resistify 0.2.1

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:

conda install -c bioconda resistify

Resistify is also available on PyPi:

pip install resistify

Resistify requires hmmer as a dependency.

Usage

To get started with Resistify:

resistify <input.fa> <output_directory>

Your input.fa should contain your protein sequences of interest. An output_directory will be created which will contain the results of your run:

• results.tsv - A table containing the primary results of Resistify.
• motifs.tsv - A table of all the NLR motifs identified 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.

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

The main column of interest is "Classification". The "Motifs" column indicates the series of NLR-associated motifs identified across the sequence. This can be useful for interpreting non-canonical NLRs!

Ultra mode

By default Resistify will perform an initial filter to remove non-NLRs prior to motif identification. Highly degraded or non-canonical NLRs may not be reported. If you wish to retain these, simply use --ultra mode to skip this step.

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.

Contributing

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

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