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Highlights in BioScience      
ISSN: 2682-4043                                                       
 

 

 
Highlights in BioScience                                    DOI: 10.36462/H.BioSci.20192                                 August 2019 | Volume2 
http://highlightsin.org/index.php/bioscience/                                 

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Citation: Zaimah N. S. (2019) Plant 

resistance gene, SNP and genome 

annotation an overview.  Highlights in 

BioScience, Volume 2.Article ID 

20192, dio:10.36462/ H.BioSci.20192 

 

 

 

 

 

 

Received: June 19, 2019 

 

Accepted: July 13, 2019 

 

Published: August 12, 2019 

 

 

Copyright:© 2019 Zaimah .This is an 

open access article distributed under 

the terms of the Creative Commons 

Attribution License, which permits 

unrestricted use, distribution, and 

reproduction in any medium, provided 

the original author and source are 

credited. 

 

 

 

Data Availability Statement: All 

relevant data are within the paper 

 

 

Funding: The authors have no support 

or funding to report. 

 

 

 

Plant Resistance Gene, SNP and Genome 

Annotation an Overview 

 
Nor S. Zaimah 

Sciences Research Institute, Selangor, Malaysia 

 

Abstract 

 

Plants have developed systems of effective and passive protection 

to safeguard themselves from pathogens. Active processes include 

kinds of immune responses that are adaptive and intrinsic. Adaptive 

immunity is focused on reaction type RNAi and works primarily 

against viruses. Biological immunity is more general and allows the 

plant to protect itself against a wide range of pathogens through 

bacterial and model resistance receptors (PPRs) and forms of 

resistance (R proteins). PPRs identify molecular models associated 

with microbes or pathogen that are preserved in a specific category 

of pathogens. With such big marker amounts, it has become 

feasible to scan the entire genome for interactions of individual 

markers with particular quantitatively hereditary traits called whole-

genome studies (WGS), genome-wide association trials (GWAS), 

or association genetics at exceptionally elevated marker densities. 

Several SNP recognition methods have been used in a specified 

plant to identify significant amounts of SNPs. These include: 

identification of SNP based on EST feature information; 

identification of SNP from sequenced genomes; re-sequencing of 

amplicons; identification of SNP using sequencing techniques of 

the next generation and identification of SNP based on cluster tests. 

Pathologic recognizes parts of Genbank completely annotated 

genome and MetaCyc has been used as a database for the reference 

pathway. In contrast to sequence similarity information used in 

other systems, Pathologic uses Genbank annotation information and 

the EC assignment as evidence of the presence of each pathway in 

the genome of interest reference database. When the matching task 

is finished, pathologic will infer a number of reactions expected to 

take place in the destination genome and will determine which one 

of those pathways in the target genome are likely to exist. 
 

Keywords: Resistance proteins, genome-wide association,  SNP, biological 

pathways, genome annotation .  

Review Article                                                                             Open Access   



 
 

                                                                                                                   Plant Resistance Gene, SNP and Genome Annotation 

 

 

 
Highlights in BioScience                                    DOI: 10.36462/H.BioSci.20192                                 August 2019 | Volume2 
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Introduction 

Proteins of resistance, in turn, identify distinctive 

elements of avirulence (Avr) which are not conserved 

between many pathogens. Induced-signaling resistance 

protein contributes to the manufacturing of reactive oxygen 

species and the induction of a particular form of programmed 

cell death, called the hypersensitive response, which damages 

the impacted cells (1). Also called gene-to-gene resistance is 

the resistance of protein-mediated innate immunity, as each R 

gene reacts to a particular pathogenic Avr gene (1). As a 

result, a big variety of R genes per plant genome are 

anticipated to be prepared to confer resistance to a wide 

range of pathogens. R genes are also under selection 

diversification to maintain pace with the fast pathogen 

evolution. Although distinct R genes react to very unique 

pathogens, they share multiple regions (domains) that have 

been conserved. R proteins can be split into four subclasses 

on the basis of these domains. 

SNP markers have acquired a great deal of concern in 

the science and breeding society  (2) over the past two 

centuries. They happen in nearly infinite amounts as 

variations between individual nucleotides and each SNP is a 

possibly helpful marker in single copy DNA. In the study of 

human genome, the potential of SNP markers is obviously 

illustrated. Several million SNP markers have been 

recognized (3) and techniques have been created 

concurrently (mainly up to 1 million) to evaluate big amounts 

of SNP markers based on huge study attempts and the 

complete description of the human genome. 

