concatenate per-region VCF files produced by basevar caller into a single VCF. The files must be provided in the correct genomic order (the tool does not sort positions).

Usage: basevar concat [options] <-o output.vcf.gz> [-L vcf.list] in1.vcf.gz [in2.vcf.gz ...]

Required:
  -o, --output=FILE      Output VCF file.

Optional:
  -L, --file-list=FILE   List of input VCF files, one per line.

Example:

# From a file list
ls outdir/*.vcf.gz | sort -V > vcf.list
basevar concat -L vcf.list -o merged.vcf.gz

# From inline file arguments
basevar concat chr1_1_2000000.vcf.gz chr1_2000001_4000000.vcf.gz -o chr1.vcf.gz

You may also use bcftools concat as a drop-in alternative.

basevar motif — cfDNA end-motif counting

Extract and count the 5' end-motif (k-mer) of every cfDNA fragment in BAM/CRAM alignments. End-motifs are a well-established feature of cell-free DNA fragmentomics — the canonical method is Jiang P. et al., Cancer Discovery 2020, "Plasma DNA End-Motif Profiling as a Fragmentomic Marker in Cancer, Pregnancy, and Transplantation" (PMID: 32111602, DOI: 10.1158/2159-8290.CD-19-0622) from Prof. Y.M. Dennis Lo's group — and are particularly informative for non-invasive prenatal testing (NIPT) and other low-pass cfDNA studies.

Reproducing the Lo lab convention: the canonical Jiang 2020 protocol (1) extracts the 5' k-mer from the reference genome at each fragment's aligned 5' position — not from the BAM SEQ field — as confirmed by the Lo group's open-source reference implementation FinaleToolkit (Zheng et al., bioRxiv 2024.05.29.596414) and the group's follow-up Mao et al., Cell Genomics 2026; (2) uses both fragment ends per pair (5' of R1 and 5' of R2); (3) restricts to properly paired reads; (4) requires MAPQ ≥ 30; (5) drops chimeric/discordant alignments via an insert-size cap (typically |isize| ≤ 1000). The recommended invocation is:

basevar motif --from-reference -f reference.fa \
              --reads both --proper-pair --max-insert-size 1000 \
              -q 30 -l 4 -o out.tsv  in1.bam in2.bam ...

The tool's defaults deliberately stay conservative (read-derived bases, --reads R1, --proper-pair off, no insert-size cap, no FASTA required) so that BAM/CRAM inputs from non-cfDNA workflows (e.g. genome-wide variant calling) still produce sensible results out of the box. Users running cfDNA / NIPT / fragmentomic analyses are strongly encouraged to use the canonical invocation above.

Each input BAM/CRAM is treated as one sample. Files are processed concurrently (one worker thread per file via -t/--thread) and results are emitted side-by-side in a single TSV without merging — every motif row carries its sample ID in the first column.

For each read passing the filters, the 5' end-motif of the underlying cfDNA fragment is extracted via one of two paths:

  • Default (read-based, no FASTA required) — take the first k bases of the BAM SEQ field directly for forward-mapped reads, or the reverse-complement of the last k bases for reverse-mapped reads (since BAM stores SEQ in reference-forward orientation, this recovers the original sequencer's 5' bases). This path is BaseVar's conservative fallback; it is itself a deviation from the canonical Lo lab convention.
  • --from-reference (canonical Lo lab path, requires -f) — fetch ref[chrom, pos, pos + k) for forward-mapped reads, or reverse_complement(ref[chrom, end - k, end)) for reverse-mapped reads, directly from the FASTA. This matches FinaleToolkit's region_end_motifs() exactly and is the recommended path for cfDNA / NIPT / fragmentomic analyses.

Motifs containing any non-ACGT base — i.e. N or an IUPAC ambiguity code such as M/R/W/S/Y/K/V/H/D/B — are excluded from the counts (and reported separately in the summary).

Full parameter reference of basevar motif

About: Count cfDNA end-motif (k-mer) frequencies from BAM/CRAM alignments.
       Each input BAM/CRAM is treated as one sample; per-sample counts are
       emitted in a single TSV (long format).
       Method follows Jiang et al., Cancer Discovery 2020 (PMID: 31911138).
Usage: basevar motif [options] <-o output.tsv> [-L bam.list] in1.bam [in2.bam ...]

Required arguments:
  -o, --output FILE            Output TSV file (sample, motif, count, frequency).

