Here we will version the main project consisting on building a sequence to graph aligner 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

Parameters inherited from GraphAligner:

• -t Number of threads.
• -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.
• -a Output file name. Format .gam or .json.

Parameters related to colinear chaining:

• --speed <int> Default 1. Use 2 or 3 (or larger values) if you want GraphChainer to be faster, but sligthly less accurate.
• --colinear-split-len <int> Default 35. The length of the fragments in which the long read is split to create anchors.
• --colinear-split-gap <int> Default 35. The distance between consecutive fragments. If --speed is set, then always --colinear-split-gap = --speed * --colinear-split-len.
• --colinear-gap 1000 Default 10000. When converting an optimal chain of anchors into an alignment path, split the path if the distance between consecutive anchors is greater than this value.

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](GraphAligner: rapid and versatile sequence-to-graph alignment) by Mikko Rautiainen and Tobias Marschall.