RecGraph is a sequence-to-graph aligner written in Rust. Differently from most aligners, RecGraph is an exact approach that implements a dynamic programming algorithm for computing an optimal alignment between a string and a variation graph. Moreover, RecGraph can allow recombinations in the alignment in a controlled (i.e., non heuristic) way - in other words, it can perform optimal alignment to path not included in the input graphs. This follows directly from the observation that a pangenome graph includes a set of related individuals that are represented as paths of the graph.
We support 4 different ways to obtain and use RecGraph:
For user convenience, we provide static binaries for x86_64 linux and windows systems (see Releases).
Install rust, then clone and install RecGraph:
git clone https://github.com/AlgoLab/RecGraph.git
cd RecGraph
cargo build --release
RecGraph is available on bioconda:
conda create -n recgraph -c conda-forge -c bioconda recgraph
We provide a docker image, hosted on docker.hub:
docker pull algolab/recgraph
docker run algolab/recgraph --help
RecGraph requires as input a variation graph in .gfa format and a set of sequences (reads) in .fasta format and computes the alignment in .gaf format. To run RecGraph, run:
cargo run --release <reads.fa> <graph.gfa> > <alignments.gaf>
# if you built with cargo, from the root of this repo
cargo run --release -- -m 1 example/reads.fa example/graph.gfa > align.gaf
# if you have the precompiled binary
./recgraph_linux_x86-64 -m 1 example/reads.fa example/graph.gfa > align.gaf
# if you have the conda version, within the correct environment
recgraph -m 1 example/reads.fa example/graph.gfa > align.gaf
# if you use docker, please bind the volume into the container (-v)
docker run -v $(pwd)/example:/data algolab/recgraph -m1 reads.fa graph.gfa > align.gaf
RecGraph can be run in several different modes (-m flag):
-m [0,1,2,3]performs the classical POA (global, local, affine gap, and local gap)-m [4,5]performs global/semiglobal alignment in pathwise mode (i.e., following the paths of the graph)-m [8,9]performs global/semiglobal alignment in recombination mode (i.e., allowing weighted recombinations)
-m 6 and -m 7 are experimental and are not fully tested yet. They perform global/semiglobal alignment with affine gap in pathwise mode.
RecGraph also allows to set multiple parameters to tweak the dynamic programming alignment procedure. Here the list of parameters (please check also --help):
-M, --match <MATCH_SCORE> Match score [default: 2]
-X, --mismatch <MISMATCH_SCORE> Mismatch penalty [default: 4]
-O, --gap-open <GAP_OPEN> Gap opening penalty [default: 4]
-E, --gap-ext <GAP_EXTENSION> Gap extension penalty [default: 2]
-R, --base-rec-cost <BASE_REC_COST> Recombination cost,
determined with -r as R + r*(displacement_length) [default: 4]
-r, --multi-rec-cost <MULTI_REC_COST> Displacement multiplier [default: 0.1]
-B, --rec-band-width <REC_BAND_WIDTH> Recombination band width [default: 1]
-b, --extra-b <EXTRA_B> First adaptive banding par,
set < 0 to disable adaptive banded [default: 1]
-f, --extra-f <EXTRA_F> Second adaptive banding par, number of basis added to both side of
the band = b+f*L, l = length of the sequence [default: 0.01]
-t, --matrix <MATRIX> Scoring matrix file, if '-t' is used, '-M' and '-X' are not used
and you should set gap penalties in this case [default: none]
RecGraph can also be used as a library for your project. To do so, add these lines to your Cargo.toml:
[dependencies]
RecGraph = { git = "https://github.com/AlgoLab/RecGraph" }
You can use the functions defined in the api.rs file (e.g., by adding use RecGraph::api::* to your file). All the functions require just a read (as a string) and the graph (as an HashGraph). Other parameters are optional.
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