Requirements

CulebrONT requires Python 3.7+ and graphviz 2.40.1+.

CulebrONT has been mostly developed to work on an HPC but a local installation is also possible.

Install culebrONT PyPI package

First install culebrONT python package with pip.

python3 -m pip install culebrONT
culebrONT --help

Now, follow this documentation according to what you want, local or HPC mode.

Steps for LOCAL installation

Install CulebrONT in a local mode using culebrONT install_local command line.

culebrONT install_local

Run installation for local computer downloading singularity images automatically.
culebrONT install_local [OPTIONS]

Options

--bash_completion, --no-bash_completion

Allow bash completion of culebrONT commands on the bashrc file

Default:

True

To create a pipeline, tools used by CulebrONT are wrapped into Singularity images. These images are automatically downloaded and used to configure files needed by the pipeline. Local mode install, without scheduler, is constrains to use singularity.

After install, optionally (but recommended) you can now check CulebrONT installation using a scaled dataset. See the section Check install for details.

Steps for HPC installation

CulebrONT uses available snakemake profiles to easier cluster installation and resources management. Run the command culebrONT install_cluster to install on HPC. We tried to make HPC installation as easy as possible but it’s necessary to adapt some files according to your HPC environment.

culebrONT install_cluster

Run installation for HPC cluster

culebrONT install_cluster [OPTIONS]

Options

-s, --scheduler <scheduler>

Type the HPC scheduler (for the moment, only slurm is available ! )

Default:

slurm

Options:

slurm

-e, --env <env>

Mode for tools dependencies

Default:

modules

Options:

modules | singularity

--bash_completion, --no-bash_completion

Allow bash completion of culebrONT commands on the bashrc file

Default:

True

--create_envmodule, --no-create_enmodules

Create a env module file allowing load culebrONT in a cluster

Default:

False

-m, --modules_dir <modules_dir>

Directory used to save the module created by –create_envmodule parameter

1. Adapt profile and cluster_config.yaml

Now that CulebrONT is installed, it offers default configuration files but they can be modified. Please check and adapt these files to your system architecture.

1. Adapt the pre-formatted f –env si`snakemake profile` to configure cluster options. See the section 1. Snakemake profiles for details.

2. Adapt the cluster_config.yaml file to manage cluster resources such as partition, memory and threads available for each job. See the section 2. Adapting cluster_config.yaml for further details.

2. Adapt tools_path.yaml

As CulebrONT uses many tools, you must install them using one of two possibilities:

  1. Either through the Singularity 3.10.0+ containers,

  2. Or using the module load mode,

culebrONT install_cluster --help
culebrONT install_cluster --scheduler slurm --env modules
# OR
culebrONT install_cluster --scheduler slurm --env singularity

If --env singularity argument is used, CulebrONT download previously build images containing environment need to run CulebrONT (tools and dependencies).

Adapt the file :file:tools_path.yaml - in YAML (Yet Another Markup Language) - format to indicate to CulebrONT where the tools are installed. See the section 3. How to configure tools_path.yaml for details.

Check install

Optionally, in order to test your install of CulebrONT pipeline, a data test called Data-Xoo-sub/ is available on https://itrop.ird.fr/culebront_utilities/.

culebrONT test_install

Test_install fonction downloads a scaled data test, writes a configuration file adapted to it and proposes a command line already to run !!!

culebrONT test_install [OPTIONS]

Options

-d, --data_dir <data_dir>

Required Path to download data test and create config.yaml to run test

This dataset is automatically downloaded by culebrONT in the -d repertory using :

culebrONT test_install -d test

Launching suggested command line done by CulebrONT, in CLUSTER mode :

culebrONT run_cluster --config test/data_test_config.yaml

Or in local mode :

culebrONT run_local -t 8 -c test/data_test_config.yaml --singularity-args "--bind $HOME"

Advance installation

1. Snakemake profiles

The Snakemake-profiles project is an open effort to create configuration profiles allowing to execute Snakemake in various computing environments (job scheduling systems as Slurm, SGE, Grid middleware, or cloud computing), and available at https://github.com/Snakemake-Profiles/doc.

In order to run CulebrONT on HPC cluster, we use profiles.

Quickly, see here an example of the Snakemake SLURM profile we use for the French national bioinformatics infrastructure at IFB.

More info about profiles can be found here https://github.com/Snakemake-Profiles/slurm#quickstart.