As more genome sequences became accessible, 

comparative assessment of various genomes became a very 

significant research method. Compared to various genomes 

of selection, biological pathway assessment is conducted 

using a range of computational techniques and databases 

(4,5). There are a range of enzymes and their substrates and 

products involved in a biological pathway. Pathways also 

communicate with each other. Thus, comparison pathway 

assessment is quite complex and can hardly be achieved 

without well-designed software systems for pathway 

assessment (4). We review the concept of plant resistance 

genes, plant SNP markers and plant Genome annotation. 

 

Resistance genes  

Most R proteins comprise a key nucleotide binding site 

(NBS) that functions as a molecular switch to regulate the 

protein's activity status, and a C-terminal, leucine-rich repeat 

region (LRR) needed to recognize Avr factor. Thus, the 

ranking of R proteins is focused on N-terminal domain 

variety (1). Drosophila Toll and human interleukin receptors 

categorized as TIR-NB-LRR proteins are homologous to 

NBS-LRR form R proteins with N-terminals. Non-TIR NBS-

LRR proteins are known to as CC-NBS-LRR proteins since 

some non-TIR proteins in their N terminus form a coiled coil 

(CC) domain (6). 

Furthermore, there are two categories of R proteins in 

their N terminus that contain an extracellular LRR. One of 

these groups, called kinases (RLKs) receptor, includes a 

kinase domain of cytoplasmic protein (7). This cytoplasmic 

protein kinase domain is also lacking in receptors such as 

proteins (RLPs). Since R genes from distinct plant species 

combine conserved domains, they could be used to monitor 

plant genomes for R genes and putative R genes (e.g., 

analogs of resistance genes, RGAs) and to generate 

molecular markers (7).  

Meyers et al. (8) investigated a genome-wide 

assessment of 149 NBS-LRR processing genes in 

Arabidopsis and verified either 55 CC-NBS-LRR (CNL) or 

94 TIR-NBS-LRR (TNL) proteins in two significant groups. 

Eight significant motifs varied in their deviation within and 

between CNL and TNL clusters, and in the model found in 

particular for plant R protein homologues. Introns may be 

more prevalent in cereals in the NBS region than in dicots. 

Only participants of the Arabidopsis Rpp8/Hrt gene class 

contain introns in the NBS domain in 20 named dicot NBS-

LRR genes. Nevertheless, in their NBS region, Pib (9), Pi-ta  

(10) and Mla1 (11), three distinguished plant resistance 

genes have introns. Repeats rich in leucine (LRRs) consist of 

redundant incomplete amino acid sections that fold into 

solvent-exposed β-cell β-loop constructions and this domain 

is believed to be engaged in ligand binding and disease 

identification (12). 

Alternating conservation motifs and hyper variance 

marked LRR areas. The variation is highest for codons (x) 

situated in the LRR consensus xxLxLxx around the two 

preserved aliphatic amino acids, and the number of LRR 

repeats ranges among family members. There were 

approximately 65 amino acids in TNL proteins between the 

NBS and LRR domains in the genome-wide assessment of 

Arabidopsis LRR domains (12). 

In Arabidopsis, in CNL proteins, LRRs represent about 

half of the C-terminal region in the TNL proteins and almost 

the entire C-terminal region. The median domain of TNL 

LRR and CNL LRR comprised an average of 14 LRRs with 

~10 separate MEME patterns spanning as many as 350 

amino acids as possible. In the rice proteins, a total of 25 

distinct LRR motifs have been recognized. In any one gene, 

the amount of LRR clusters varied from 3 to 40 (13)
 
. There 

are more than 150 genes of NBS-LRR in the Arabidopsis 

thaliana DNA. A sum of 166 NBS-LRR sequences were 

mentioned in (13), along with 33 truncated sequences. These 

NBS-LRR genes happen in their chromosome layout as 51 



 
 

                                                                                                                   Plant Resistance Gene, SNP and Genome Annotation 

 

 

 
Highlights in BioScience                                    DOI: 10.36462/H.BioSci.20192                                 August 2019 | Volume2 
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singletons and 40 clusters. Meyers et al. (2003) identified 

more NBS-LRR mutations by using comprehensive manual 

re-annotation of the same species genomic structure. They 

identified 149 NBS-LRR genes as well as 58 truncated 

genes; spread as 40 singletons and 43 clusters were the 149 

non-truncated genes. 

In Arabidopsis thaliana, TIR-NBS-LRR genes family 

was detected out CC-NBS-LRR genes by approximately two 

to one, showing either a latest amplification of the former 

group or reduction of the latter gene group (13–15). NBS-

LRR gene loci Arabidopsis are often not dispersed uniformly 

in the chromosomal genome. There are super groups on 

chromosomes 1 and 5 whereas chromosomes 2 and 3 were 

comparatively lacking in the genes of NBS-LRR (13,14). The 

clusters are believed to be engaged in both R-gene diversity 

generation and conservation. 