Optional arguments:
  -L, --align-file-list FILE   BAM/CRAM files list, one path per row.
  -f, --reference FILE         Reference FASTA file (required for CRAM input).
  -r, --regions REG[,...]      Restrict counting to these regions, comma-separated.
                               Formats: chr | chr:start | chr:start-end
  -l, --length INT             Motif length k, range [1, 10] [4]
  -q, --mapq INT               Minimum MAPQ to keep a read [30]
  -t, --thread INT             Number of worker threads (one file per thread)
                               [hardware_concurrency]
      --reads {R1|R2|both}     Which read in a pair to use for the 5' end-motif [R1].
                               The default `R1` is conservative and works for any
                               BAM/CRAM. To reproduce the canonical Lo lab cfDNA
                               end-motif method (Jiang et al., Cancer Discovery 2020,
                               PMID: 32111602), use `--reads both` so that EACH
                               fragment contributes TWO end-motifs (5' of R1 AND
                               5' of R2).
      --include-zero           Emit all 4^k motifs (zeros included) in TSV (default ON)
      --no-include-zero        Suppress motifs with zero count in TSV.
      --filename-has-samplename
                               Derive sample IDs from filenames instead of reading the
                               BAM @RG SM tag.  E.g. /path/SampleA.bam -> SampleA.
      --proper-pair            Only count reads flagged as properly paired
                               (BAM_FPROPER_PAIR).  Required by the Lo lab convention
                               for cfDNA analyses; silently ignored for SE data. [off]
      --max-insert-size INT    Discard reads whose |insert size| > INT. 0 = no limit.
                               Lo lab cfDNA pipelines typically use 1000 to drop
                               chimeric / discordantly-mapped reads.
                               Silently ignored for SE data. [0]
      --from-reference         Extract motifs from the REFERENCE genome at each
                               fragment's 5' alignment position (canonical Lo lab
                               cfDNA method, as implemented in FinaleToolkit /
                               Zheng et al., bioRxiv 2024.05.29.596414).  Requires
                               -f/--reference.  Recommended for cfDNA / NIPT /
                               fragmentomic analyses.  When OFF (default), motifs
                               are extracted from the read's own sequenced bases
                               (BaseVar's conservative fallback that does not
                               require a FASTA). [off]
  -h, --help                   Show this help message and exit.

Recommended cfDNA / NIPT invocation (Lo lab convention):
  basevar motif --from-reference -f ref.fa \
                --reads both --proper-pair --max-insert-size 1000 \
                -q 30 -l 4 -o out.tsv  in1.bam in2.bam ...

Sample identification

For each input file the runner resolves a sample ID using the following order:

  1. The SM value of the first @RG line in the BAM/CRAM header (default).
  2. The filename stem — everything before the first . of the basename — when --filename-has-samplename is set, or when the @RG SM tag is missing.

If two inputs resolve to the same sample ID, that ID will appear in two distinct row blocks of the TSV; consider --filename-has-samplename together with disambiguating filenames (e.g. SampleA.run1.bam, SampleA.run2.bam) when this matters.

Default filters (cfDNA-friendly)

A read contributes to the motif count only if all the following hold:

  • The read is mapped (not BAM_FUNMAP).
  • It is not a secondary, supplementary, duplicate, or QC-fail alignment.
  • MAPQ >= --mapq (default 30).
  • For paired-end data, the read matches the --reads policy (default R1). Single-end reads are treated as an implicit R1 (accepted under R1/both, rejected under R2).
  • The first k decoded bases contain only A/C/G/T (no N or IUPAC ambiguity codes).

Output format

The TSV file uses long format with one row per (sample, motif) pair:

#sample  motif   count   frequency
SampleA  AAAA    18342   0.045312
SampleA  AAAC    7521    0.018584
...
SampleB  AAAA    20131   0.048876
SampleB  AAAC    8033    0.019508
...
  • sample: sample ID (see Sample identification above).
  • motif: the k-mer (length controlled by -l).
  • count: number of reads in that sample whose 5' end-motif equals this k-mer.
  • frequency: count / used_reads_of_that_sample — frequencies are computed per-sample, not against the pooled total.

When --include-zero is enabled (the default), all 4k motifs are emitted for every sample — yielding a tidy, ML-ready fixed-shape feature matrix. Use --no-include-zero to drop zero-count rows.

A human-readable summary is also written to stdout, listing the per-sample totals (total / filtered / used / N-motif counts) plus an aggregate row.

Concurrency

File-level parallelism is the granularity used by the motif counter — each worker thread processes a single BAM/CRAM end-to-end, and there is no shared mutable state between workers. The number of workers is automatically capped at min(--thread, number_of_inputs). Pass -t 1 to force the single-threaded path (useful for deterministic profiling).

