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!

*Optional database not included!

Installation

Resistify is available on Conda:

conda install -c bioconda resistify

Docker/Podman containers are also available through the biocontainers repository. To use these with - for example - singularity, simply run:

singularity exec docker://quay.io/biocontainers/resistify:<tag-goes-here>

Usage

To get started with Resistify:

resistify <input.fa> <output_directory>

CoCoNat-based CC annotation (EXPERIMENTAL)

Version 0.5.0 has introduced an optional module that will use CoCoNat to improve the identification of CC domains. To use this feature, you will first need to download their databases:

wget https://coconat.biocomp.unibo.it/static/data/coconat-plms.tar.gz
tar xvzf coconat-plms.tar.gz

Then, simply provide a path to the database folder with the argument --coconat. CoCoNat will then be used to improve the annotation of coiled-coil domains in your input.

This uses a stripped down version of CoCoNat - currently it will only identify CC domain boundaries and not predict residue-level registers or oligomerization states.

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. You can use this to identify any NLR-associated motifs in a dataset.

Results

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.
• annotations.tsv - A table of the raw annotations for each sequence.
• nbarc.fasta - A fasta file of all the NB-ARC domains identified.
• nlr.fasta - A fasta file of all NLRs identified.

As an example, let's look at the results of a Resistify run against the NLR ZAR1.

results.tsv

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

The main column of interest is "Classification", where we can see that it has been identified as a canonical CNL. The "Motifs" column indicates the series of NLR-associated motifs identified across the sequence - this can be useful if an NLR has an undetermined or unexpected classification. The columns "MADA", "MADAL", and "CJID" correspond to common NLR sequence signatures. Here, it appears that ZAR1 has a MADA-like motif.

motifs.tsv

Sequence	Motif	Position	Probability	Downstream_sequence	Motif_sequence	Upstream_sequence
ZAR1	extEDVID	65	0.9974	LVADL	RELVYEAEDILV	DCQLA
ZAR1	VG	159	0.9924	YDHTQ	VVGLE	GDKRK
ZAR1	P-loop	188	1.0	IMAFV	GMGGLGKTT	IAQEV
ZAR1	RNSB-A	211	0.9981	EIEHR	FERRIWVSVS	QTFTE
ZAR1	Walker-B	259	0.973	QYLLG	KRYLIVMD	DVWDK
ZAR1	RNSB-B	290	0.9846	RGQGG	SVIVTTR	SESVA
ZAR1	RNSB-C	317	0.9994	HRPEL	LSPDNSWLLF	CNVAF
ZAR1	RNSB-D	417	0.9875	SHLKS	CILTLSLYP	EDCVI
ZAR1	GLPL	356	0.9998	VTKCK	GLPLT	IKAVG
ZAR1	MHD	486	0.9965	IITCK	IHD	MVRDL
ZAR1	LxxLxL	511	0.9398	PEGLN	CRHLGI	SGNFD
ZAR1	LxxLxL	560	0.9973	TDCKY	LRVLDI	SKSIF
ZAR1	LxxLxL	587	0.9993	ASLQH	LACLSL	SNTHP
ZAR1	LxxLxL	611	0.9995	EDLHN	LQILDA	SYCQN
ZAR1	LxxLxL	635	0.999	VLFKK	LLVLDM	TNCGS
ZAR1	LxxLxL	685	0.9987	KNLTN	LRKLGL	SLTRG
ZAR1	LxxLxL	712	0.9723	INLSK	LMSISI	NCYDS
ZAR1	LxxLxL	740	0.9995	TPPHQ	LHELSL	QFYPG
ZAR1	LxxLxL	765	0.9976	HKLPM	LRYMSI	CSGNL
ZAR1	LxxLxL	817	0.9391	QSMPY	LRTVTA	NWCPE

Here, the positions, probabilities, and sequence of NLRexpress motif hits are listed. The five amino acids upstream and downstream of the motif site are also provided.

domains.tsv

Sequence	Domain	Start	End
ZAR1	MADA	0	21
ZAR1	CC	4	129
ZAR1	NB-ARC	162	410
ZAR1	LRR	511	817

This file contains the coordinates of the domains identified by Resistify.

annotations.tsv

Sequence	Domain	Start	End	E_value	Score	Source
ZAR1	MADA	0	21	1.5e-06	16.2	HMM
ZAR1	CC	4	128	2.3e-23	70.0	HMM
ZAR1	CC	27	48	NA	NA	Coconat
ZAR1	CC	60	75	NA	NA	Coconat
ZAR1	CC	113	129	NA	NA	Coconat
ZAR1	NB-ARC	162	410	1.4e-89	287.2	HMM
ZAR1	LRR	511	817	NA	NA	NLRexpress

This file contains the raw annotations for each sequence, and the method which was used to identify them.