Meyers et al. (14) noted that the NBS gene 

chromosomal allocation is substantially non-random in 

maize: chromosome 11 includes about one-quarter of the 

NBS genes. Five hundred and thirty-five NBS encoding 

genes have been recognized in rice, including 480 non-TIR 

NBS-LRR genes. TIR-NBS-LRR genes have been not 

detected in the rice genomic DNA. In 44 gene clusters, two 

hundred and sixty-three genes (51 percent) occurred. In the 

"clustered" allocation category, there are 40 pairs and 17 

triplets, 394 genes. A total of 125 singletons of NBS have 

been distributed across all chromosomes. In the plant 

genome, the proportion of singletons to the complete amount 

of NBS chromosomes (24.1%) was comparable to that of 

Arabidopsis (26.8%). 

Baumgarten et al. (16) proposed that most of the 

genomic dispersion of NBS-LRR genes was produced by 

duplication and translocation of whole chromosome sections 

(segmental duplication), rather than by small-scale ectopic 

duplication. Most of the dramatic variation happens within 

local chromosomal areas of the NBS-LRR gene copy 

number. Zhou et al. (17) revealed that 51% of NBS genes in 

rice originated in 44 gene clusters, where a cluster is an area 

with four and sometimes more genes within 200 kb or less. 

Many surveys of NBS-LRR sequences or analogs of 

resistance genes have shown that R genes or NBS-LRR 

sequences are also structured in big clusters in other plant 

species. 

High-throughput genomic studies and plant genome 

sequences accessible in international databases provide 

unprecedented possibilities to recognize novel R-genes, 

investigate their role and method of diversification, find new 

genes for resistance, and eventually elucidate their interaction 

processes between pathogens and their crop hosts
 
 (18). In 

2009, Sanseverino et al. (18) launched the Plant Disease 

Resistance Gene database (PRGdb), a comprehensive 

repository of R-genes across hundreds of plant species, with 

the intention of facilitating research on this agriculturally 

important gene family. A total of 16,844 gene records were 

included in PRGdb version 1.0. Of these 73 R-genes (e.g. the 

' reference ' data collection) were recognized and manually 

curated, 6,308 were putative R-genes recovered from NCBI 

Genbank, and 10,463 were putative R-genes computationally 

anticipated from NCBI UniGene information. 

Many plant genome-sequencing initiatives have 

developed quickly over the past few years. For example, 

potato (19), tomato (20), and melon (21) genomes have been 

finished, providing an chance to find extra R-genes (22). 

Sanseverino et al. (22) presented an overview of the PRGdb 

database of crop resistance genes. This database has been 

extended to include more than 6-fold valuable biological data 

from 233 plant species on a total of 104,459 R-genes. Of 

these papers, 112 are defined in the literature as manual-

curated R-genes to confer resistance to 122 distinct 

pathogens. All other genes were predicted gene. 

  

 SNP identification based on EST sequence data 

Large amounts of expressed sequence tags (ESTs) were 

produced for many plant species (23). The number of 

accessible ESTs in the NCBI EST database ranges from less 

than 10,000 to over one million ESTs in significant crop 

plants and 1.5 million ESTs for the Arabidopsis model plant. 

These ESTs were obtained in many cases within the 

framework of international efforts and were accumulated 

from a limited set of different lines and could therefore 

represent as a source for SNP identification. In some 

instances, ESTs were specifically produced for SNP 

detection from distinct lines as in Arabidopsis thaliana (24) 

and in other instances, using bioinformatics assessment 

techniques (25), ESTs from heterozygous extremely 

polymorphic samples were used to identify SNP. 

 

SNP identification from sequenced genomes 

Currently, many crop species genomic sequences have 

been released. Sequenced genomes can be used to identify 

big amounts of SNPs in several respects. SNPs can be mined 

immediately in the genomic sequence in the event of 

heterozygous species such as grape or poplar, since two 

genome models have actually been produced. Two distinct 

specimens were sequenced in the scenario of rice (26) and 

grape (27) so that SNPs can be mined by comparing the two 

genome models (28). For Arabidopsis thaliana , genome re-

sequencing panels were built based on the Col-0 

chromosome sequences and used among 20 genotypes to 

identify SNPs based on hybridization (29,30). 



