fastreer 2.1.0

CLI toolkit for phylogenetic trees and distance matrices from VCF and FASTA

fastreeR: Fast Tree Reconstruction Tools for Genomics (VCF/FASTA to Distance/Tree)

fastreeR is a hybrid toolkit combining a high-performance Java backend (BioInfoJava-Utils, a modular Java library for bioinformatics pipelines) with flexible and user-friendly interfaces across multiple platforms and environments, enabling seamless integration into a variety of genomic workflows. It enables fast computation of distance matrices and phylogenetic trees from genetic variant data in VCF or genomic sequences in FASTA format.

Integration and Accessibility

fastreeR offers interface, which is accessible in the following ways:

• 🆕 Java Backend (v2.2.0) !! implements streaming bootstrap; from VCF file get a newick tree with encoded bootstrap support values
• Java Backend (v2.0.0) 100x times FASTreER and only a couple hundred MB RAM needed. Java 11+ suggested.
• Bioconda: install with conda install -c bioconda fastreer (recipe)
• Docker: available on DockerHub and GHCR for containerized execution
• PyPI: install with pip install fastreer (repository)
• Python CLI: through a lightweight Python wrapper that calls the Java backend
• R / Bioconductor: via rJava (package)
• Galaxy: available on Galaxy Toolshed.
• Pure Java API: developers can integrate this library directly in Java-based pipelines or software.

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• fastreeR: Fast Tree Reconstruction Tools for Genomics
  • Integration and Accessibility
  • Key Features
  • Requirements
    • RAM Requirements
  • Installation and Usage
    • Conda
    • Docker
    • PyPI
    • Python CLI
    • R package
    • Galaxy
    • From Java backend source
  • Distances from VCF
  • CLI Interface
    • Commands
    • Examples
    • Options
  • Integration with Java Backend
  • Integration with R
  • Sample data
  • Citation
  • Author
  • License

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Key Features

• 🥾 Streaming bootstrap support from VCF to NEWICK.
• 🚀 With a superior multithreaded concurrency model and minimal RAM usage, from GBs down to just MBs!
• ⚡ Ultra-fast computation of sample-wise cosine distances from large VCF and D2S k-mer based distances from FASTA files.
• Generate agglomerative neighbor-joining phylogenetic trees directly from VCF or distance matrices.
• Multithreaded execution for speed and scalability.
• Cluster distance matrices hierarchically with dynamic tree pruning.
• Clean Python CLI for scripting and pipeline integration
• Streamlined integration with R via rJava
• Available on Galaxy Toolshed
• Compatible with standard bioinformatics formats (PHYLIP, Newick)

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Requirements

• Java 11+ (LTS version with improved concurrency)
• Python 3.7+
• Maven (if you want to build from the source)
• GNU/Linux, Windows or macOS

Memory requirements for VCF input

No more GBs of RAM! Only the distance matrix is kept in memory:

• 4 bytes x (#samples²) x #threads
• Example: 1000 samples with 32 threads → ~128MB RAM

VCF caching is minimal: Only 2 VCF lines per thread are pre-cached.

• In the simple diploid case (e.g., 0/1, 1|0), each genotype requires ~4 characters (8 bytes).
• For 1000 samples and 32 threads, this adds up to ~1MB RAM.

JVM will need at least 64-128 MB in order to efficiently run.

Total memory footprint: just a few hundred MB, even for large datasets.

It is not straightforward to define a strict minimum amount of RAM required for a given number of SNPs and samples, as JVM behavior can vary across different systems and configurations. From our own experiments, a rough estimate for the minimum usable memory is around 10 bytes per variant per sample. For example, a VCF file with 1 million variants and 1,000 samples would require at least 10 x 10⁶ x 10³ = 10 GB of allocated memory. However, running with this minimal allocation may result in frequent and prolonged garbage collection events, leading to significantly longer runtimes. For optimal execution, we recommend allocating 15-20 bytes per variant per sample (i.e., 15-20 GB for the same example), which reduces garbage collection overhead and ensures smoother performance.

In order to allocate RAM, a special parameter needs to be passed while JVM initializes. JVM parameters can be passed by setting java.parameters option. The -Xmx parameter, followed (without space) by an integer value and a letter, is used to tell JVM what is the maximum amount of heap RAM that it can use. The letter in the parameter (uppercase or lowercase), indicates RAM units. For example, parameters -Xmx1024m or -Xmx1024M or -Xmx1g or -Xmx1G, allocate 1 Gigabyte or 1024 Megabytes of maximum RAM for JVM.

