Draft genome of Albugo candida Indian variant causing white rust in Brassica juncea unravel variations based on geographic distribution

doi:10.21203/rs.3.rs-4862026/v1

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Draft genome of Albugo candida Indian variant causing white rust in Brassica juncea unravel variations based on geographic distribution

https://doi.org/10.21203/rs.3.rs-4862026/v1

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Background

White rust disease elicited by biotrophic pathogen, Albugo candida is a leading cause of yield losses in oilseed Brassica crops, especially widely cultivated B. juncea. The highly diverse nature of the A. candida pathogen and its ability to adapt to various agro-climatic conditions across the globe has posed significant challenges in effectively managing this disease. Therefore, development of a successful control strategy leveraging genomic data of the white rust pathogen is important because it can reveal profound insights into the identification of different forma specialis, phylogenetics, evolution patterns, population biology, secretome and pathogenesis thus, contributing to the oilseed cultivation in India and across the globe. In the present investigation, high quality draft genome of virulent Ac2v race of A. candida infecting B. juncea was generated by Nanopore and Illumina technologies.

Results

The raw sequencing data was assembled into a genome of 36.88 Mb with 415 scaffolds and N50 = 301.91kb. The variant analysis showed 1,24,974 SNPs with an average density of 3.3 per kb genome against Ac2vPB assembly. Approximately 24.29% of the genome consists of repetitive elements, including 1039 SSRs. A total number of 13,715 coding genes were revealed in the genome with an average distribution of 359.03 genes per Mb. Out of these predicted genes, 11,556 were annotated based on sequence homology and 355 were predicted as effectors with no transmembrane domain and N terminal signal peptide. The annotation of 355 effectors revealed that 141 of them had homologs, while rest 214 were novel. Additionally, phylogenetic analysis through average nucleotide identity revealed a similarity of 99.64% between the Canadian and Indian Ac2v isolate. Furthermore, 10 new contigs were identified in the Indian isolate that showed no sequence similarity to the Canadian isolate, suggesting variation within the race based on the geography.

Conclusion

Altogether, the present work provides genomic resources and framework for the dissection of this complex pathogen which will help refining our understanding of the Albugo-Brassica interaction.

Albugo candida

Brassica juncea

Effectors

Nanopore

Whole genome assembly

Albugo candida (Pers.) O. Kuntze belonging to the class oomycete is a highly destructive biotrophic pathogen causing white rust disease, and is a significant yield limiting factor among widely cultivated vegetable and oilseed brassicas [1]. The disease got its name due to the development of characteristic creamish to white, zoosporangia filled pustules on the abaxial leaf surface of the susceptible hosts that coalesce to form larger pustules as the disease progresses [2]. Moreover, the infection can potentially disseminate systemically to the inflorescence resulting in its abnormal development, called the ‘stagheads’. In general, obligate biotrophy results in basic compatibility with a specific host due to coevolution and codependent process [3, 4]. Consequently, this adaptive evolution ensures efficient exploitation of the host species, while leading to an incompatible reaction in the non-host plants [5]. However, A. candida comprises a diverse complex of physiological races that can infect multiple hosts belonging to the Brassicaceae family [6, 7, 8]. Among the races reported to date, race 2 (Ac2V) results in a significant yield loss of 20–60% in the economically important oilseed mustard, Brassica juncea worldwide [9]. Besides race 2, race Ac7 which is predominantly confined to B. rapa is also known to infect some genotypes of B. juncea [10]. Apart from these races, an additional 22 physiological races have been documented, affecting over 200 species spanning 63 genera [11]. Furthermore, the inter-relation between genetic diversity and pathogenicity differences have also been observed among the geographically distinct A. candida isolates. Jouet et al. (2019) did a comprehensive phylogenetic analysis of Albugo spp. collected from different plant hosts and geographical regions and observed differences in the clustering of the Ac2V isolates from India and Canada. Moreover, RAPD and diversity analyses based on ITS and COX sequences have also shown great variation in the geographically differentiated isolates of the race 2 [12]. Differences in the virulence pattern of A. candida isolates collected from the different three agroclimatic regions of India namely, Hisar, Pantnagar and Samastipur have also been reported based on the biochemical variation caused upon the infection in B. juncea [13]. This varying levels of aggressiveness among the isolates is most probably due to the differences in their genetics and molecular characteristics, resulting in distinct secretome repertoires among these isolates [14]. Typically, biotrophs of the fungal class oomycete are highly adaptive with specialized and extensive secretomes. These secreted proteins are responsible for the suppression of host defense, thus ultimately facilitating the disease progression by exploiting the host physiology [15]. Nevertheless, the Ac2V secretome is documented to be two-thirds the size of the secretome of another oomycetic pathogen, Phytophthora infestans [16]. Unlike the substantial expansions observed in the secretome of Phytophthora and Hyaloperonospora genomes, the A. candida secretome exhibits comparatively modest number of gene families. Moreover, a considerable number of anticipated genes from A. candida genome lack substantial sequence correspondence with different oomycetes. This notable sequence divergence suggests specific adaptations in Albugo candida which could also describe its generalist nature. Therefore, many pathological and molecular studies are underway to comprehend the pathogenic variability in A. candida. As the pathogen is evolving rapidly, there is a need of advanced genomic studies to understand the genetic variability. So far, whole genome sequencing has been reported for race 4, isolate AcNc2 infecting A. thaliana from Norwich (UK); AcEm2, which was collected from the wild brassica, Capsella bursa-pastoris from Kent (UK) [17]; race 9, isolate AcBoT and AcBoL from B. oleracea in Lincolnshire (UK) [18]; race 2, isolate Ac2VRR [19] and Ac2vPB [20], which infects B. juncea, collected from Canada. Recent developments in sequencing techniques and advanced bioinformatic tools have facilitated rapid and comprehensive analysis of genomic data, revealing insights into virulence-associated genes, secretome, regulatory and signaling cascades, epigenetic factors that impact host-pathogen interactions and genetic diversity. Profiling of SSRs among the isolates also aid in understanding the evolutionary relationships across species. Additionally, exploring SNPs and functional variants altering phenotypes, provides opportunities to investigate pathogens’ association with virulence.

Therefore, to support ongoing efforts toward the Brassica improvement program with respect to white rust management, the present study reports a high-quality draft genome of A. candida race 2 Indian isolate found highly pathogenic to B. juncea cultivated in the Indian agro-climatic conditions. Additionally, we predicted the genome-wide distribution of SNPs, transposable elements and SSRs. Furthermore, prediction of the effector repertoire of A. candida through secretome analysis and annotation, can provide insights into the pathogenesis of this fungal pathogen. The genomic assets generated through this study will help in formulating rigorous control strategies for combating this destructive pathogen in Indian mustard (B. juncea).

