GeneidX with masking step

In this repository we provide a pipeline for the annotation of any genome starting from any raw genome assembly.

The two main steps are an initial genome masking step that was adapted from the nf-core/genomeannotator pipeline, and then a gene prediction step using GeneidX.

Check our repository of GeneidX for more information on the method and a benchmarking with several species.

Stay tuned for an article with detailed descriptions and feel free to contact us if you are trying it and find any problem.

Running GeneidX with masking

Having defined the parameters and ensuring that Nextflow and a container technology.

This pipeline requires the uncompressed genome assembly and the taxid of the species to annotate.

nextflow run ferriolcalvet/geneidXmask -profile <docker/singularity>
                                        --assembly <GENOME>.fa
                                        --taxid <TAXID>
                                        --outdir <OUTPUT_directory>

or alternatively, clone the repository and then run it (highly recommended)

git clone https://github.com/FerriolCalvet/geneidXmask.git
cd geneidXmask
nextflow run main.nf -profile <docker/singularity>
                      --assembly <GENOME>.fa
                      --taxid <TAXID>
                      --outdir <OUTPUT_directory>

Before running GeneidXmask

1. Make sure ( Docker or Singularity ) and Nextflow are installed in your computer.

• Docker
• Singularity
• Nextflow

2. Define the input parameters:

• Uncompressed FASTA file with .fa termination.
• Taxid of the species to annotate or from the closest relative with a described taxid.
• A repeats file can be optionally provided to the program, otherwise these repeats will be automatically chosen.
• Also a set of proteins can be provided or otherwise they are automatically chosen.
• See GeneidX repository for more parameters that can be tuned.

Work in progress

• It is recommended to clone the repository and then run the pipeline from there.
• So far the output of the predictions is not stored in the path indicated when running the pipeline, but in output/species/{taxid of the species}.
• If you are running the pipeline multiple times, it is recommended that you define a directory for downloading the docker/singularity images to avoid having to download them multiple times. See singularity.cacheDir variable in nextflow.config.

Follow us on Twitter (@GuigoLab) for updates in the article describing our method.