DOI: 10.5061/dryad.sxksn03d6
title: Deep-learning-based annotation of 230 superasterid genomes reveals a harmonized dataset of 91,366 NLRs

description: Plant nucleotide-binding leucine-rich repeat receptors (NLRs) are
 intracellular immune receptors crucial for pathogen recognition and immune
 responses. Despite their importance, NLRs are often challenging to
 annotate and frequently overlooked by standard annotation pipelines. To
 address the variability in NLR annotation accuracy across pipelines, we
 performed a harmonized de novo annotation of 230 high-quality superasterid
 genomes using the deep learning-based software Helixer (Holst et
 al. 2023), resulting in the annotation of 10,124,265 protein sequences.
 Additionally, we employed NLRtracker, which leverages
 InterProScan for domain identification, to detect NLR and NLR-associated
 sequences (Kourelis et al. 2021, Blum et al. 2025). Using the NLR
 definition from the RefPlantNLR dataset, we identified 91,366 NLRs, with
 counts ranging from 12 and 19 in the parasitic plants Cuscuta campestris
 and Orobanche coerulescens to 2,804 in Solanum tuberosum (potato). Beyond
 NLR annotation, we provide genome annotations, including proteomes, coding
 nucleotide sequences (CDS), and GFF files generated by Helixer. This
 dataset offers a valuable resource for standardized comparative genomics
 and evolutionary studies across superasterids.
description: Helixer v0.3.2 (Stiehler et al. 2020; Holst et al. 2023) was
 executed using Singularity for genome FASTA files with the option
 '--lineage land_plant', which applies the default model
 (land_plant_v0.3_a_0080.h5) for land plants. Coding DNA sequences (CDS)
 and protein FASTA files were extracted from the output GFF files using
 GffRead v0.12.7 (Pertea and Pertea 2020) with the '-x' and
 '-y' options, respectively. The extracted protein sequences were
 then analyzed using NLRtracker (Kourelis et al. 2021), which integrates
 InterProScan v5.65-97.0 (Jones et al. 2014). BUSCO
 scores were generated using BUSCO v5.5.0 with [-m protein
 --lineage_dataset viridiplantae_odb10] options (Manni et al.
 2021).
description: # Deep-learning-based annotation of 230 superasterid genomes reveals a
 harmonized dataset of 91,366 NLRs
 [https://doi.org/10.5061/dryad.sxksn03d6](https://doi.org/10.5061/dryad.sxksn03d6) ## Description of the data and file structure ## **Abstract** Plant nucleotide-binding leucine-rich repeat receptors (NLRs) are intracellular immune receptors crucial for pathogen recognition and immune responses. Despite their importance, NLRs are often challenging to annotate and frequently overlooked by standard annotation pipelines. To address the variability in NLR annotation accuracy across pipelines, we performed a harmonized de novo annotation of 230 high-quality superasterid genomes using the deep learning-based software *Helixer* (Holst et al. 2023), resulting in the annotation of 10,124,265 protein sequences. Additionally, we employed *NLRtracker*, which leverages InterProScan for domain identification, to detect NLR and NLR-associated sequences (Kourelis et al. 2021, Blum et al. 2025). Using the NLR definition from the RefPlantNLR dataset, we identified 91,366 NLRs, with counts ranging from 12 and 19 in the parasitic plants *Cuscuta campestris* and *Orobanche coerulescens* to 2,804 in *Solanum tuberosum* (potato). Beyond NLR annotation, we provide genome annotations, including proteomes, coding nucleotide sequences (CDS), and GFF files generated by Helixer. This dataset offers a valuable resource for standardized comparative genomics and evolutionary studies across superasterids. #### **Helixer output legend** Genome annotations are categorized according to the phylogenetic order, based on information from APG IV (The Angiosperm Phylogeny Group et al. 2016). Each order has a subdirectory containing genome assembly FASTA, GFF annotations, CDS FASTA, protein FASTA, and NLRtracker output files. Two files containing compiled proteomes and CDS FASTA files with source assembly tags are also provided. **NLRtracker output legend** In the NLRtracker folder for each genome, the following files are found: | **File extension** | **Description** | | :----------------------------- | :------------------------------------------------------------------------------ | | \* \_NLRtracker.tsv | NLRtracker overview output with gene status. | | \*\_NLR.lst | Identifier list of NLRs. | | \*\_NLR.gff3 | NLR annotation of motifs, domains, and regions in GFF3 format. | | \*\_NLR.fasta | NLR FASTA sequences. | | \*\_NLR-associated.lst | Identifier list of NLR associated genes. | | \*\_NLR-associated.gff3 | NLR associated genes annotation of motifs, domains, and regions in GFF3 format. | | \*\_NLR\_associated.fasta | NLR associated genes FASTA sequences. | | \*\_NBARC.fasta | NB-ARC domain FASTA sequences. | | \*\_NBARC\_deduplictated.fasta | Deduplicated NB-ARC domain FASTA sequences. | | \*\_iTOL.txt | Domain annotation file for iTOL. | | \*\_iTOL\_dedup.txt | Domain annotation file of the deduplicated sequences for iTOL. | | \*\_Domains.tsv | Full-length and domain sequence and metadata for all NLRtracker output. | | interpro\_result.gff | InterProScan output of the query proteome. |   Recommended decompressing method for NLRtracker output files: "tar -xzvf" #### **Supplementary Data** **Data S1.** Species list and metadata: * Assembly acession: Genome ID from the source database (NCBI: "GC*", *Genome warehouse: "GWH") * Assembly name * Assembly release date * Organism name * Organism taxonomic ID: NBCI taxonomic ID * Assembly statistics: * Assembly Scaffold N50 * Assembly Scaffold L50 * Assembly Contig N50 * Assembly Contig L50 * Taxonomic information: * Kingdom * Phylum * Class * Order * Family * Genus * Species * Subspecies (if present, otherwise NA) * Variety (if present, otherwise NA) **Data S2.** Per genome sequence number statistics table for proteomes, total NLR, and putative NLR types determined by NLRtracker, and proteome BUSCO scores. ## Code/software ### **Methods** Helixer v0.3.2 (Stiehler et al. 2020; Holst et al. 2023) was executed using Singularity for genome FASTA files with the option '--lineage land_plant', which applies the default model (land_plant_v0.3_a_0080.h5) for land plants. Coding DNA sequences (CDS) and protein FASTA files were extracted from the output GFF files using GffRead v0.12.7 (Pertea and Pertea 2020) with the '-x' and '-y' options, respectively. The extracted protein sequences were then analyzed using NLRtracker (Kourelis et al. 2021), which integrates InterProScan v5.65-97.0 (Jones et al. 2014). BUSCO scores were generated using BUSCO v5.5.0 with [-m protein --lineage_dataset viridiplantae_odb10] options (Manni et al. 2021). ## Access information Genome assemblies and metadata were derived from the following sources: * NCBI Datasets ([https://www.ncbi.nlm.nih.gov/datasets/](https://www.ncbi.nlm.nih.gov/datasets/)) * Genome Warehouse ([https://ngdc.cncb.ac.cn/gwh/](https://ngdc.cncb.ac.cn/gwh/))