Morphological and microscopic analysis of A. candida

The leaves collected from white rust infected B. juncea plants were observed for disease development. The pustules appeared on the abaxial surface in the form of irregularly distributed, small pinheads, that were creamish in colour with length ranging from 2.5–4.5 mm (av. 3.6 mm, n = 50) and diameter 1–4 mm (av. 2.6 mm, n = 50). Light microscopy of the infected leaves revealed hyphae that were branched and regularly or irregularly swollen. Some intercellular hyphae developed into unbranched, club-shaped sporogenous hyphae that formed long chain of sorus filled with numerous sporangia arranged in a basipetal succession (Fig. 1A). Sporangia appeared hyaline, globose and arranged in basipetal chains with 22.5–27.5 µm (av. 22.7) diameter (n = 50) (Fig. 1B). Furthermore, the sexual oospores were thick walled, pale yellow in colour, and globose with verrucose indentations on the wall surface (Fig. 1C). Haustoria, which are the exchange sites for nutrient uptake and fungal virulence factors such as effectors, were mostly globose and surrounded by a thick sheath with a narrow stalk (Fig. 1D). .

Genome sequencing, assembly, and BUSCO analysis

The purity of the high molecular weight-DNA, isolated from the zoosporangial mass of the A. candida (Ac2v) pathogen ‘Delhi' isolate was confirmed by sequence homology of the ITS region via PCR amplification followed by Sanger sequencing. The contaminant free DNA was then subjected to complete sequencing on the Oxford Nanopore (ONT) platform for long reads and on the Illumina HiSeq platform for short reads. The paired-end ONT and Illumina libraries generated a total data of 6.3 GB and 4.5 GB, respectively. The raw data were then filtered based on the quality score, trimmed and adapter sequences were removed. Error correction was performed to obtain reads representing at-least 40x genomic coverage. The corrected reads were then used for genome assembly into contigs and then assembled into scaffolds using MaSuRCA v3.4.2. Furthermore, Illumina reads were used to improve the base accuracy of the genomic scaffolds and gap filling. The aforementioned assembler statistics resulted in 547 contigs that were assembled into 415 scaffolds with N50 of 301.91 kb, L50 of 39, and a hybrid assembly of 36.88 Mb of A. candida (Table 1). The assembly is longer than SOLID-based Ac2VRR (34.56 Mb) and the Illumina-based AcNc2 assemblies, however, is slightly smaller than the PacBio based Ac2VPB assembly (38.96 Mb) [19, 20]. The contigs were also evaluated for homology search using NCBI blast, while most of the contigs match to the Ac2vPB assembly, 62 contigs were identified with no significant hits (Supplementary table 6). To validate the new findings, 10 contigs (contig_129, contig_131, contig_135, contig_138, contig_163, contig_22, contig_24, contig_25, contig_27 and contig_163) were taken at random and primers were designed to validate the novel contigs through PCR which confirmed their presence (Fig. 3). The total length of 62 contigs correspond to 2,34,330 bp which represents 0.63% of the total variation in the 36.88 Mb of genome. Since, only the mapped reads have been considered for the assembly into contigs, the absence of significant similarity in the 62 contigs suggests rearrangement of certain regions in the Delhi strain resulting in the varaiation. The quantitative assessment of A. candida hybrid genome assembly and annotation completeness was carried out using BUSCO. The draft assembly consisted of 97% of complete BUSCOs and a very minute percentage of missing and fragmented BUSCOs, i.e., 1% and 2%, respectively. Meanwhile, BUSCO analysis of the predicted proteins from the assembly showed 99% of complete BUSCOs for Stramenopile database and 57.1% of fungal database (Table 2).

Table 1

Statistics of draft genome assembly of *A. candida* Delhi isolate
Statistics	A. candida assembled genome
Quantitative analysis of genome assembly
Size of assembled genome	36.88 Mb
Total number of scaffolds is assembly	415
N50 scaffold length	301.91 Kb
Longest scaffold length	1.03 Mb
L50	39
GC (%)	43.44%
No. of genes predicted	13,715
Qualitative analysis of assembly
BUSCOs Complete (percentage) /fragmented (percentage)/Missing (percentage) (Metaeuk)	100% / 0%/ 0%
BUSCOs Complete number (percentage) /fragmented number (percentage)/Missing number (percentage)(Augustus)	97%/ 1%/ 2%
BUSCO for proteins
BUSCOs Complete number (percentage) /fragmented number (percentage)/Missing number (percentage) (Stramenopiles_odb)	99 (99%)/ 0 (0%)/ 1 (1%)
BUSCOs Complete number (percentage) /fragmented number (percentage)/Missing number (percentage)(fungi_odb)	433 (57.1%)/ 50 (6.6%)/ 275 (36.3%)

Bioinformatics analysis

Transposable Elements (TEs), Repetitive Sequences and variant analysis

Transposable elements (TEs) are the chief mechanistic drivers of genome evolution, therefore, whole-genome data was analyzed for understanding the transpositional landscape of the A. candida pathogen as represented in Table 2. The A. candida genome harbours both Class I and Class II transposable element (TE) families. The repeat elements occupied only 24.29% of the genome. Long terminal repeat (LTR) retroelements were the most abundant TEs, representing 7.87% of the assembly, followed by Long interspersed nuclear element (LINEs), which constitute 7.3% of the genome. Among the LTR elements, Copia/TY1 (5.6%) was more copious than Gypsy/DIRS1 (2.01) and retroviral elements (0.27). In addition, the repeat sequences consisted of DNA transposons (3.19%), low-complexity repeats (0.02%), small RNA (0.39%), simple repeats (0.23%), and some unknown sequences (5.65%).

