${\color{black}PAP:\ {\color{red}P}arallel\ {\color{red}A}nnotation\ {\color{red}P}ipeline}$

Parallel Annotation Pipeline was developed using the Snakemake workflow management system, providing a streamlined and automated approach to annotating protein sequences, enabling researchers to rapidly and accurately characterize large-scale proteomic data sets. PAP integrates seamlessly with a range of existing bioinformatics tools and databases, allowing you to easily transfer annotations from multiple sources and combine them into a single, comprehensive annotation set. Additionally, PAP version for HPC cluster and its highly parallelized architecture make it the ideal tool for researchers with this type of computing resources looking to annotate protein sequences.

Dependencies:

blastp (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download)

diamond (https://github.com/bbuchfink/diamond)

hmmscan (http://hmmer.org/)

Perl (https://www.perl.org/get.html) (v5.30.0)

Databases

Installation:

Option 1

PAP pipeline it is written in Snakemake and Perl. For greater convenience/ease of installing PAP, we use the Apptainer/Singularity container platform and build an image with the complete environment (script and dependencies) needed to run PAP.

You just need to download the Singularity image PAP and have installed "Apptainer/Singularity". If you don't have it installed, you can install it:

with Conda

conda install -c conda-forge singularity

Alternatively, x86_64 RPMs are available on GitHub immediately after each Apptainer release and they can be installed directly from there:

with RPMs

sudo yum install -y https://github.com/apptainer/apptainer/releases/download/v1.1.3/apptainer-1.1.3-1.x86_64.rpm

with DEB

wget https://github.com/apptainer/apptainer/releases/download/v1.1.3/apptainer_1.1.3_amd64.deb

sudo apt-get install -y ./apptainer_1.1.3_amd64.deb

For more details of the Apptainer installation process, go here.

Option 2

Make sure you have all dependencies and databases properly installed. You also need to download and have all the 'bin' scripts in your path.

You can check Snakemake on their site for more details of this.

Quick usage: (Install Option 1)

PAP <protein.fasta>

notes:

1- You need to put "PAP" in your path, otherwise you must give the whole path so that it can be found.

2- The input [fasta](https://en.wikipedia.org/wiki/FASTA_format) file must exist in your $HOME, otherwise you need to set the environment variable SINGULARITY_BIND
to bind paths where your sequences are located
ex: export SINGULARITY_BIND="../path/for/the/input/fasta"

Quick usage: (Install Option 2)

For "protein.faa" file name run:

snakemake --cores <thread_numbers> -s /path/of/Snakefile

If protein fasta files have other names, then run:

snakemake --cores <thread_numbers> --config PROTREF="current_protein_fasta_filename" -s /path/of/Snakefile

PROTREF= "protein.faa" # Fasta file of the reference proteins that we want to transfer or annotate in our genome. Default: "protein.faa"

Output files

A file in tsv format with the annotation of the proteins.

File "annotation_table.tbl"

Citation

Estrada K, Verleyen J. PAP:Parallel Annotation Pipeline. 2021. [Computer software] https://doi.org/10.5281/zenodo.7958138

Authors

Dr. Karel Estrada; M.C Jerome Verleyen

[email protected]

Twitter: @kjestradag

Acknowledgments

PAP wouldn't be the same without advice and suggestions from Alejandro Sánchez.

PAP uses Snakemake for pipeline development, Blastp and HMMER to perform alignments and SignalP for signal peptide prediction. Additionally, PAP takes information from other databases such as GO and KEGG and incorporates it into the final annotation report.