Requirements

CulebrONT requires Python 3.7+, graphviz 2.40.1+ and R 4.0+.

CulebrONT is developed to work mostly on an HPC distributed cluster but a local, single machine, installation is also possible.

Install CulebrONT PyPI package

First, install the 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 (single machine) 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 by the configuration files of the pipeline. Local mode install, without scheduler, is constrains to use these Singularity images.

Warning

Singularity images are downloaded in the location of the package CulebrONT. Be careful these images need at approximatly 4G of free space. If installed with Pypi with the flag –user (without root), the package is installed in your HOME.

Optionally (but recommended), after installing in local, you can check the CulebrONT installation using a dataset scaled for single machine. See the section Check install for details.

Steps for HPC distributed cluster installation

CulebrONT uses any available snakemake profiles to ease cluster installation and resources management. Run the command culebrONT install_cluster to install on a HPC cluster. We tried to make cluster installation as easy as possible, but it is somehow necessary to adapt a few files according to your cluster 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

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

1. Adapt the pre-formatted f –env si`snakemake profile` to configure your 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 the two following 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 specified, CulebrONT will download previously build Singularity images, containing the complete environment need to run CulebrONT (tools and dependencies).

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

Check install

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

culebrONT test_install

Test_install function 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 will be automatically downloaded by CulebrONT in the -d repertory using :

culebrONT test_install -d test

Launching the (suggested, to be adapted) command line in CLUSTER mode will perform the tests:

culebrONT run_cluster --config test/data_test_config.yaml

In local mode, type :

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 take advantages of profiles.

Quickly, see here an example of the Snakemake SLURM profile we used 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 the number of jobs submitted in parallel
latency-wait: 60000000
use-envmodules: true        # adapt True/False for env of singularuty, but only active one possibility !
use-singularity: false      # if False, please install all R packages listed in 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

Note

About versions of tools, the user can choose themself what version of tools to use with modules or with singularity. HOWEVER, the pipeline was validated with specific versions (check the singularity def) so it may leads to error due to parameter changes. Assembly

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 built Singularity images you want to use. Absolute paths are strongly recommended. 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

To ensure SINGULARITY containers to be really 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 the modules available on your cluster (here is an example):

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

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 all R packages (Singularity.report.def), thus you can use this one to create a dedicated 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 a cluster configuration file to manage cluster resources through the job scheduler. This would save users a painful exploration of the snakefiles 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_medaka_oneshot
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