Table 2

Types of transposable elements identified in *A. candida* genome
Types of transposable elements	Number of elements	length occupied (bp)	Percentage of sequence (%)
Retroelements	5219	5697587	15.45
SINEs:	274	101513	0.28
Penelope:	0	0	0
LINEs:	1732	2691482	7.3
CRE/SLACS	0	0	0
L2/CR1/Rex	0	0	0
R1/LOA/Jockey	0	0	0
R2/R4/NeSL	0	0	0
RTE/Bov-B	134	90188	0.24
L1/CIN4	673	1083305	2.94
LTR elements:	3213	2904592	7.87
BEL/Pao	0	0	0
Ty1/Copia	2460	2066099	5.6
Gypsy/DIRS1	696	740442	2.01
Retroviral	57	98051	0.27
DNA transposons	1535	1175672	3.19
hobo-Activator	0	0	0
Tc1-IS630-Pogo	468	505134	1.37
En-Spm	0	0	0
MULE-MuDR	1052	667095	1.81
PiggyBac	0	0	0
Tourist/Harbinger	0	0	0
Other (Mirage, P-element, Transib)	0	0	0
Rolling-circles	0	0	0
Unclassified	5954	2084549	5.65
Total interspersed repeats		8957808	24.29
Small RNA	357	143706	0.39
Simple repeats	1295	85889	0.23
Low complexity	164	7310	0.02

A total of 1039 SSRs were revealed with a relative abundance of 28.16 SSRs/Mb of the genome of the test race. The most frequent motifs were mono- (63.6%) followed by di- (22.1%), tri- (8.7%), tetra- (2.1%), penta- (0.1%), and hexa-nucleotides (3.3%). The genome sequence also yielded 108 compound SSRs. The percentage frequency of SSRs based on the nucleotide size revealed that SSRs composed mostly of smaller repeats, while SSRs with large repeats represented a smaller percentage. The distribution of the different SSR types is heterogeneous, particularly in mono- and di-nucleotides. Among P1, A/T (77.7%) was most frequently occurring and were also the most frequent motif in the entire genome followed by G/C (22.2%). Among P2, the highly distributed motifs were AG/CT (32.6%), AT/AT (31.7%) and AC/GT (26.5%). While in P3, motifs AAG/CTT (32.9%) and AAC/GTT (19.7%) were the most abundant. Among P4, ACAT/ATGT (54.5%) was copious, in P5 the only representative sequence was AAGAT/ATCTT and in P6, motif AATGTG/ACATTC (41.1%) was mostly present (Fig. 3).

The present assembly was also compared with the Ac2vPB and Ac2VRR assembly for variant analysis. A total of 1,24,974 variants were identified in the genome against Ac2vPB, out of which 1,24,923 were SNPs and 51 were InDels, while 92,444 SNPs and 52 InDels were found against the Ac2VRR assembly. This is an indicative of variation that exists between the two isolates delimited by the geographical barriers, even though they belong to the same race (race 2). Therefore, suggesting the highly variable nature of the A. candida pathogen depending upon the place of origin.

Gene prediction and functional annotation

Scaffolds arising from the primary assembly were repeat masked and further comprehensive gene predictions in the assembly was carried out using the BRAKER2 program followed by functional annotation using eggNOG emapper v2.1.8 and InterProScan v5.55-88.0 to predict protein domains, GO terms and putative pathway. A total of 13,715 genes was predicted in the Indian isolate of A. candida, which is higher than the predicted genes in Ac2vPB assembly (13,073), however, surprisingly fewer than Ac2VRR (15,826). Out of the predicted 13,715 genes, 11,556 genes were annotated with GO, COG, KEGG, CAZy and Pfam databases accounting for 84.2% of the total predicted genes (Table 3). tRNAscan-SE, Rfam and Rnammer were used to predict tRNA, snRNA and rRNA, respectively. Results showed that the genome contained 314 transfer RNAs, 22 snRNAs, and 66 ribosomal RNAs represented by 48 number of 8S subunit while, only 6 number of 28S and 18S subunit each. GO analysis for the genome revealed that the top five enriched GO term were cell, cellular process, metabolic process, and binding, with 4419 genes, 4214 genes, 3517 genes, and 3468 genes, respectively (Fig. 7).

Table 3

Functional annotation summary of *A. candida* genome from different databases
Database	Number of genes	Percentage
GO	4698	34.2%
KEGG	6357	46.3%
COG	7358	53.6%
Pfams	9602	70.0%
CAZy	231	1.7%
PHI	5922	43.2%
BiGG	128	0.9%
Reactome Pathway	8783	64.0%
MetaCyc Pathway	7288	53.1%
TMHMM	2209	16.1%

Among 31 KEGG pathways, the top five pathways were ‘Global and Overview’, ‘Signal transduction’, ‘Organismal system’, Transport and catabolism’, and ‘Cell growth and death’, including 2826 genes, 2821 genes, 1480 genes, 1117 genes, and 1082 genes, respectively (Fig. 8). According to the results of gene set enrichment analysis for top KEGG pathways, a significant proportion of genes in A. candida Indian isolate are likely involved in metabolism, environmental information processing and organismal systems.

Through COG mapping, 7358 proteins were assigned to different COG categories (Fig. 9A). The most gene-abundant classes in the COG groups included ‘Replication and repair’, with the highest number of genes (897), followed by ‘Post-translational modification, protein turnover, chaperone functions’, ‘Translation, ribosomal structure and biogenesis’, ‘Signal Transduction’, ‘Intracellular trafficking and secretion’, ‘Amino acid metabolism and transport’ and ‘Carbohydrate metabolism and transport’. These findings emerged as indicative of an enriched and diverse set of proteins associated with metabolism and secretion functions. These proteins perform a central role in enhancing the absorption and metabolism of nutrients from substrates, thereby fostering infection in the host plant. CAZy database was used on the predicted fungal genes to annotate the carbohydrate-active enzyme (CAZymes). A total of 231 genes were assigned to CAZymes families out of which the highest percent was represented by glycoside hydrolases (GHs, 49.1%), followed by glycosyl transferases (GTs, 37.3%), carbohydrate-binding modules (CBMs, 5.5%), polysaccharide lyases (PLs, 4.5%), and carbohydrate esterases (CEs, 3.6%) (Fig. 9B). GHs are usually predominant in majority of fungal pathogens, their function to break down glycosidic bonds is an indicative of their diverse role in the fungal pathogenesis like breakdown and modification of the plant cell wall components. Especially for a biotroph like A. candida which depends on its host for nutrition, GHs can play a major role in cell wall manipulation allowing development of haustoria. An inclusive analysis of these CAZys can give valuable information to discern the host-pathogen interaction, ultimately contributing towards our understanding of disease establishment. Homology search for pathogenicity-related genes among 13,715 predicted genes revealed 9152 genes that aligned, with the threshold of Evalue < 1e − 05, to the PHI database. These pathogenesis-related proteins were predominantly categorized in six groups considering their functional characteristics and distinctive traits. Most PHI genes were categorized under reduced virulence (3732 genes), followed by unaffected pathogenicity (1175 genes), loss of pathogenicity (555 genes), lethal (210 genes), increased virulence (179 genes) and least under plant_avirulence_determinant (71 genes).

