resistify 0.4.0

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

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:0.2.2--pyhdfd78af_0

Alternatively, Resistify is also available on PyPi:

pip install resistify

Please note that Resistify requires hmmer as a dependency which will need to be installed if using pip.

Usage

To get started with Resistify:

resistify <input.fa> <output_directory>

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.
• nbarc.fasta - A fasta file of all the NB-ARC domains identified.
• nlrs.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
NP_190664.1	852	CNNNNNNNNNLLLLLLLLLL	CNL	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
NP_190664.1	extEDVID	65	0.9974	LVADL	RELVYEAEDILV	DCQLA
NP_190664.1	VG	159	0.9924	YDHTQ	VVGLE	GDKRK
NP_190664.1	P-loop	188	1.0	IMAFV	GMGGLGKTT	IAQEV
NP_190664.1	RNSB-A	211	0.9981	EIEHR	FERRIWVSVS	QTFTE
NP_190664.1	Walker-B	259	0.973	QYLLG	KRYLIVMD	DVWDK
NP_190664.1	RNSB-B	290	0.9846	RGQGG	SVIVTTR	SESVA
NP_190664.1	RNSB-C	317	0.9994	HRPEL	LSPDNSWLLF	CNVAF
NP_190664.1	RNSB-D	417	0.9875	SHLKS	CILTLSLYP	EDCVI
NP_190664.1	GLPL	356	0.9998	VTKCK	GLPLT	IKAVG
NP_190664.1	MHD	486	0.9965	IITCK	IHD	MVRDL
NP_190664.1	LxxLxL	511	0.9398	PEGLN	CRHLGI	SGNFD
NP_190664.1	LxxLxL	560	0.9973	TDCKY	LRVLDI	SKSIF
NP_190664.1	LxxLxL	587	0.9993	ASLQH	LACLSL	SNTHP
NP_190664.1	LxxLxL	611	0.9995	EDLHN	LQILDA	SYCQN
NP_190664.1	LxxLxL	635	0.999	VLFKK	LLVLDM	TNCGS
NP_190664.1	LxxLxL	685	0.9987	KNLTN	LRKLGL	SLTRG
NP_190664.1	LxxLxL	712	0.9723	INLSK	LMSISI	NCYDS
NP_190664.1	LxxLxL	740	0.9995	TPPHQ	LHELSL	QFYPG
NP_190664.1	LxxLxL	765	0.9976	HKLPM	LRYMSI	CSGNL
NP_190664.1	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	E_value
NP_190664.1	MADA	0	21	5e-07
NP_190664.1	CC	4	128	7.8e-24
NP_190664.1	NB-ARC	162	410	4.6e-90
NP_190664.1	LRR	511	817	NA

This file contains the coordinates of NLR domains identified by Resistify. Not that the LRR domain does not have an E-value - this is because it is determined via LRR motifs rather than HMM hits. I'd treat this file with a bit of caution - in some cases the CC domain will correspond solely to the position of the CC motif rather than the coordinates of a Pfam hit.

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.

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.

How does it work?

Resistify uses 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