In order to allocate 1024MB of RAM for the JVM, through R code, use:

options(java.parameters = "-Xmx1024M")

When using fastreeR as a CLI, then RAM allocation in MB can be achieved with the relevant argument --mem MEM.

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Installation and Usage

Via Conda

fastreeR is available on Bioconda. You can install it in a new conda environment like so:

conda create -y -n fastreer-env -c bioconda fastreer && activate fastreer-env
fastreeR --help

Via Docker

fastreeR is available as a lightweight, multithreaded, platform-independent Docker image hosted on both DockerHub and GHCR.

From DockerHub:

docker pull gkanogiannis/fastreer:latest

Or from GitHub Container Registry (GHCR):

docker pull ghcr.io/gkanogiannis/fastreer:latest

To compute a tree directly from a VCF file:

docker run --rm -v $(pwd):/data gkanogiannis/fastreer:latest \
    VCF2TREE -i /data/input.vcf -o /data/output.nwk --threads 4

This:

• Mounts your working directory $(pwd) inside the container
• Reads input.vcf and writes output.nwk relative to your host
• Uses 4 threads for faster computation

The Docker image includes:

• Java 21
• Python3
• All required .jar libraries
• The fastreeR.py CLI entry point

Example: FASTA to distance

docker run --rm -v $(pwd):/data gkanogiannis/fastreer \
    FASTA2DIST -i /data/sequences.fasta -o /data/sequences.dist -k 4 -t 2

Memory tuning. Use the --mem option to control how much memory is allocated to the Java backend:

docker run --rm -v $(pwd):/data gkanogiannis/fastreer \
    VCF2TREE -i /data/input.vcf -o /data/output.nwk --mem 128

Internally, this sets the Java heap to -Xmx128G.

As a PyPI Module

You can install the Python CLI directly from PyPI using:

pip install fastreer

This will install the fastreeR command-line tool (fastreer) and include the Java backend jars required for running all commands.

To check it installed correctly:

fastreeR --version

Via a Python CLI wrapper

Another easy method for using fastreeR is by its Python CLI:

git clone https://github.com/gkanogiannis/fastreeR.git
python fastreeR/fastreeR.py

Note: If you want to use a custom backend location, set the environment variable FASTREER_JAR_DIR.

As an R package

To install fastreeR as an R package:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}
BiocManager::install("fastreeR")

You can install the development version of fastreeR R package like so:

devtools::install_github("gkanogiannis/fastreeR")

With Galaxy

Search in Galaxy Tools for fastreer or ask your Galaxy Admin to install it from toolshed.

From java backend source

To build the Java backend from source code:

git clone https://github.com/gkanogiannis/fastreeR.git
git clone https://github.com/gkanogiannis/BioInfoJava-Utils.git
pushd BioInfoJava-Utils
mvn clean initialize package && popd

Then copy the resulting .jar file(s) to the fastreeR/inst/java/ directory:

cp BioInfoJava-Utils/bin/*.jar fastreeR/inst/java/

Finally run the tool from its Python CLI:

python fastreeR/fastreeR.py

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Distances from VCF

Calculates a cosine type dissimilarity measurement between the n samples of a VCF file.

Biallelic or multiallelic (maximum 7 alternate alleles) SNP and/or INDEL variants are considered, phased or not. Some VCF encoding examples are:

• heterozygous variants : 1/0 or 0/1 or 0/2 or 1|0 or 0|1 or 0|2
• homozygous to the reference allele variants : 0/0 or 0|0
• homozygous to the first alternate allele variants : 1/1 or 1|1

If there are n samples and m variants, an nxn zero-diagonal symmetric distance matrix is calculated. The calculated cosine type distance (1-cosine_similarity)/2 is in the range [0,1] where value 0 means completely identical samples (cosine is 1), value 0.5 means perpendicular samples (cosine is 0) and value 1 means completely opposite samples (cosine is -1).

The calculation is performed by a Java back-end implementation, that supports multi-core CPU utilization and can be demanding in terms of memory resources.

Output distances is a PHYLIP compatible file will contain n+1 lines. The first line contains the number n of samples and number m of variants, separated by space. Each of the subsequent n lines contains n+1 values, separated by space. The first value of each line is a sample name and the rest n values are the calculated distances of this sample to all the samples. Example output file of the distances of 3 samples calculated from 1000 variants:

3 1000
Sample1	0.0	0.5	0.2
Sample2	0.5	0.0	0.9
Sample3	0.2	0.9	0.0

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CLI Interface

The Python CLI (fastreeR.py) interfaces with the Java backend via subprocess, providing a unified command-line interface for all supported tools.

