rdrpcatch 0.0.7

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RdRpCATCH

RNA-dependent RNA polymerase Collaborative Analysis Tool with Collections of pHMMs

RdRpCATCH is collaborative effort to combine various publicly available RNA virus RNA-dependent RNA polymerase pHMM databases in one tool to facilitate their detection in (meta-)transcriptomics data.

RdRpCATCH is written in Python and uses the pyHMMER3 library to perform pHMM searches. In addition, the tool scans each sequence (aa or nt) in the input file with the selected databases and provides the best hit (hit with the highest bitscore across all databases) as output. In addition, RdRpCATCH provides information about the number of profiles that were positive for each sequence across all pHMM databases, and taxonomic information based on the MMseqs2 easy-taxonomy and search modules against a custom RefSeq Riboviria database.

** The tool has been modified to use rolypoly code/approaches **

Supported databases

• NeoRdRp ¹ : 1182 pHMMs
• NeoRdRp2 ²: 19394 pHMMs
• RVMT ³: 710 pHMMs
• RdRp-Scan ⁴ : 68 pHMMs
• TSA_Oleandrite_fam ⁵: 77 pHMMs
• TSA_Oleandrite_gen ⁶ : 341 pHMMs
• LucaProt_pHMM⁷ : 754 pHMMs

1. Sakaguchi, S. et al. (2022) 'NeoRdRp: A comprehensive dataset for identifying RNA-dependent RNA polymerases of various RNA viruses from metatranscriptomic data', Microbes and Environments, 37(3). doi:10.1264/jsme2.me22001
2. Sakaguchi, S., Nakano, T. and Nakagawa, S. (2024) 'Neordrp2 with improved seed data, annotations, and scoring', Frontiers in Virology, 4. doi:10.3389/fviro.2024.1378695
3. Neri, U. et al. (2022) 'Expansion of the global RNA virome reveals diverse clades of bacteriophages', Cell, 185(21). doi:10.1016/j.cell.2022.08.023
4. Charon, J. et al. (2022) 'RDRP-Scan: A bioinformatic resource to identify and annotate divergent RNA viruses in metagenomic sequence data', Virus Evolution, 8(2). doi:10.1093/ve/veac082
5. Olendraite, I., Brown, K. and Firth, A.E. (2023) 'Identification of RNA virus–derived rdrp sequences in publicly available transcriptomic data sets', Molecular Biology and Evolution, 40(4). doi:10.1093/molbev/msad060
6. Olendraite, I. (2021) 'Mining diverse and novel RNA viruses in transcriptomic datasets', Apollo. Available at: https://www.repository.cam.ac.uk/items/1fabebd2-429b-45c9-b6eb-41d27d0a90c2
7. Hou, X. et al. (2024) 'Using artificial intelligence to document the hidden RNA virosphere', Cell, 187(24). doi:10.1016/j.cell.2024.09.027

Installation

Prerequisites

For the installation process, conda is required. If you don't have conda installed, you can find instructions on how to https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html
Mamba is a faster alternative to conda. If you have it installed, you can use it instead of conda.

Installation steps

The package is available as a bioconda package. You can install it using the following command:

conda env create rdrpcatch -c bioconda rdrpcatch

Alternatively, you can install RdRpCATCH from python package index (PyPI) using pip. This requires the installation of the dependencies manually. The dependencies are:

• mmseqs2
• seqkit

The dependencies can be installed using conda or mamba. Follow these steps:

Create a new conda environment and install the dependencies:

conda env create -n rdrpcatch python=3.12
conda activate rdrpcatch
conda install -c bioconda mmseqs2==17.b804f seqkit==2.10.0

Install the tool from pip:

pip install rdrpcatch

Activate the environment and download the RdRpCATCH databases:

conda activate rdrpcatch
rdrpcatch download --destination_dir path/to/store/databases

• Note 1: The databases are large files and may take some time to download (~ 3 GB).
• Note 2: The databases are stored in the specified directory, and the path is required to run RdRpCATCH.
• Note 3: If you encounter an SSL error while downloading, please try again. The error seems to appear sporadically during testing, and a simple re-initiation of the downloading process seems to fix it.

Usage

RdRpCATCH can be used as a CLI tool as follows:

# make sure the conda environment is activated
# conda activate rdrpcatch

# scan the input fasta file with the selected databases
rdrpcatch scan -i path/to/input.fasta -o path/to/output_dir -db_dir path/to/database

input:

The input file can be one or more nucleotide or protein sequences in multi-fasta format. The output directory is where the results will be stored. We recommend specifying the type of the sequence in the command line, An optional argument --seq_type (nuc or prot) can be used to specify if the input fasta file sequences are nucleotide or amino acid.

Commands

The following two commands are available in RdRpCATCH:

• rdrpcatch scan
• rdrpcatch download

rdrpcatch download:

Command to download pre-compiled databases from Zenodo. If the databases are already downloaded in the specified directory , the command will check for updates and download the latest version if available.

Argument	Short Flag	Type	Description
--destination_dir	-dest	PATH	Path to the directory to download HMM databases. [required]
--concept-doi	``	TEXT	Zenodo Concept DOI for database repository
--help	``		Show help message and exit

rdrpcatch scan:

Search a given input using selected RdRp databases.