Secretome and effector diversity

Of the total 13,715 protein encoding genes, SignalP v6.0 predicted 688 secretory proteins with signal peptides (SP) in the N-terminal region. This repertoire of secretory protein appears to be smaller than that identified from the transcriptome of the Ac2VRR (929 secreted proteins) and Ac2VPB (1,104 secreted proteins) isolates of A. candida [19, 20]. The predicted secretory proteins were then subjected to TMHMM that identified 526 secretory proteins without transmembrane domain from the protein dataset. Removing the rest 162 proteins that had one or multiple transmembrane domains the predicted secretory proteins were then classified using EffectorP, which predicted 355 effector proteins. Among these effector proteins, 304 were categorized as cytoplasmic effectors (85.6%) and 51 as apoplastic effectors (14.4%). The predicted effector proteins were then annotated against Pfam, GO, KEGG, COG, PHI and CAZy database to evaluate their role in pathogenesis. Annotation of the 355 effector proteins showed only 125 proteins with identity and description while rest 230 proteins were novel and were further classified based on the motifs into RxL and CCG types.

Out of the 125 annotated effectors, 64 proteins aligned to PHI-base at 1e-05 under different categories such as reduced virulence, unaffected pathogenicity, loss of pathogenicity, hypervirulence and avirulence effector. Of the rest 61 proteins, 4 were classified under CAZy as glycoside hydrolase and glycosyl transferases, nine were identified to have plant cell wall modification activity out of which three were identified to be pectate lyase while five were polygalacturonase and only one was found to have cellulase activity. Moreover, the results showed three cysteine rich secretory proteins that are known to be responsible for eliciting defense response in non-host plants [21, 22, 23].

Among the crinkling and necrosis (CRN) class effectors six proteins were identified that had amino-terminal “LYLAK” instead of “LFLAK” domain similar to the CRN effector identified in Phytophthora sojae (Fig. 4) [24]. However, Interestingly, out of the six CRN proteins, only one protein was identified as effector while the rest five had no signal peptide sequence suggesting their non-secretory nature. Apart from CRNs we also identified six secretory proteins without transmembrane domain annotated as elicitins through InterProScan. Elicitins are a highly conserved group of secreted proteins and includes members such as INF1, a 10 KDa protein from P. infestans. The presence of six cysteine residues is a common feature of elicitins and has been used to mine databases revealing 156 elicitins (ELI) or elicitin-like (ELL) genes from seven Phytophthora species [25].

Cellulose Binding Elicitor Lectin (CBEL) is a cell wall glycoprotein first reported from P. parasitica var. nicotianae that is responsible for eliciting a defense response in the host Nicotiana benthamiana [26]. CBELs from Phytophthora species generally has a secretion signal followed by interleaved Cellulose Binding (CBD) and Apple domains which mediate protein-protein or protein-carbohydrate interactions. Links et al. (2011) reported two CBELs in their Ac2VRR genome. Ac2VRR-CBEL1 had a signal peptide while Ac2VRR-CBEL2 did not contain a signal peptide. However, three CBELS were identified in the present assembly containing signal peptide with no transmembrane domain and having sequence similarity to the CBEL reported from P. parasitica. The results also showed two effector proteins with cellulose binding domain following the signal peptide but no apple domain.

The search for RXLR effector candidates among the 290 novel effector proteins against the RdEER.hmm, showed no hits for homology suggesting a lack of the commonly found RxLR motif in A. candida [27]. However, Links et al. (2011) reported a small group of genes that encode secreted proteins with a variant RxLR motif (Ac-RxL). Therefore, we build a MEME motif using sequences of “Ac-RxL” effector proteins reported in Ac2VRR assembly. The motif “RSLRRSTE” was then used to search in our list of putative effector proteins through MAST. As opposed to 26 predicted RXL effector identified in Ac2VRR assembly, in the present assembly only 3 gene moles were predicted which contained a putative sec-dependent signal peptide, lacked homology to known proteins, and had an Ac-RXL motif R(SQK)L(FRKE) near the N terminal (Fig. 5).

CHxC effectors are another conserved effector motif commonly present in most of the Oomycetes. However, Furzer et al. (2022) reported that compared with the consensus motif from AlNc14 (A. laibachii) genome, the previously reported histidine residue in the CHxC motif was less conserved in Ac2VPB, but a glycine six amino acids after the second cysteine was highly conserved, making the consensus motif CxxCxxxxxG. They redefined the effector class as “CCGs” in A. candida. CCG type effectors were also reported in the Ac2VRR assembly. Therefore, based on the previously identified CCG class effectors in Ac2VRR and Ac2VPB genome, a motif was generated through MEME (Fig. 6). Using the predicted effectors as input file in MAST, a total of 13 CCG protein candidates were identified that showed no homology to other protein, accounting for 3% of the secretome. This number is far less than the, 110 CCG candidates identified in Ac2vPB assembly and 40 in Ac2VRR, this could be an indicative of the difference in virulence between the two variants, suggesting a low degree of virulence in the Indian isolate as compared to its Canadian variant. Another reason for the difference in the numbers of CCG and RxLR effectors could be that, in the case of the Canadian isolate, the effectors were predicted from the transcriptome dataset. In contrast, in the present study, the proteins predicted from the genome were used as the query for effector prediction, although the same pipeline and tools were used as in previous studies. Rest of the secretome had no known motif and need to be further evaluated for their role in pathogenesis.

Genome-level phylogenetic analysis

Although previous studies have revealed the phylogeny and genetic diversity of A. candida, most of them have been based on the sequence alignment of nuclear ITS or mitochondrial COX [12, 28]. In current investigation, the phylogenetic position of A. candida race Ac2v Delhi isolate was elucidated by constructing genome-based phylogeny using average nucleotide identity (ANI). It is a robust tool that provides reliable evolutionary relationships among species by calculating the nucleotide-level genomic similarity [29]. As the ANI values remain unaffected by horizontal gene transfer or variable evolutionary rates, it serves as a fine indicator of the relatedness among the coding region of two genomes [30]. Typically, an ANI value surpassing 95% is widely accepted and is a benchmark for species classification and clustering. Conferring from the results as shown in Fig. 10, the ANI values varied from 75.2–99.8%. Among Albugo genus the present assembly had high average nucleotide identity with race Ac2vPB (99.64%) and Ac2vRR (99.47%) therefore clustering them together in the phylogeny tree (Fig. 11), followed by race Ac7v (98.5%), while for Albugo laibachii race AlNc14, infecting Arabidopsis thaliana, the genome showed low ANI value (80.23%), confirming the difference in species. The phylogeny confirmed that the most prevalent race of A. candida infecting B. juncea in India is a variant of race 2 rather race 7 which also infects the same host plant. Although the difference in ANI values among the A. candida isolates might not seem much, but as low as 0.36% between Ac2vPB and Ac2v Delhi variant suggest that the genome have frequently undergone wide range of re-arrangements rather than indels accumulating to huge variations thus evidencing towards its generalist behaviour. This is also suggested by identification of new contigs in case of the Ac2v Delhi assembly, as the reads were mapped to the reference genome however, when assembled into contigs the BLAST result showed no sequence similarity.

