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. 2024 Aug 28;56(1):20240029. doi: 10.2478/jofnem-2024-0029

Genome Announcement: Draft Genome Assembly of Heterodera humuli Generated Using Long-Read Sequencing

Lester A Núñez-Rodríguez 1,4, Catherine L Wram 2,✉, Cedar Hesse 3, Inga A Zasada 4
PMCID: PMC11366395  PMID: 39221107

Abstract

The hop cyst nematode, Heterodera humuli, is the most common plant-parasitic nematode associated with hop worldwide. This study reports the draft genome of H. humuli generated on the PacBio Sequel IIe System with the ultra-low DNA input HiFi sequencing method, and the corresponding genome annotation. This genome resource will help further studies on H. humuli and other cyst nematodes.

Keywords: cyst nematodes, Heterodera humuli, hop, long reads, ultra-low DNA input

Cyst nematodes are considered the second most important plant-parasitic nematodes (PPNs) worldwide, with Globodera and Heterodera as the most important genera (Jones et al., 2013). These PPNs are important not only for the extensive crop loss they cause but also for their ability to survive for long periods of time (> 20 years) in soil without a suitable host (Wright and Perry, 2006). The genus Heterodera is divided into nine groups based on morphological and molecular information: Afenestrata, Avenae, Bifenestra, Cardiolata, Cyperi, Goettingiana, Humili, Sacchari, and Schachtii (Handoo and Subbotin, 2018). The hop cyst nematode, Heterodera humuli, is the most common PPN associated with hop worldwide. Heterodera humuli is commonly found in Washington, Idaho, and Oregon, the most important states for hop production in the U.S. (Darling et al., 2023). Despite the importance of the pest, very little molecular data is available for H. humuli, including a lack of genomic or transcriptomic resources. Of the 82 described Heterodera spp. there are only three publicly available genomes, two from the Schachtii group (H. glycines and H. schachtii), and one from the Goettingiana group (H. carotae) (Masonbrink et al., 2019; Siddique et al., 2022; Wram et al., 2022). Generating a high-quality genome of H. humuli will provide an important resource for research regarding the management of H. humuli and for a wider range of cyst nematode investigations.

In 2022, a hopyard in Salem, Oregon was sampled for H. humuli. Briefly, soil was collected from seven plots (five soil cores/plot) using a 2.5-cm-diameter soil probe with a probe length of 30 cm (JMC, Newton, IA). The soil was mixed and placed on an aluminum tray to dry it for at least one week in a greenhouse at the Nematology Lab at USDA-ARS-HCDPMRU in Corvallis, Oregon. Cysts were extracted from dried soil using the USDA-cyst extractor (Zasada et al., 2019). Cysts (n = 115) were handpicked, placed in a petri dish, and cut to release encysted eggs. The eggs were cleaned and transferred into a 1.5 ml Eppendorf tube then filled with water to a final volume of 0.5 ml. The total number of nematodes (eggs and second-stage juveniles) was determined by counting a subsample of 50 µl. Excess water was subsequently removed by centrifugation and aspiration to a final volume of approximately 50 µl. Nematodes were shipped overnight to the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaigne, Illinois on dry ice for DNA extraction and sequencing. DNA extraction was performed as in Arora et al. (2023). Briefly, high molecular weight DNA was extracted from a total of 10,200 nematodes with the MagAttract HMW DNA kit (Qiagen, Hilden, Germany). The High-Sensitivity dsDNA kit (ThermoFisher Scientific, Waltham, MA) was used to determine DNA concentration using a Qubit (Thermo Fisher Scientific). DNA integrity was assessed using a Femto Pulse system (Agilent, Santa Clara, CA).