Argument	Short Flag	Type	Description
--input	-i	FILE	Path to the input FASTA file. [required]
--output	-o	DIRECTORY	Path to the output directory. [required]
--db_dir	-db_dir	PATH	Path to the directory containing RdRpCATCH databases. [required]
--db_options	-dbs	TEXT	Comma-separated list of databases to search against. Valid options: RVMT, NeoRdRp, NeoRdRp.2.1, TSA_Olendraite_fam, TSA_Olendraite_gen, RDRP-scan, Lucaprot_HMM,Zayed_HMM, all
--custom-dbs		PATH	Path to directory containing custom MSAs/pHMM files to use as additional databases
--seq_type	-seq_type	TEXT	Type of sequence to search against: (prot,nuc) Default: unknown
--verbose	-v	FLAG	Print verbose output.
--evalue	-e	FLOAT	E-value threshold for HMMsearch. (default: 1e-5)
--incevalue	-incE	FLOAT	Inclusion E-value threshold for HMMsearch. (default: 1e-5)
--domevalue	-domE	FLOAT	Domain E-value threshold for HMMsearch. (default: 1e-5)
--incdomevalue	-incdomE	FLOAT	Inclusion domain E-value threshold for HMMsearch. (default: 1e-5)
--zvalue	-z	INTEGER	Number of sequences to search against. (default: 1000000)
--cpus	-cpus	INTEGER	Number of CPUs to use for HMMsearch. (default: 1)
--length_thr	-length_thr	INTEGER	Minimum length threshold for seqkit seq. (default: 400)
--gen_code	-gen_code	INTEGER	Genetic code to use for translation. (default: 1)
--bundle	-bundle		Bundle the output files into a single archive. (default: False)
--keep_tmp	-keep_tmp		Keep the temporary files generated during the analysis. (default: False)

Output files

rdrpcatch scan will create a folder with the following structure:

Output	Description
{prefix}_rdrpcatch_output_annotated.tsv	A tab-separated file containing the results of the RdRpCATCH analysis.
{prefix}_rdrpcatch_fasta	A directory containing the sequences that were identified as RdRp sequences.
{prefix}_rdrpcatch_plots	A directory containing the plots generated during the analysis.
{prefix}_gff_files	A directory containing the GFF files generated during the analysis. (For now only based on protein sequences)
tmp	A directory containing temporary files generated during the analysis. (Only available if the -keep_tmp flag is used )

Output table fields

A summary of the results is stored in the {prefix}_rdrpcatch_output_annotated.tsv file, which contains the following fields:

Field	Description
Contig_name	The name of the contig.
Translated_contig_name (frame)	The name of the translated contig and the frame of the RdRp sequence.
Sequence_length(AA)	The length of the RdRp sequence in amino acids.
Total_databases_that_the_contig_was_detected(No_of_Profiles)	The name of databases and the number of profiles that the RdRp sequence was detected by.
Best_hit_Database	The database with the best hit.
Best_hit_profile_name	The name of the profile with the best hit.
Best_hit_profile_length	The length of the profile with the best hit.
Best_hit_e-value	The e-value of the best hit.
Best_hit_bitscore	The bitscore of the best hit.
RdRp_from(AA)	The start position of the RdRp sequence, in relation to the amino acid sequence.
RdRp_to(AA)	The end position of the RdRp sequence, in relation to the amino acid sequence.
Best_hit_profile_coverage	The fraction of the profile that was covered by the RdRp sequence.
Best_hit_contig_coverage	The fraction of the contig that was covered by the RdRp sequence. (Based on aminoacid sequence)
MMseqs_Taxonomy_2bLCA	The taxonomy of the RdRp sequence based on MMseqs2 easy-taxonomy module against a custom RefSeq Riboviria database.
MMseqs_TopHit_accession	The accession of the top hit in the RefSeq Riboviria database.
MMseqs_TopHit_fident	The fraction of identical matches of the top hit in the RefSeq Riboviria database.
MMseqs_TopHit_alnlen	The alignment length of the top hit in the RefSeq Riboviria database.
MMseqs_TopHit_eval	The e-value of the top hit in the RefSeq Riboviria database.
MMseqs_TopHit_bitscore	The bitscore of the top hit in the RefSeq Riboviria database.
MMseqs_TopHit_qcov	The query coverage of the top hit in the RefSeq Riboviria database.
MMseqs_TopHit_lineage	The lineage of the top hit in the RefSeq Riboviria database.

Citations

Manuscript still in preparation. If you use RdRpCATCH, please cite this GitHub repository A precompiled version of the used databases is available at Zenodo DOI: 10.5281/zenodo.14358348.
If you use RdRpCATCH, please cite the underlying third party databases :

Acknowledgements

RdRpCATCH is a collaborative effort and we would like to thank all the authors and developers of the underlying databases.

Contact

Dimitris Karapliafis ([e-mail]), potentially via slack/teams or an issue in the main repo.

##TODO:

• loud logging is linking to the utils.py file, not the actual line of code causing the error.
• drop db_dir argument and use global/environment/config variable that is set after running the download command

Contributing

TBD up to Dimitris and Anne

Licence

MIT