Preparing the profile’s config.yaml file

Once, your basic profile is created. To finalize it, modify as necessary the culebrONT/culebrONT/default_profile/config.yaml to customize Snakemake parameters that will be used internally by CulebrONT:

restart-times: 0
jobscript: "slurm-jobscript.sh"
cluster: "slurm-submit.py"
cluster-status: "slurm-status.py"
max-jobs-per-second: 1
max-status-checks-per-second: 10
local-cores: 1
jobs: 200                   # edit to limit number jobs submit
latency-wait: 60000000
use-envmodules: true        # Adapt True/False only active one
use-singularity: false      # if False, please install all R package on tools_config.yaml ENVMODULE/R
rerun-incomplete: true
printshellcmds: true

2. Adapting cluster_config.yaml

In the cluster_config.yaml file, you can manage HPC resources, choosing partition, memory and threads to be used by default or specifically for each rule/tool depending on your HPC Job Scheduler (see there). This file generally belongs to a Snakemake profile (see above).

Warning

If more memory or threads are requested, please adapt the content of this file before running on your cluster.

A list of CulebrONT rules names can be found in the section Threading rules inside culebrONT

Warning

For some rules in the cluster_config.yaml as rule_graph or run_get_versions we use by default wildcards, please don’t remove it.

3. How to configure tools_path.yaml

In the tools_path file, you can find two sections: SINGULARITY and ENVMODULES. In order to fill it correctly, you have 2 options:

1. Use only SINGULARITY containers. In this case, fill only this section. Put the path to the build singularity images you want to use. Absolute paths are strongly recommended but not mandatory. See the section ‘How to build singularity images’ for further details.

SINGULARITY:
    REPORT : '{INSTALL_PATH}/containers/Singularity.report.sif'
    TOOLS : '{INSTALL_PATH}/containers/Singularity.culebront_tools.sif'

Warning

For SINGULARITY containers to be actually used, one needs to make sure that the –use-singularity flag is included in the snakemake command line.

  1. Use only ENVMODULES. In this case, fill this section with modules available on your cluster (here is an example):

ENVMODULE:
    R : "r"
    QUAST : "quast"
    MAUVE : "mauve"
    SHASTA : "shasta"
    ASSEMBLYTICS : "assemblytics"
    MEDAKA : "medaka"
    KAT : "kat"
    BLOBTOOLS : "blobtools"

CulebrONT needs a wide set of R modules for reporting, if you use ENVMODULE R. Just have a look at dependencies in the Containers/Singularity.report.def file. Yes, plenty of packages!! That’s why we provide build singularity containers ready to use and recommend them for the R part.

Note

TIP !! We provide a singularity container for R packages (Singularity.report.def), you can use this one to create a module environment.

Warning

Make sure to specify the –use-envmodules flag in the snakemake command line for ENVMODULE to be implemented. More details can be found here: https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html#using-environment-modules

And more …

How to build singularity images

You can build your own image using the available .def recipes from the culebrONT/culebrONT/containers/ directory.

Warning

Be careful, you need root access to build singularity images

cd culebrONT/culebrONT/containers/
sudo make build

Threading rules inside culebrONT

Please find here the rules names found in CulebrONT code. It is recommended to set threads using the snakemake command when running on a single machine or in the cluster configuration file to manage cluster resources via the job scheduler. This would save the user a painful exploration of the snakefiles code of CulebrONT.

run_flye
run_canu
run_minimap_for_miniasm
run_miniasm
run_minipolish
run_raven
convert_fastq_to_fasta
run_smartdenovo
run_shasta
run_circlator
tag_circular
tag_circular_to_minipolish
rotate_circular
run_fixstart
run_makerange
run_nanopolish_index
preparing_ref_to_nanopolish
run_nanopolish_variants
run_nanopolish_merge
index_fasta_to_correction
run_minialign_to_medaka
run_medaka_train
run_medaka_consensus
run_medaka_merge
run_pilon_first_round
run_pilon
run_racon
preparing_fasta_to_quality
run_quast
run_busco
run_diamond
run_minimap2
run_blobtools
run_mummer
run_assemblytics
combined_fastq
run_KAT
run_mauve
run_bwa_mem2
run_flagstat
final
rule_graph
run_report_snakemake
run_flagstats_stats
run_busco_stats
run_racon_version
run_busco_version
run_benchmark_time
run_get_versions
stats_assembly
run_report