Albugo candida presents a serious threat to the quantity and quality of oil production in Indian mustard cultivars which are grown in different agroclimatic zones of India. Since, a comprehensive genome dataset can serve as an important resource for understanding pathogenicity, virulence effectors, retroelements, and evolutionary origins, aiding in the formation of suitable strategy for effective management of white rust pathogen, we completed the whole-genome sequencing and derived the best assembly through hybrid mode. The 36.88 Mb draft genome of A. candida race Ac2v Indian variant was assembled with a total of 547 contigs into 415 scaffolds with N50 of 301.91 kb and L50 of 39. Secretome analysis revealed 355 effectors, 3 of which were RXL proteins and 13 were CCGs. A small repertoire of CRNs, elicitins and CBELs was also reported in the current assembly. The genome annotation of 11,556 genes were classified under three broad groups cellular components, biological and function process. The highest number of genes were classified under biological processes. CAZys showed only a small percent of genes (1.7%) showing catalytic and carbohydrate binding function. The extensive data and information garnered from the present assembly of A. candida will also contribute to a better comprehension of the racial variability within the pathogen. Deciphering the genome of Indian virulent race Ac2v of A. candida will also pave the way for future research in various aspects of the pathogen, including its evolution in response to environmental variations, biosynthesis of secretory proteins associated with its pathogenesis and the dynamics of host-pathogen interactions. Additionally, it will aid in the development of sustainable management strategies for this pathogen.

Purification and maintenance of A. candida inoculum

Seeds of the susceptible B. juncea cv. Pusa Jaikisan acquired from National Institute for Plant Biotechnology (NIPB), India were sown in sterile soil and maintained at 15 hrs. of light with the daytime temperature of 16°C and 14°C at night in growth chambers. One-month-old plants were subsequently inoculated with A. candida zoosporangia to establish a fresh infection and maintain the pathogen. For inoculum preparation, the zoosporangial powder was scraped from a single pustule on the infected leaves and collected in gelatin capsules. The sporangia were then homogenized in sterile double distilled water (sporangia count ~ 2 × 10⁴) and incubated at 8°C for 1.30 hr. and kept for another 10 minutes at room temperature to facilitate the release of zoospores. The plants were drop inoculated (10 µl) using a surface sterilized pipette and kept at 14°C for 24 hrs, after which they were further transferred to the growth chamber for disease development.

Morphological and microscopic analysis of A. candida

For microscopic analysis, the infected leaves were collected in carnoy’s solution for destaining upto 24 hrs., followed by rehydration with distilled water. The rehydrated leaves were stained using aniline blue and observed with fluorescence microscope (Axiolab4, Carl Zeiss, Germany). For other structures a suspension of fungal inoculum from infected leaves was mounted with 60% lactic acid and observed under brightfield. Sporangial sizes (in µm) from five sporangia per pustule were recorded (total of 10 pustules, n = 50) at 40X magnification. The pustule size (in mm) was also recorded by measuring the length and width of 50 randomly selected pustules using a scale. The colour of the pustules was visually assessed from the infected samples.

Genomic DNA extraction

For the high molecular weight-DNA isolation, pustules filled with zoosporangia on heavily infected leaves were scraped and collected in sterile gelatin capsules. The spores were ruptured by dry beating for 30 seconds using glass beads in 2 ml microcentrifuge tubes under cryogenic conditions, followed by DNA extraction through CTAB method [31]. Ruptured spores were homogenized in CTAB buffer and the vials were incubated at 65°C for 30–40 minutes. These tubes were then centrifuged for 5 minutes at 10,000 rpm, to separate supernatant from the debris. Tubes were mixed with 20 µl RNase (20 mg/ml; G biosciences) and kept in water-bath for 10 minutes at 65°C. Subsequently, a mix of phenol:chloroform:isoamyl-alcohol (Merck) was added in equal volumes and invert mixed. These tubes were then centrifuged at 13,000 rpm for 10 minutes to separate the sample mix into three layers. The top aqueous layer was carefully pipetted into a fresh vial, to which equivalent volume of chilled 100% isoamyl-alcohol was added and mixed gently. The vials were kept at − 20°C for 30 min. to precipitate the DNA and then centrifuged for 20 minutes at 13,000 rpm under cool temperature (4°C). The resulting DNA pellet was washed by 70% ethanol and allowed to dry in air. Upon drying, the pellet was dissolved in 1x TE buffer and used for library preparation. DNA concentrations and purity were measured with Qubit 4 fluorometer (Thermo Scientific, USA) and the quality was checked on a 0.8% agarose gel. Furthermore, to check for any other contamination in the DNA sample ITS amplification was performed using the ITS1 (forward) and ITS4 (reverse) primers [32]. Furthermore, the amplicon was separated on a 2% gel and eluted using PureLink gel extraction kit (Invitrogen, U.S.A.) for Sanger sequencing.

Library preparation and sequencing

The genome was sequenced on two platforms, NovaSeq 6000 (Illumina, California, U.S.A.) for short reads and MinION MklB (Oxford Nanopore Technologies, UK) for long reads. Library preparation for Nanopore was done by Ligation Sequencing kit (SQK-LSK109) as per the manufacture’s protocol which included DNA repair and end prep for adapter attachment followed by purification with AMPure XP beads. For Illumina sequencing, randomly fragmented DNA (200–400 bp) were processed for adapter ligation, 3’ adenylation, end repair and PCR amplification. Following magnetic bead purification, the Qubit 4 fluorometer was used to qualify library (Thermo Fisher Scientific, USA), and insert length was checked on agarose gel (2%). Libraries that qualified were sequenced on both the platforms (Centyl Biotech, India). Furthermore, base calling of the Nanopore Signal Dataset was performed using the guppy (5.0.1) high-accuracy base calling model in order to derive the Fastq sequences.

