resistify 0.1.1

A resistance gene annotation tool

Resistify

Resistify is a program which classifies plant NLRs by their protein domain and motif architecture. It is designed to be lightweight - no manual database installations or tricky dependencies here!

Installation

Resistify is available on Conda. To get started with Resistify, simply run:

conda install resistify

Resistify requires biopython and scikit-learn==0.24.2. It also requires hmmsearch and jackhmmer - install these via conda or any other means. I'd recommend creating an environment for it specifically, as scikit-learn dependencies are a bit busted...

Alternatively, you can use:

conda create -n resistify python==3.9 pip hmmer
pip install resistify

Usage

To run Resistify:

resistify <input.fa> <output_directory>

Your input.fa should contain the amino acid sequences of your proteins of interest. Multiline and sequence description fields are allowed. Stop codons "*" are permitted at the end of sequences - internal stop codons are not.

An output_directory will be created which will contain the results of your run:

• results.tsv - A table of the length, classification, count of NB-ARC motifs, as well as the presence of any MADA motif or CJID domain
• motifs.tsv - A table of all the NLRexpress motifs for each sequence
• domains.tsv - A table of all the domains identified for each sequence
• nbarc.fasta - A fasta file of all the NB-ARC domains identified.

How does it work?

Resistify is a two step process.

First, all sequences are searched for CC, RPW8, TIR, and NB-ARC domains. This is used to quickly filter out any non-NLR sequences and identify the primary architecture of each NLR.

Secondly, each potential NLR sequence is scanned for CC, TIR, NB-ARC, and LRR associated motifs via NLRexpress. These are used as an additional layer of evidence to reclassify each NLR by predicting LRR domains, and predicting any CC or TIR domains which may have been missed in the initial hmmsearch.

Resistify will also search for N-terminal MADA motifs and CJID domains that are common to CNLs and TNLs respectively.

A note on run time

Version 0.1.1 has introduced multithreading 🎉 - use the --threads argument to get started.

The run time of resistify scales linearly with the total number of NLRs present in the input sequence file. A file with 200 NLRs will take approximately twice as long as a file with 100 NLRs. This does not apply to the total number of sequences - an input of 50,000 sequences with 100 NLRs will run just as fast as an input of 1,000 sequences with 100 NLRs.

Here's an example of the results.tsv for ZAR1:

Sequence	Length	Motifs	Domains	Classification	NBARC_motifs	MADA	MADAL	CJID
ZAR1	852	CNNNNNNNNNLLLLLLLLLL	CNL	CNL	9	False	True	False

Contributing

Contributions are greatly appreciated! If you experience any issues running Resistify, please get in touch via the Issues page.

Citing

Resistify - A rapid and accurate annotation tool to identify NLRs and study their genomic organisation
Moray Smith, John T. Jones, Ingo Hein
bioRxiv 2024.02.14.580321; doi: https://doi.org/10.1101/2024.02.14.580321

You must also cite:

NLRexpress—A bundle of machine learning motif predictors—Reveals motif stability underlying plant Nod-like receptors diversity
Martin Eliza C. , Spiridon Laurentiu , Goverse Aska , Petrescu Andrei-José
Frontiers in Plant Science 2022; doi: https://doi.org/10.3389/fpls.2022.975888