 
 

                                                                                                                   Plant Resistance Gene, SNP and Genome Annotation 

 

 

 
Highlights in BioScience                                    DOI: 10.36462/H.BioSci.20192                                 August 2019 | Volume2 
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As a public database (https://www.ncbi.nlm.nih.gov/), 

the Single Nucleotide Polymorphism database (dbSNP) was 

created. This database contains 1,648,103,041 SNPs for 

many organisms, e.g., Brassica napus (901.5 thousand 

SNPs); Arabidopsis thaliana (1.1 million SNPs); Cicer 

arietinum (519.1 thousand SNPs); Phoenix dactylifera (3.5 

million SNPs); Glycine max (16.9 million SNPs) and Zea 

mays (54.3 million). 

 

Genome annotation 

Karp et al. (31) established Pathway methods that use 

the pathological algorithm to assess the enzymatic responses 

catalyzed for each gene product in a query genome and then 

combine the identified response list from a reference 

database against all accessible processes. Thompson et al. 

(32) produced the KEGG Automatic Annotation Server 

(KAAS) towards the manually mapped KEGG/GENES 

registry database (33) providing functional gene annotation 

through BLAST comparisons, single best hit (SBH) and 

bidirectional best scores (BBH). As yield were produced KO 

tasks to genes and anticipated KEGG processes. Overbeek et 

al. (2005) launched the SEED platform that offers a web-

based,' subsystems'-based, relative genome annotation 

method. Subsystems were a collection of functional features 

observed in any prevalent biological system, including 

cellular processes, phenotypes, or complicated multi-subunit 

constructions. 

Haft et al. (35) established the TIGR Comprehensive 

Microbial Resources (CMR) to enable users from accessing 

all completed sequences of bacterial genomes. CMR offers 

two kinds of assets for annotation: main annotation from the 

genome sequencing centre and TIGR annotation produced 

through an interactive TIGR annotation method. The CMR 

Pathway Tool Kit comprises of three classifications of 

pathway assessment tools: ' Genome Properties ' offers 

information on the features of species extracted from 

genomic data and literature references ; ' Genome Properties 

Detailed Comparison ' offers direct step-by-step information 

for selected clients of a collection of genomes ; and ' KEGG 

Pathway Display ' shows KEGG's presence-based pathway 

measures. 

Conesa et al. (36) developed the Blast2GO (B2G), a 

universal guide for GO annotation, visualization and stats 

that provides sophisticated functional evaluation for non-

model organism genomics studies. B2G is intended to enable 

annotation of instant and high-throughput succession and 

incorporate annotation-based data mining features. B2G 

utilizes BLAST (37) 
 

to discover homologs for fasta-

formatted sequences of inputs. The program excerpts GO 

conditions by referring to existing annotation associations for 

each hit acquired. Finally, an annotation rule gives GO 

conditions to the sequence of queries. It is possible to 

visualize annotation and functional analysis in a graph form 

that reconstructs the GO interactions and highlights the most 

appropriate regions. 

In an embedded genome framework, Markowitz et al. 

(38) introduced the Integrated Microbial Genomes (JGI) for 

comparative microbial genome assessment. The data model 

comprising the IMG scheme includes main genomic 

sequence data, algorithmically forecast and ordered gene 

models, pre-computed sequence resemblance data, functional 

annotation, and pathway data. Microbial gene statistical 

analysis in IMG is conducted in a relative framework of 

various microbial genomes where a range of instruments can 

be used to assess genomes in terms of genome-specific 

statistics, genes and sequence conserved. 

The KEGG Orthology-Based Annotation System 

(KOBAS) was implemented by Wu et al. (39)  that can 

provide statistical significance examinations for anticipated 

pathways. KO terms have been chosen based either on 

sequence resemblance to KEGG/GENES entries or on 

KEGG/GENES cross-database links when a list of sequence 

classifications is accessible in the databases. Compared to the 

background model, regularly occurring or statistically 

significantly enriched query sequence pathways are 

recognized forward to KO assignment. 

Pireddu et al. (40) created a Path-A (Pathway Analyst) 

software that uses a number of query protein samples from a 

genome and defines which sequences are probable to occur 

using multiple sequence evaluation methods (e.g. SVM, 

BLAST and HMM) in any of its endorsed model pathways. 

The approach of the model pathway allows the pathway 

prediction algorithm to predict instances of a pathway with 

pathway structure variations that have never been observed in 

the training pathway set. 

At present, Path-A offers abstract models for 10 

pathways, covering 125 cases of genome-specific processes. 

Choi and Kim (4) established a Comparative Pathway 

Workbench System (ComPath) that enables researchers to 

compare biological processes in multiple genomes using a 

spreadsheet-style online interface where different sequence-

based analyzes can be conducted either to match enzymes 

(e.g. sequence clustering) or processes (e.g. pathway hole 

recognition) to search for a de novo enzyme forecast 

algorithm. 

 

 

 

 

https://www.ncbi.nlm.nih.gov/


 
 

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