Bioinformatics and statistical analysis

Data processing

The Illumina reads were trimmed for bases of low quality, adapters and poly X tails using Fastp v0.23.2 [33]. Reads were retained after trimming if the average base quality was greater than Q30. For the Nanopore data, sequences were filtered with a length threshold of 1000 bp and passed through PoreChop v0.2.4 to remove adapters and to split chimeric reads if any [34]. Furthermore, the quality of the raw sequences from nanopore was checked throughs LongQC [35]. The Nanopore and Illumina data were further subjected to contamination screening, since it is possible that many micro-eukaryotes can harbor host genetic content or form relationships with other species or kingdoms of life. Albugo genomes from NCBI were downloaded and used for mapping with minimap2 2.24-r1122 for Nanopore reads and BWA-MEM for Illumina reads [36, 37]. For the Illumina dataset, reads that mapped pairwise were extracted, while, for Nanopore all the mapped reads were considered. The processed and contamination free nanopore reads were then corrected with NECAT [38].

Genome assembly

The processed reads were directly assembled into contigs with Canu v2.2 [39]. The assembly was further pruned to remove duplicate contigs and to resolve them into alternative haplotypes using purge dups v1.2.6. [40]. For scaffolding ntLink-Longstitch v1.3.4 was used [41]. Later Samba from MaSuRCA v4.0.9 was employed keeping matching length of minimum 2000 bp and maximum overhang of 500 bp, to fill the gaps [42]. Finally, the scaffolds were polished iteratively with 5 rounds of Racon v1.4.3 using Nanopore Mapped reads and, subsequently, 4 rounds of polca from MaSuRCA with Illumina reads to improve the base accuracy of the genomic scaffolds.

Gene prediction and annotation

Prior to gene prediction the genome was repeat masked using Red v2.0 (Repeat Detector) to mask the false gene models arising due to transposable elemnts, low complexity regions and other interspersed repeats. Genes were predicted ab initio in the A. candida assembly through BRAKER2 [43], using the fungus training option with GeneMark-ES. This approach involves the initial prediction of putative gene-models using ProtHint with the reference proteome, followed by the prediction of models with GeneMark, which are then used for training Augustus. The BRAKER2-predicted gene models were annotated functionally by eggNOG emapper v2.1.8 and InterProScan v5.55-88.0 to predict protein domains through Pfam, putative pathways and metabolic reactions through KEGG, GO and COG databases while CAZymes were annotated through the CAZyDB (carbohydrate-active enzymes database) [44, 45]. For the prediction of transfer RNAs (tRNAs), tRNAscan-SE with eukaryotic parameters was used [46]. The rRNAs were predicted using RNAmmer, and for annotation of small nuclear RNAs, homology search against the Rfam database was done [47, 48].

Identification of transposable elements (TEs), repeated sequences and variant analysis

For the de-novo estimate of repeated sequence in scaffolds RepeatModeler was used. Subsequently, RepeatMasker was used to identify the type of repeat sequence along with their location and frequency within a genomic segment, utilizing the database generated from RepeatModeler as the reference library [49]. SSRs were predicted through MicroSAtellite Identification (MISA) tool to find the type of motif families and their frequency in the genome [50]. The SNPs and indels were predicted by read alignment of the present assembly with the available genome sequences of A. candida Ac2vRR and Ac2vPB using the BWA-MEM aligner. The output file resulting from the alignment was then converted to BAM file using samtools v0.1.19 [51]. For variant calling mpileup program from bcftools was used at a read depth of 10x.

Secretome and Effector Identification

Transeq (EMBOSS) was utilized to translate the predicted gene sequences into protein sequences which was then used as query for the command line based SignalP v6.0 program to discover the putative secretory proteins on the basis of N terminal signal peptides [52]. Subsequently, TMHMM v2.0 was used to further shortlist the secretory proteins with no transmembrane domains [53]. The candidate secretory proteins (CSPs) were then filtered through EffectorP-3.0 [54] to identify and characterize them into cytoplasmic and apoplastic effectors. The candidate effectors were annotated with Pfam, CAZy and PHI-base through homology search (E-value ≤ 1e − 05). Furthermore, effectors without any significant hits were classified based on the motifs (among first 30–100 amino acids) using the command line MEME suit (Fig. 12) [55].

Phylogenetic analysis

The 9 representative genomes of Albugo species along with other oomycete pathogens as listed in Table 4 were downloaded from the NCBI (data accessed on 2nd January 2024). Average nucleotide identity of the draft genome was calculated using FastANI to create a distance matrix which was then used as an input file in MEGA11 [56] to visualize the phylogenetic tree.

Table 4

List of the representative genomes of the fungal pathogens of class Oomycetes
Pathogen species	Assembly accession
Ac2VPB.fna	CAJNAR010000001.1
Ac2vRR.fna	JAHTBM010000001.1
Ac7v.fna	LGTM01000001.1
AcEm2.fna	JZXB01000001.1
AcEx.fna	LGTN01000001.1
AcNc2.fna	CAIX01000001.1
AlNc14.fna	CACSLJ010000001.1
AcBot.fna	CAIV01000001.1
AcBol.fna	CAJG01000001.1
Peronospora_belbahrii.fna	CAKKTJ010000001.1
Hyaloperonospora_parasitica.fna	JAPPWB010000001.1
Peronosclerospora_sorghi.fna	CM047580.1
Plasmopara_halstedii.fna	NW_020186923.1
Phytophthora_parasitica.fna	KI669561.1
Phytophthora_infestans.fna	DS029320.1
Phytophthora_sojae.fna	NW_009258115.1
Pythium_aphanidermatum.fna	KE463865.1
Pythium_oligandrum.fna	JAHRIK010000001.1
Pythium_vanterpoolii.fna	JAADVD010000001.1
Bremia_lactucae.fna	CM033803.1

Ethics approval and consent to participate

Not Applicable

Consent for publication

Not Applicable

Competing interests

The authors have declared that no competing interests exist.

Funding

The authors are gratefully for the financial assistance rendered by the DST-SERB (DST-SERBCRG/2020/004860) New Delhi.

Author Contribution

A.K.G., M.R. and R.C.B. conceived and supervised the project. S.M., R.T., A.K., P.N., B.M.B. and J.A. designed the experiments. S.M., R.T., A.K. and Y.I. performed the experiments. S.M., Y.I. and A.K.G. analyzed data and wrote the manuscript. P.R., Y.K.Y. and P.D.M. contributed equally to this work.

Acknowledgement

The authors are gratefully for the financial assistance rendered by the DST-SERB (DST-SERBCRG/2020/004860) New Delhi.