Motif counter examples

Multi-sample run with 8 worker threads (4-mer end-motif, default filters):

basevar motif \
    -o end_motif.4mer.tsv \
    -t 8 \
    -L bamfile.list

Restrict to a target region with stricter MAPQ, two samples:

basevar motif \
    -o end_motif.chr11.tsv \
    -r chr11:5246595-5248428 \
    -q 30 \
    sample1.bam sample2.bam

Use both R1 and R2 (each fragment contributes two end-motifs), 5-mer:

basevar motif \
    -o end_motif.5mer.tsv \
    -l 5 --reads both \
    -t 4 \
    -L bamfile.list

Reproduce the Lo lab cfDNA end-motif method (Jiang 2020) end-to-end:

basevar motif \
    -o end_motif.lo_lab.tsv \
    --from-reference -f reference.fa \
    --reads both --proper-pair --max-insert-size 1000 \
    -q 30 -l 4 \
    -t 8 \
    -L bamfile.list

Read-based motifs (no FASTA required, BaseVar's conservative default):

basevar motif \
    -o end_motif.read_based.tsv \
    -t 8 \
    -L bamfile.list

With no --from-reference, the 5' k-mer is taken directly from the BAM SEQ field (the original sequenced bases) rather than the reference. This path does not require a FASTA, but it is itself a deviation from the canonical Lo lab convention: the Lo group's reference open-source implementation FinaleToolkit (Zheng et al., bioRxiv 2024.05.29.596414) and the Lo group's own follow-up paper (Mao et al., Cell Genomics 2026) both fetch end-motifs from the FASTA. For cfDNA / NIPT / fragmentomic analyses, prefer the recommended invocation above.

Force filename-derived sample IDs (useful when BAM headers lack @RG SM):

basevar motif \
    -o end_motif.tsv \
    --filename-has-samplename \
    -t 16 \
    -L bamfile.list

Process CRAM files (reference required):

basevar motif \
    -o end_motif.tsv \
    -f reference.fasta \
    -L cram.list

basevar subsam — Extract samples from VCF

Extract a subset of samples from a BaseVar VCF and output a new VCF with recalculated INFO fields (AC/AN/AF).

Usage: basevar subsam [options] -i <input.vcf[.gz]> -o <output.vcf[.gz]> [-s <samplelist>]

Options:
  -i, --input FILE      Input VCF/BCF file (required).
  -o, --output FILE     Output VCF/BCF file (required).
  -s, --sample FILE     File with sample names to keep (one per line).
  -O, --output-type     v: VCF | z: bgzipped VCF | b: BCF | u: uncompressed BCF
                        Default: inferred from output filename extension.
  --no-update-info      Do not recalculate AC/AN/AF INFO fields.
  --keep-all-site       Retain sites that become reference-only after subsetting.

Examples:

# Extract samples listed in a file
basevar subsam \
    -i full_cohort.vcf.gz \
    -o subset.vcf.gz \
    -s sample_names.txt

# Extract two specific samples, output as plain VCF
basevar subsam \
    -i full_cohort.vcf.gz \
    -o subset.vcf \
    -O v \
    SampleA SampleB

# Keep all sites (including ref-only after subsetting) and skip INFO update
basevar subsam \
    -i full_cohort.vcf.gz \
    -o subset.vcf.gz \
    -s sample_names.txt \
    --keep-all-site --no-update-info

Tips and best practices

  • -B / --batch-count: Controls how many samples are processed per batch. Lower values reduce per-thread memory but increase I/O. For large cohorts (>5000 samples) -B 500 is a good starting point.
  • --filename-has-samplename: If your BAM files are named {SampleID}.bam or {SampleID}.cram, always set this flag — it avoids reading every BAM header and can save hours on large cohorts.
  • --smart-rerun: Safe to add on any re-run; the program checks existing batch files and skips completed work.
  • Memory estimation: threads × batch_size / 200 × ~3–4 GB. E.g., 24 threads, -B 200 → ~72–96 GB total.
  • Output compression: Always use .vcf.gz as the output filename — BaseVar automatically writes bgzipped output when the extension is .vcf.gz.

Development

BaseVar is under active development. To update to the latest version:

git pull
git submodule update --recursive
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build

About

This is the official development repository for BaseVar, which call variants for large-scale ultra low-pass (<1.0x) WGS data, especially for NIPT data

www.cell.com/cell-genomics/fulltext/S2666-979X(24)00288-X

Topics

bioinformatics variant-calling cfdna low-coverage-sequencing nipt basevar

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basevar v2.3.1 Latest
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