Output visualisation

I've kept the output files of Resistify fairly minimal so that users can carry out their own analysis/visualisation. Here are some examples of how Resistify can be used to create basic plots.

Phylogenetics

Resistify extracts the NB-ARC domains of each hit so we can easily build a phylogenetic tree. Here, we create a tree rooted on the NB-ARC domain of CED-4. The mafft | fastree method is used here for brevity rather than accuracy.

echo -e ">ced4\nREYHVDRVIKKLDEMCDLDSFFLFLHGRAGSGKSVIASQALSKSDQLIGINYDSIVWLKDSGTAPKSTFDLFTDILLMLARVVSDTDDSHSITDFINRVLSRSEDDLLNFPSVEHVTSVVLKRMICNALIDRPNTLFVFDDVVQEETIRWAQELRLRCLVTTRDVEISNAASQTCEFIEVTSLEIDECYDFLEAYGMPMPVGEKEEDVLNKTIELSSGNPATLMMFFKSCEPKTFEKMAQLNNKLESRGLVGVECITPYSYKSLAMALQRCVEVLSDEDRSALAFAVVMPPGVDIPVKLWSCVIPVD" >> output/nbarc.fasta

mafft output/nbarc.fasta | fasttree > output/nbarc.tree

We can now plot the tree:

library(tidyverse)
library(ggtree)

tree <- read.tree("output/nbarc.tree")
tree <- treeio::root(tree, outgroup = "ced4")

results <- read_tsv("output/results.tsv") |>
  mutate(Sequence = paste0(Sequence, "_1"))

myplot <- ggtree(tree, layout = "circular") %<+% results

myplot <- myplot +
  geom_tippoint(aes(colour = Classification))

Domain plotting

Somtimes, it might be of interest to plot the distribution of domains and motifs across each NLR. Achieving this with Resistify is quite simple:

library(tidyverse)

motif_translation = c(
  "extEDVID" = "CC",
  "bA" = "TIR",
  "aA" = "TIR",
  "bC" = "TIR",
  "aC" = "TIR",
  "bDaD1" = "TIR",
  "aD3" = "TIR",
  "VG" = "NB-ARC",
  "P-loop" = "NB-ARC",
  "RNSB-A" = "NB-ARC",
  "Walker-B" = "NB-ARC",
  "RNSB-B" = "NB-ARC",
  "RNSB-C" = "NB-ARC",
  "RNSB-D" = "NB-ARC",
  "GLPL" = "NB-ARC",
  "MHD" = "NB-ARC",
  "LxxLxL" = "LRR"
)

domains <- read_tsv("output/domains.tsv")
results <- read_tsv("output/results.tsv")
motifs <- read_tsv("output/motifs.tsv") |>
  mutate(Domain = motif_translation[Motif])

myplot <- ggplot() +
  geom_segment(data = results, aes(y = Sequence, yend = Sequence, x = 0, xend = Length)) +
  geom_segment(data = domains, aes(y = Sequence, yend = Sequence, x = Start, xend = End, colour = Domain)) +
  geom_point(data = motifs, aes(y = Sequence, x = Position, colour = Domain))

Cute! NB: Some false-positive motif hits are evident in this example - it might be of interest to not plot them, or plot only LRR motifs which tend to be a bit more informative.

A note on run time

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

If you use the CoCoNat module, please cite:

CoCoNat: a novel method based on deep learning for coiled-coil prediction
Giovanni Madeo, Castrense Savojardo, Matteo Manfredi, Pier Luigi Martelli, Rita Casadio
Bioinformatics 2023; doi: https://doi.org/10.1093/bioinformatics/btad495