Data Availability

Data supporting the results of this study are available within the paper. The reagents, plant and microbe materials, and datasets used, generated, and analyzed during this work are available from the corresponding author upon request. The genome sequencing data mentioned in this study has been deposited in the NCBI database under accession number JBBMRL000000000.

Kamoun S, Furzer O, Jones JDG, Judelson HS, Ali GS, Dalio RJD, Roy SJ, Schena L, Zambounis A, Panabieres F, Cahill D, Ruocco M, Figueiredo A, Chen XR, Hulvey J, Stam R, Lamour K, Gijzen M, Tyler BM, Grunwald NJ, Mukhtar MS, Tome DF, Tor M, Ackerveken GVD, McDowell J, Daayf F, Fry WE, Lindqvist-Kreuze H, Meijer HJ, Petre B, Ristaino J, Yoshida K, Birch PR, Govers F. The Top 10 oomycete pathogens in molecular plant pathology. Mol Plant Pathol. 2015;16(4):413–34.
Saharan GS, Verma P, Borhan PMMH, Singh DK. Analysis of white rust research progress through bibliography. J Oilseed Brassica. 2014;1(5):42–115.
Cooper AJ, Latunde-Dada AO, Woods-Tor A, Lynn J, Lucas JA, Crute IR, Holub EB. Basic Compatibility of Albugo candida in Arabidopsis thaliana and Brassica juncea Causes Broad-Spectrum Suppression of Innate Immunity. Mol Plant Microbe Interact. 2008;21(6):745–56.
Thines M. Phylogeny and evolution of plant pathogenic oomycetes—a global overview. Eur J Plant Pathol. 2014;138(3):431–47.
Raman A, Sharma A, Subrahmanyam S. Effectors in plant–microbe interactions. J Plant Interact. 2012;7(4):376–376.
Kole C, Williams PH, Rimmer SR, Osborn TC. Linkage mapping of genes controlling resistance to white rust (Albugo candida) in Brassica rapa (syn. campestris) and comparative mapping to Brassica napus and Arabidopsis thaliana. Genome. 2002;45(1):22–7.
Panjabi-Massand P, Yadava SK, Sharma P, Kaur A, Kumar A, Arumugam N, Sodhi YS, Mukhopadhyay A, Gupta V, Pradhan AK, Pental D. Molecular mapping reveals two independent loci conferring resistance to Albugo candida in the east European germplasm of oilseed mustard Brassica juncea. Theor Appl Genet. 2010;121(1):137–45.
Pound GS, Williams PH. Biological races of Albugo candida. Phytopathology. 1963;53:1146–9.
Gupta A, Raj R, Kumari K, Singh S, Solanki I. Management of major diseases of Indian mustard through balanced fertilization, cultural practices and fungicides in calcareous soils. Proc Natl Acad Sci. 2018;88:229–39.
Petrie GA. Races of Albugo candida (white rust and staghead) on cultivated Cruciferae in Saskatchewan. Can J Plant Pathol. 1988;10(2):142–50.
Jouet A, Saunders DGO, McMullan M, Ward B, Furzer O, Jupe F, Cevik V, Hein I, Thilliez GJA, Holub EB, Oosterhout CV, Jones JDG. Albugo candida race diversity, ploidy and host-associated microbes revealed using DNA sequence capture on diseased plants in the field. New Phytol. 2019;221(3):1529–43.
Yadav R, Pandya R, Tiwari S, Singh B, Tripathi M. Genetic variability in Albugo candida pathogen isolates collected from Indian mustard in Northern Madhya Pradesh using RAPD marker analysis. IJCS. 2019;7(2):237–41.
Chatterjee A, Nirwan S, Mohapatra S, Sharma P, Agnihotri A, Shrivastava N. Biochemical aspects of pathogenic variability in white rust infected Indian mustard. Mycologia. 2022;114(4):757–68.
Gupta NC, Yadav S, Arora S, Mishra DC, Budhlakoti N, Gaikwad K, Rao M, Prasad L, Rai PK, Sharma P. Draft genome sequencing and secretome profiling of Sclerotinia sclerotiorum revealed effector repertoire diversity and allied broad-host range necrotrophy. Sci Rep. 2022;12(1):21855.
Mapuranga J, Zhang N, Zhang L, Chang J, Yang W. Infection Strategies and Pathogenicity of Biotrophic Plant Fungal Pathogens. Front Microbiol. 2022;13:799396.
Raffaele S, Win J, Cano LM, Kamoun S. Analyses of genome architecture and gene expression reveal novel candidate virulence factors in the secretome of Phytophthora infestans. BMC Genomics. 2010;11(1):637.
Borhan MH, Gunn N, Cooper A, Gulden S, Tör M, Rimmer SR, Holub EB. WRR4 Encodes a TIR-NB-LRR Protein That Confers Broad-Spectrum White Rust Resistance in Arabidopsis thaliana to Four Physiological Races of Albugo candida. Molecular Plant-Microbe Interactions. 2008;21(6):757–768.
Kemen E, Gardiner A, Schultz-Larsen T, Kemen AC, Balmuth AL, Robert-Seilaniantz A, Bailey K, Holub EB, Studholme DJ, Maclean D, Jones JDG. Gene Gain and Loss during Evolution of Obligate Parasitism in the White Rust Pathogen of Arabidopsis thaliana. PLoS Biol. 2011;9(7):e1001094.
Links MG, Holub E, Jiang RH, Sharpe AG, Hegedus D, Beynon E, Sillito D, Clarke WE, Uzuhashi S, Borhan MH. De novo sequence assembly of Albugo candida reveals a small genome relative to other biotrophic oomycetes. BMC Genomics. 2011;12(1):503.
Furzer OJ, Cevik V, Sebastian F, Kate B, Amey R, Christian S, Dan ML, Eric BH, Jonathan DGJ. An Improved Assembly of the Albugo candida Ac2V Genome Reveals the Expansion of the CCG Class of Effectors. Mol Plant Microbe Interact. 2022;35(1):39–48.
Mattinen L, Tshuikina M, Mäe A, Pirhonen M. Identification and Characterization of Nip, Necrosis-Inducing Virulence Protein of Erwinia carotovora subsp. carotovora. Molecular Plant-Microbe Interactions®. 2004;17(12):1366–1375.
Pemberton CL, Whitehead NA, Sebaihia M, Bell KS, Hyman LJ, Harris SJ, Matlin AJ, Robson ND, Birch PRJ, Carr JP, Toth IK, Salmond GP. Novel Quorum-Sensing-Controlled Genes in Erwinia carotovora subsp. carotovora: Identification of a Fungal Elicitor Homologue in a Soft-Rotting Bacterium. Mol Plant Microbe Interact. 2005;18(4):343–53.
Motteram J, Küfner I, Deller S, Brunner F, Hammond-Kosack KE, Nürnberger T, Rudd JJ. Molecular Characterization and Functional Analysis of MgNLP, the Sole NPP1 Domain–Containing Protein, from the Fungal Wheat Leaf Pathogen Mycosphaerella graminicola. Mol Plant Microbe Interact. 2009;22(7):790–9.