Commands

General Syntax

python3 fastreeR.py <COMMAND> [OPTIONS]

COMMAND	Description
VCF2DIST	Compute a cosine distance matrix from a VCF file
VCF2TREE	Compute a Newick NJ tree directly from a VCF
DIST2TREE	Compute a Newick NJ tree from a distance matrix
FASTA2DIST	Compute a D2S distance matrix from a FASTA file

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Examples

Compute Distance Matrix from VCF

python fastreeR.py VCF2DIST -i input.vcf -o output.dist --threads 16 --verbose

Compute Newick NJ tree directly from a VCF file.

python fastreeR.py VCF2TREE -i input.vcf -o output.nwk --threads 16 --verbose

You can also request bootstrap replicates directly from the VCF source. The Java backend will perform streaming bootstrap sampling and encode bootstrap support values at internal nodes of the returned Newick string. For example:

python fastreeR.py VCF2TREE -i input.vcf -o output_with_boot.nwk --threads 8 --bootstrap 100

The generated Newick will contain node support values (percentage across replicates) which can be inspected with phylogenetic tools such as ape in R.

Compute Tree from Distance Matrix

python fastreeR.py DIST2TREE -i output.dist -o output.nwk

Input format: tab-separated PHYLIP-compatible matrix.

Compute D2S k-mer distance matrix from a FASTA file.

python3 fastreeR.py FASTA2DIST -i seqs.fasta -o output.dist -k 4 -t 2 --normalize

Pipe input from gzip-compressed file

zcat input.vcf.gz | python fastreeR.py VCF2TREE -i - -o output.nwk

Print version and citation

python fastreeR.py --version

Output Examples

• Distance matrices: PHYLIP-compatible text
• Trees: Newick format
• Output is streamed line-by-line (suitable for large datasets)

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Options (common to all commands)

• -i, --input : Input file (VCF or distance matrix). Use - for stdin.
• -o, --output : Output file. If omitted, prints to stdout.
• -t, --threads : Number of threads (default: 1).
• --mem MEM : Max RAM for JVM in MB (default: 256).
• --lib LIB : Path to the folder containing backend JAR libraries (default: inst/java)
• --verbose : Print progress information to stderr.
• --pipe-stderr : Pipe stderr and forward from Python (default: direct passthrough to terminal).
• --version : Print version and citation information.

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Integration with Java Backend

The CLI wraps tools from the BioInfoJava-Utils project and dynamically builds the Java classpath from all .jar files located in inst/java/.

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Integration with R

All core functionality is available via the fastreeR R package (Bioconductor/devel):

library(fastreeR)
tree <- vcf2tree("input.vcf")
plot(tree)

See fastreeR R manual and fastreeR R vignette for usage in R.

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Sample data

Toy vcf, fasta and distance sample data files are provided in inst/extdata.

samples.vcf.gz

Sample VCF file of 100 individuals and 1000 variants, in Chromosome22, from the 1K Genomes project. Original file available at http://hgdownload.cse.ucsc.edu/gbdb/hg19/1000Genomes/phase3/

vcfFile <- system.file("extdata", "samples.vcf.gz", package = "fastreeR")

samples.vcf.dist.gz

Distances from the previous sample VCF

vcfDist <- system.file("extdata", "samples.vcf.dist.gz", package = "fastreeR")

samples.vcf.istats

Individual statistics from the previous sample VCF

vcfIstats <- system.file("extdata", "samples.vcf.istats", package = "fastreeR")

samples.fasta.gz

Sample FASTA file of 48 random bacteria RefSeq from ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/bacteria/.

fastaFile <- system.file("extdata", "samples.fasta.gz", package = "fastreeR")

samples.fasta.dist.gz

Distances from the previous sample FASTA

fastaDist <- system.file("extdata", "samples.fasta.dist.gz", package = "fastreeR")

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Citation

If you use fastreeR in your research, please cite:

Anestis Gkanogiannis (2016) A scalable assembly-free variable selection algorithm for biomarker discovery from metagenomes
BMC Bioinformatics 17, 311.
https://doi.org/10.1186/s12859-016-1186-3
https://github.com/gkanogiannis/fastreeR

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Author

Anestis Gkanogiannis Website: https://www.gkanogiannis.com
ORCID: 0000-0002-6441-0688

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License

fastreeR is licensed under the GNU General Public License v3.0.
See the LICENSE file for details.

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