recipe fjord

ONT amplicon sequencing pipeline for bacterial identification

Homepage:

https://github.com/adnsvrtsn/fjord

License:

MIT / MIT

Recipe:

/fjord/meta.yaml

FJORD (Flexible Joint Operational pipeline for Reference-based Diagnostics) is an amplicon sequencing pipeline for bacterial identification from Oxford Nanopore Technologies long-read data. It maps reads to a GTDB-formatted reference database, generates consensus sequences, clusters similar sequences with IUPAC-aware consolidation, and assigns taxonomy via BLAST.

package fjord

(downloads) docker_fjord

Versions:

1.0.0-0

Depends:
  • on bash >=3.2

  • on biopython

  • on blast

  • on python >=3.8

  • on samtools >=1.15

  • on vsearch

  • on x-mapper

Additional platforms:

Installation

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install fjord

to add into an existing workspace instead, run:

pixi add fjord

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install fjord

Alternatively, to install into a new environment, run:

conda create -n envname fjord

with envname being the name of the desired environment.

Container

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/fjord:<tag>

(see fjord/tags for valid values for <tag>).

Integrated deployment

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats