GraphChainer is an accurate aligner of long reads to a variation graph, based on co-linear chaining.

To compile, run these:

• Install miniconda https://conda.io/projects/conda/en/latest/user-guide/install/index.html
• git submodule update --init --recursive
• conda env create -f CondaEnvironment.yml
• conda activate GraphChainer
• make bin/GraphChainer

Quickstart: ./bin/GraphChainer -t 4 -f reads.fastq -g graph.gfa -a out.gam

Key parameters:

• -t Number of threads (optional, default 1).
• -f Input reads. Format .fasta / .fastq / .fasta.gz / .fastq.gz. You can input multiple files with -f file1 -f file2 ... or -f file1 file2 ....
• -g Input graph, format .gfa / .vg. This graph must be acyclic, see below how to construct an acyclic graph with vg.
• -a Output file name. Format .gam or .json.

Parameters related to colinear chaining:

• --speed <int> Use 2 or 3 (or larger values) if you want GraphChainer to be faster, but sligthly less accurate (default 1).
• --colinear-split-len <int> The length of the fragments in which the long read is split to create anchors (default 35).
• --colinear-split-gap <int> The distance between consecutive fragments (default 35). If --speed is set, then always --colinear-split-gap = --speed * --colinear-split-len.
• --colinear-gap <int> When converting an optimal chain of anchors into an alignment path, split the path if the distance in the graph between consecutive anchors is greater than this value (default 10000).

Use vg and run:

vg construct -t 30 -a -r {ref} -v {vcf} -R 22 -p -m 3000000

If you use GraphChainer, please cite as:

Jun Ma, Manuel Cáceres, Leena Salmela, Veli Mäkinen, Alexandru I. Tomescu. GraphChainer: Co-linear Chaining for Accurate Sequence-to-Variation-Graph Alignment. Submitted, 2021

GraphChainer is built on the excellent code base of GraphAligner, which is released under MIT License. GraphAligner is described in the paper GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment by Mikko Rautiainen and Tobias Marschall.