Resistify 🍃

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Resistify is a program which rapidly identifies and classifies plant resistance genes from protein sequences. It is designed to be lightweight and easy to use.

Getting started

Resistify is available via the Bioconda channel:

conda create -n resistify bioconda::resistify
conda activate resistify

Note

If you want to use the GPU-accelerated pipelines, conda may fail to install a GPU-ready version of pytorch. If this occurs, try installing pytorch-gpu bioconda::resistify instead.

When using conda, please ensure that your Bioconda has been configured correctly.

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>

If you are having issues with conda, you can instead try installing directly from the repository:

pip install https://github.com/SwiftSeal/resistify/archive/refs/tags/v0.6.2.tar.gz

Note that resistify requires hmmer to be installed and available in your system's PATH, which will not be installed automatically when using pip.

Usage

Identifying NLRs

To predict NLRs within a set of protein sequences, simply run:

resistify nlr <input.fa>

and Resistify will identify and classify NLRs, and return some files:

• 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.

By default, Resistify will only return sequences with NB-ARC domains. If you wish to identify highly fragmented NLRs, you can use the --retain option which will predict and report NLR-associated motifs for all sequences. It'll be a bit slower!

If you want to increase the sensitivity of coiled-coil domain annotation, you can use the option --coconat. This will use CoCoNat to predict coiled-coil domains. In practice, I wouldn't expect this mode to pick up on a significant number of missed CC domains, but it can pick up on cryptic CCs that do not have an identifiable EDVID motif.

How does it work?

Resistify carries out an initial search for common NLR domains to quickly filter and annotate the input sequences. Then, Resistify executes a re-implementation of NLRexpress to conduct a highly accurate search for NLR-associated motifs. If --coconat is used, this will also be executed to scavenge for potentially missed coiled-coil domains. Together, this evidence is used to classify NLRs according to their domain architecture.

Identifying PRRs

Important

This pipeline is currently in development - due to other commitments I can't currently benchmark this properly and make no guarantees to its accuracy yet! Feedback is appreciated.

To predict PRRs within a set of protein sequences, simply run:

resistify prr <input.fa>

and Resistify will identify and classify PRRs, and return some files:

• results.tsv - A table containing the primary results of Resistify.
• motifs.tsv - A table of all the LRR 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.
• prr.fasta - A fasta file of all PRRs identified.

Warning

This pipeline is GPU-accelerated and will be extremely slow on CPU only.

How does it work?

First, Resistify searches for domains associated with a recently described classification system for RLP/RLKs. Then, a re-implementation of TMbed is used to predict transmembrane domains - sequences with a single α-helix transmembrane domain and an extracellular domain of at least 50 amino acids are considered as RLPs. Finally, NLRexpress is used to to identify LRR domains.

Sequences are classified as being either RLPs or RLKs depending on the presence of an internal kinase domain, and are classified according to their extracellular domain.

Downloading model data

Note

This only applies to the --coconat and PRR pipelines! The standard NLR pipeline does not require any external databases.

By default, Resistify will automatically download the models required for CoConat and TMbed to your $HOME/.cache directory. If you'd like to manually install the databases instead, you can use the resistify download_models utility to download these to a directory of your choice. To provide these local models to the CoCoNat and TMbed processes, simply pass the path of the models directory via the --models argument. Approximately 13G of disk space is required.

Results

results.tsv (nlr)

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.

results.tsv (prr)

Sequence	Length	Type	Classification	Signal_peptide
fls2	1174	RLK	LRR	True

For PRRs, sequences can be of the type RLP or RLK - both are single pass transmembrane proteins, and RLKs have an internal kinase domain. Classification refers to the domains identified in the external region. If multiple domains are identified, they will each be reported as a semi-colon separated list. If a signal peptide is identified in the sequence, this is reported accordingly.

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. In PRR mode, only LRR motifs will be reported.

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

Sometimes, 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.

Frequently asked questions

Q: Can Resistify be used to predict resistance genes from genomic data?

A: Unfortunately, Resistify cannot be directly applied to a genome to predict resistance genes, unlike tools such as NLR-Annotator. If gene annotations are unavailable for your genome, my advice would be to use a tool like Helixer to perform ab initio gene prediction.

Q: According to the Motif string, some of my genes have NLR motifs in unexpected places - are these significant?

A: False positives do occur for the motif predictions, and unexpected predictions such as a single CC motif in the LRR domain are unlikely to be representative of a true domain annotation. You can find a figure of the prediction accuracy rates for each predictor here. False positives shouldn't interfere with the classification accuracy.

Q: The NLRexpress step is quite slow - how can I speed it up?

A: More threads! The process is relatively fast on a non-NLR sequence, but can be quite slow when applied to an NLR. Resistify will automatically use as many threads as possible - I've used up to 128 threads and it scales fairly well. It's primarily due to the underlying jackhmmer process, which is slow when applied to NLRs, but not non-NLRs. As a result the --retain option doesn't have as much of a performance impact as you might expect.

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

Smith, M., Jones, J. T., & Hein, I. (2024). Resistify - A rapid and accurate annotation tool to identify NLRs and study their genomic organisation. bioRxiv, 2024-02. https://doi.org/10.1101/2024.02.14.580321

You must also cite:

Martin, E. C., Spiridon, L., Goverse, A., & Petrescu, A. J. (2022). NLRexpress—A bundle of machine learning motif predictors—Reveals motif stability underlying plant Nod-like receptors diversity. Frontiers in Plant Science, 13, 975888. https://doi.org/10.3389/fpls.2022.975888

If you use the CoCoNat module, please cite:

Madeo, G., Savojardo, C., Manfredi, M., Martelli, P. L., & Casadio, R. (2023). CoCoNat: a novel method based on deep learning for coiled-coil prediction. Bioinformatics, 39(8), btad495. https://doi.org/10.1093/bioinformatics/btad495

If you use the PRR module, please cite:

Bernhofer, M., & Rost, B. (2022). TMbed: transmembrane proteins predicted through language model embeddings. BMC bioinformatics, 23(1), 326. https://doi.org/10.1186/s12859-022-04873-x