Stam R, Jupe J, Howden AJM, Morris JA, Boevink PC, Hedley PE, Huitema E. Identification and characterisation CRN effectors in Phytophthora capsici shows modularity and functional diversity. PLoS ONE. 2013;8(4):e59517.
Jiang RHY, Tyler BM, Whisson SC, Hardham AR, Govers F. Ancient Origin of Elicitin Gene Clusters in Phytophthora Genomes. Mol Biol Evol. 2006;23(2):338–51.
Mateos FV, Rickauer M, Esquerré-Tugayé MT. Cloning and Characterization of a cDNA Encoding an Elicitor of Phytophthora parasitica var. nicotianae That Shows Cellulose-Binding and Lectin-Like Activities. Mol Plant Microbe Interact. 1997;10(9):1045–53.
Win J, Kamoun S. Adaptive evolution has targeted the C-terminal domain of the RXLR effectors of plant pathogenic oomycetes. Plant Signal Behav. 2008;3(4):251–63.
Choi YJ, Hong SB, Shin HD. Genetic diversity within the Albugo candida complex (Peronosporales, Oomycota) inferred from phylogenetic analysis of ITS rDNA and COX2 mtDNA sequences. Mol Phylogenet Evol. 2006;40(2):400–509.
Zhang N, Luo J, Bhattacharya D. Advances in Fungal Phylogenomics and Their Impact on Fungal Systematics. Adv Genet. 2017;100:309–28.
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9(1):5114.
Doyle JJ, Doyle JL. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull. 1987;19(1):11–5.
White TJ, Bruns T, Lee S, Taylor J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ, editors. PCR Protocols: a Guide to Methods and Applications. New York: Academic; 1990. pp. 315–22.
Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–890.
Wick RR, Judd LM, Gorrie CL, Holt KE. Completing bacterial genome assemblies with multiplex MinION sequencing. Microb Genom. 2017;3(10):e000132.
Fukasawa Y, Ermini L, Wang H, Carty K, Cheung MS. LongQC: A Quality Control Tool for Third Generation Sequencing Long Read Data. G3 Genes|Genomes|Genetics. 2020;10(4):1193–6.
Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34(18):3094–100.
Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754–60.
Chen Y, Nie F, Xie SQ, Zheng YF, Dai Q, Bray T, Wang YX, Xing JF, Huang ZJ, Wang DP, He LJ. Efficient assembly of nanopore reads via highly accurate and intact error correction. Nat Commun. 2021;12(1):60.
Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k -mer weighting and repeat separation. Genome Res. 2017;27(5):722–36.
Guan D, McCarthy SA, Wood J, Howe K, Wang Y, Durbin R. Identifying and removing haplotypic duplication in primary genome assemblies. Bioinformatics. 2020;36(9):2896–8.
Coombe L, Warren RL, Wong J, Nikolic V, Birol I. ntLink: A Toolkit for De Novo Genome Assembly Scaffolding and Mapping Using Long Reads. Curr Protoc. 2023;3(4):e733.
Zimin AV, Marçais G, Puiu D, Roberts M, Salzberg SL, Yorke JA. The MaSuRCA genome assembler. Bioinformatics. 2013;29(21):2669–77.
Brůna T, Hoff KJ, Lomsadze A, Stanke M, Borodovsky M. BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP + and AUGUSTUS supported by a protein database. NAR Genom Bioinform. 2021;3(1):lqaa108.
Cantalapiedra CP, Hernández-Plaza A, Letunic I, Bork P, Huerta-Cepas J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol Biol Evol. 2021;38(12):5825–9.
Jones P, Binns D, Chang HY, Fraser M, Li W, McAnulla C, McWilliam H, Maslen J, Mitchell A, Nuka G, Pesseat S. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30(9):1236–40.
Chan PP, Lin BY, Mak AJ, Lowe TM. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res. 2021;49(16):9077–96.
Lagesen K, Hallin P, Rødland EA, Stærfeldt HH, Rognes T, Ussery DW. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007;35(9):3100–8.
Kalvari I, Nawrocki EP, Ontiveros-Palacios N, Argasinska J, Lamkiewicz K, Marz M, Griffiths-Jones S, Toffano-Nioche C, Gautheret D, Weinberg Z, Rivas E. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res. 2021;49(D1):D192–200.
Flynn JM, Hubley R, Goubert C, Rosen J, Clark AG, Feschotte C, Smit AF. RepeatModeler2 for automated genomic discovery of transposable element families. Proceedings of the National Academy of Sciences. 2020;117(17):9451–9457.
Thiel T, Michalek W, Varshney R, Graner A. Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L). Theor Appl Genet. 2003;106(3):411–22.
Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27(21):2987–93.
Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011;8(10):785–6.
Krogh A, Larsson B, von Heijne G, Sonnhammer ELL. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J Mol Biol. 2001;305(3):567–80.
Sperschneider J, Dodds PN. EffectorP 3.0: Prediction of Apoplastic and Cytoplasmic Effectors in Fungi and Oomycetes. Mol Plant Microbe Interact. 2022;35(2):146–56.
Bailey TL, Johnson J, Grant CE, Noble WS. The MEME Suite. Nucleic Acids Res. 2015;43(W1):W39–49.
Tamura K, Stecher G, Kumar S. MEGA11: Molecular Evolutionary Genetics Analysis Version 11. Mol Biol Evol. 2021;38(7):3022–7.

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Draft genome of Albugo candida Indian variant causing white rust in Brassica juncea unravel variations based on geographic distribution

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Abstract

Background

Results

Conclusion

Figures

Background

Results and discussion

Genome sequencing, assembly, and BUSCO analysis

Bioinformatics analysis

Transposable Elements (TEs), Repetitive Sequences and variant analysis

Gene prediction and functional annotation

Secretome and effector diversity

Genome-level phylogenetic analysis

Conclusion

Materials and Methods

Genomic DNA extraction

Library preparation and sequencing

Bioinformatics and statistical analysis

Data processing

Genome assembly

Gene prediction and annotation

Identification of transposable elements (TEs), repeated sequences and variant analysis

Secretome and Effector Identification

Phylogenetic analysis

Declarations

Competing interests

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

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