Purified genomic DNA (5 ng) was sheared with Megaruptor 3 (Diagenode, Denville, NJ), amplified using the PacBio Ultra-Low DNA input kit following the company’s protocol (Pacific Biosciences, Menlo Park, CA), and converted into a PacBio library with the SMRTbell Express Template Prep kit version 2.0 (PacBio Biosciences). Qubit was used for library quantification and Femto Pulse for fragment size distribution. Sequencing was carried out on 1 SMRT cell 8 M on a PacBio Sequel IIe system using the circular consensus sequencing (CCS) mode and a 30h movie time with default parameters (three minimum passes and a minimum accuracy of 99%). The sequencing yielded 14.9 GB of CCS data. The total number of reads was 2,800,608 with a mean read length of 5,327 bp.

PacBio HiFi reads were filtered with the GNU Awk program v.4.0.2 (http://www.gnu.org/licenses/) and reads less than 3 kb were removed leaving 1,791,046 reads for assembly. Hifiasm v.0.16.1-r375 (Cheng et al., 2021) with default parameters was used for genome assembly. Blobtools v.1.0 (Laetsch and Blaxter, 2017) with default parameters used to visualize de novo assembled contigs based on the read coverage and GC content. To determine coverage, raw data was mapped to the de novo assembly using minimap2 with default parameters (Li, 2018). A phylum identification was given to each contig in the assembly based on BLAST similarity (E-value < 10e−25) to sequences in the NCBI “nt” database or other PPN genomes (Heterodera carotae [NCBI BioProject:PRJNA774818], H. glycines [NCBI BioProject:PRJNA381081], H. schachtti [NCBI BioProject:PRJNA722882], Meloidogyne incognita [ENA Project:PRJEB8714], Radopholus similis [NCBI BioProject:PRJNA541590], Globodera rostochiensis [ENA Project: PRJEB13504], and Globodera pallida [ENA Project: PRJEB123]). Reads belonging to contigs with >10X coverage and identified as Nematoda or with no assigned identity (no-hit) were used to reassemble the H. humuli genome using the Hifiasm v.0.16.1-r375 algorithm. Blobtools v.1.0 was again used to visualize the re-assembly based on the read coverage and GC content as described previously. After the filtered assembly was complete, contigs identified as belonging to the phylum Nematoda or with no identity (1,487 contigs out of 1,489 contigs) were retained to make the final genome assembly for H. humuli. The Blobtools visualizations of the meta and final assemblies are in Supplemental Figs. 1 and 2, respectively. Genome statistics (Table 1) were obtained using QUAST 5.2.0 default parameters (Gurevich et al., 2013).

Table 1.

Comparison of genome assembly statistics of Heterodera humuli and three other Heterodera species.

Type of analysis Parameter H. carotae (PRJNA774818)x H. glycines (PRJNA381081)x H. humuli (PRJNA1048471)x H. schachtii (PRJNA767548)x
Genome statistics Total Number of bp 95,115,141 157,978,452 90,806,450 190,153,211
Number of Contigs 17,835 9 1,487 705
Largest contig 113,425 23,985,585 568,746 4,323,191
Contigs ≥ 25000 699 9 871 609
Contigs ≥ 50000 103 9 559 531
Average GC% 39.39 36.66 36.11 32.36
Contig N50 13,935 17,907,690 111,383 500,954
Contig L50 1,836 4 231 86
Number of N’s per 100 kbp 0 1,062.01 0 2,253.83
Predicted number coding genes 17,037 22,465 15,428 29,851
Predicted number of transcripts 17,322 29,933 16,848 31,564
Predicted Protein BUSCO Analysis (Nematoda_ob10 – 3,131 genes) Complete BUSCOs (C) (%) 1,301 (41.5%) 1,884 (60.2%) 2,030 (64.9%) 2,080 (66.4%)
Complete BUSCOs and single-copy BUSCOs (S) (%) 1,237 (39.5%) 1,612 (51.5%) 1,590 (50.8%) 1,675 (53.5%)
Complete and duplicated BUSCOs (D) (%) 64 (2%) 272 (8.7%) 440 (14.1%) 405 (12.9%)
Fragmented BUSCOs (F) (%) 124 (4%) 47 (1.5%) 66 (2.1%) 69 (2.2%)
Missing BUSCOs (M) (%) 1,706 (54.5%) 1,200 (38.3%) 1,035 (33%) 982 (31.4%)
Predicted Protein BUSCO Analysis (Eukaryota_ odb10 - 255 genes) C (%) 160 (62.8%) 199 (78%) 221 (86.6%) 224 (87.8%)
S (%) 156 (61.2%) 176 (69%) 174 (68.2%) 200 (78.4%)
D (%) 4 (1.6%) 23 (9%) 47 (18.4%) 24 (9.4%)
F (%) 52 (20.4%) 20 (7.8%) 13 (5.1%) 15 (5.9%)
M (%) 43 (16.8%) 36 (14.2%) 21 (8.3%) 16 (6.3%)
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x

