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Metagenome Graph Project

GitHub release (latest by date) bioconda downloads install with conda install with docker install from source documentation

MetaGraph is a tool for scalable construction of annotated genome graphs and sequence-to-graph alignment.

The default index representations in MetaGraph are extremely scalable and support building graphs with trillions of nodes and millions of annotation labels. At the same time, the provided workflows and their careful implementation, combined with low-level optimizations of the core data structures, enable exceptional query and alignment performance.

Main features:

  • Large-scale indexing of sequences
  • Python API for querying in the server mode
  • Encoding k-mer counts (e.g., expression values) and k-mer coordinates in source sequences (e.g., for lossless encoding of genomes)
  • Sequence alignment against very large annotated graphs (sub-k seeding allows using arbitrarily short seeds)
  • Scalable cleaning of very large de Bruijn graphs (to remove sequencing errors)
  • Support for custom alphabets (e.g., {A,C,G,T,N} or amino acids)
  • Algorithms for differential assembly

Design choices in MetaGraph:

  • Use of succinct data structures and efficient representation schemes for extremely high scalability
  • Algorithmic choices that work efficiently with succinct data structures (e.g., always prefer batched operations)
  • Modular support of different graph and annotation representations
  • Use of generic and extensible interfaces to support adding custom index representations / algorithms with little code overhead.

Documentation

Online documentation is available at https://metagraph.ethz.ch/static/docs/index.html. Offline sources are here.

Install

Conda

Install the latest release on Linux or Mac OS X with Anaconda:

conda install -c bioconda -c conda-forge metagraph

Docker

If docker is available on the system, immediately get started with

docker pull ghcr.io/ratschlab/metagraph:master
docker run -v ${HOME}:/mnt ghcr.io/ratschlab/metagraph:master \
    build -v -k 10 -o /mnt/transcripts_1000 /mnt/transcripts_1000.fa

and replace ${HOME} with a directory on the host system to map it under /mnt in the container.

To run the binary compiled for the Protein alphabet, just add --entrypoint metagraph_Protein:

docker run -v ${HOME}:/mnt --entrypoint metagraph_Protein ghcr.io/ratschlab/metagraph:master \
    build -v -k 10 -o /mnt/graph /mnt/protein.fa

As you see, running MetaGraph from docker containers is very easy. Also, the following command (or similar) may be handy to see what directory is mounted in the container or other sort of debugging of the command:

docker run -v ${HOME}:/mnt --entrypoint ls ghcr.io/ratschlab/metagraph:master /mnt

All different versions of the container image are listed here.

Install From Sources

To compile from source (e.g., for builds with custom alphabet or other configurations), see documentation online.

Typical workflow

  1. Build de Bruijn graph from Fasta files, FastQ files, or KMC k-mer counters:
    ./metagraph build
  2. Annotate graph using the column compressed annotation:
    ./metagraph annotate
  3. Transform the built annotation to a different annotation scheme:
    ./metagraph transform_anno
  4. Query annotated graph
    ./metagraph query

Example

DATA="../tests/data/transcripts_1000.fa"

./metagraph build -k 12 -o transcripts_1000 $DATA

./metagraph annotate -i transcripts_1000.dbg --anno-filename -o transcripts_1000 $DATA

./metagraph query -i transcripts_1000.dbg -a transcripts_1000.column.annodbg $DATA

./metagraph stats -a transcripts_1000.column.annodbg transcripts_1000.dbg

Print usage

./metagraph

Build graph

  • Simple build

./metagraph build -v --parallel 30 -k 20 --mem-cap-gb 10 \
                        -o <GRAPH_DIR>/graph <DATA_DIR>/*.fasta.gz \
2>&1 | tee <LOG_DIR>/log.txt
  • Build with disk swap (use to limit the RAM usage)

./metagraph build -v --parallel 30 -k 20 --mem-cap-gb 10 --disk-swap <GRAPH_DIR> \
                        -o <GRAPH_DIR>/graph <DATA_DIR>/*.fasta.gz \
2>&1 | tee <LOG_DIR>/log.txt

Build from k-mers filtered with KMC

K=20
./KMC/kmc -ci5 -t4 -k$K -m5 -fm <FILE>.fasta.gz <FILE>.cutoff_5 ./KMC
./metagraph build -v -p 4 -k $K --mem-cap-gb 10 -o graph <FILE>.cutoff_5.kmc_pre

Annotate graph

./metagraph annotate -v --anno-type row --fasta-anno \
                           -i primates.dbg \
                           -o primates \
                           ~/fasta_zurich/refs_chimpanzee_primates.fa

Convert annotation to Multi-BRWT

  1. Cluster columns
./metagraph transform_anno -v --linkage --greedy \
                           -o linkage.txt \
                           --subsample R \
                           -p NCORES \
                           primates.column.annodbg

Requires N*R/8 + 6*N^2 bytes of RAM, where N is the number of columns and R is the number of rows subsampled.

  1. Construct Multi-BRWT
./metagraph transform_anno -v -p NCORES --anno-type brwt \
                           --linkage-file linkage.txt \
                           -o primates \
                           --parallel-nodes V \
                           -p NCORES \
                           primates.column.annodbg

Requires M*V/8 + Size(BRWT) bytes of RAM, where M is the number of rows in the annotation and V is the number of nodes merged concurrently.

Query graph

./metagraph query -v -i <GRAPH_DIR>/graph.dbg \
                        -a <GRAPH_DIR>/annotation.column.annodbg \
                        --discovery-fraction 0.8 --labels-delimiter ", " \
                        query_seq.fa

Align to graph

./metagraph align -v -i <GRAPH_DIR>/graph.dbg query_seq.fa

Assemble sequences

./metagraph assemble -v <GRAPH_DIR>/graph.dbg \
                        -o assembled.fa \
                        --unitigs

Assemble differential sequences

./metagraph assemble -v <GRAPH_DIR>/graph.dbg \
                        --unitigs \
                        -a <GRAPH_DIR>/annotation.column.annodbg \
                        --diff-assembly-rules diff_assembly_rules.json \
                        -o diff_assembled.fa

See metagraph/tests/data/example.diff.json and metagraph/tests/data/example_simple.diff.json for sample files.

Get stats

Stats for graph

./metagraph stats graph.dbg

Stats for annotation

./metagraph stats -a annotation.column.annodbg

Stats for both

./metagraph stats -a annotation.column.annodbg graph.dbg

Developer Notes

Makefile

The Makefile in the top level source directory can be used to build and test metagraph more conveniently. The following arguments are supported:

  • env: environment in which to compile/run ("": on the host, docker: in a docker container)
  • alphabet: compile metagraph for a certain alphabet (e.g. DNA or Protein, default DNA)
  • additional_cmake_args: additional arguments to pass to cmake.

Examples:

# compiles metagraph in a docker container for the `DNA` alphabet
make build-metagraph env=docker alphabet=DNA

Update and create a new release

Creating a new version release is done in three steps:

  1. Update package.json and set the version
  2. Add a tag with that new version
  3. Make a new release on github

License

Metagraph is distributed under the GPLv3 License (see LICENSE). Please find further information in the AUTHORS and COPYRIGHTS files.