NCBI GenBank BioProject number.

The objective of this genome sequencing effort was to provide high quality reference data to expand the publicly available genomic resources for cyst nematodes. The assembled genome size of H. humuli is 90,806,450 bp in a total of 1,487 contigs (Table 1) with an average 112X coverage. The genome provided in this study is smaller than the other available Heterodera spp. genomes, up to ~ 100 Mb less when compared to the H. schachtii genome (Table 1). The potential genome size and repeat length for H. humuli are estimated to be 90,077,635 and 63,372,724 bp, respectively, by using reads > 3 kb as input and a kmer size of 21 in GenomeScope (Vurture et al., 2017). The assembly has a GC content of 36.11% and a N50 of 111,383 bp (Table 1).

Completeness of the assembly of H. humuli and the genomes of the three Heterodera spp. mentioned above was determined with BUSCO v5.4.5 using the eukaryota_odb10 and nematoda_odb10 databases (Manni et al., 2021) (Supplementary Fig. 3). Heterodera humuli had the second and third most complete number of BUSCO genes when using the eukaryota_odb10 and nematoda_odb10 databases, respectively (Supplementary Fig. 3).

To annotate the genome of H. humuli, highly repetitive regions were identified and masked using RepeatModeler v 2.0.2 and RepeatMasker v 4.1.0, respectively, using default parameters (Smit and Hubley, 2015a; Smit and Hubley, 2015b). Predicted proteins from the H. glycines genome (Masonbrink et al., 2019) were used as protein hints to annotate the masked genome with BRAKER3 v 3.0.3 using default parameters (Lomsadze et al., 2005; Stanke et al., 2006; Gotoh, 2008; Stanke et al., 2008; Iwata and Gotoh, 2012; Buchfink et al., 2015; Hoff et al., 2016; Hoff et al., 2019; Bruna et al., 2020; Bruna et al., 2021). In general, H. humuli has a smaller number of predicted number of transcripts compared to the other Heterodera species included in this analysis. When compared to H. schachtii, H. humuli has 14,716 fewer transcripts (Table 1). The BUSCO analysis was also performed on the predicted proteins from all Heteredora spp. included in this study using both the nematoda_odb10 and eukaryota_odb10 databases. Heterodera humuli had the second highest complete BUSCO score with both the nematoda_odb10 (64.9 %) and eukaryota_odb10 (86.6 %) databases (Table 1).

The partial mitochondrial cox1 gene was retrieved from the H. humuli assembly using usearch11 (Edgar, 2010) with the H. humili accession number ON007077 from NCBI. Other cox1 sequences from Heterodera spp. were retrieved from NCBI, along with outgroup Rotylenchus spp., to perform a phylogenetic analysis. Sequences were aligned with the Clustal W algorithm with default parameters (Thompson et al., 1994) and trimmed using BioEdit v.7.0.5.3 (Hall, 1999). The best substitution model was determined using jModelTest 2.1.10 v20160303 with default parameters (Darriba et al., 2012) and a Bayesian phylogenetic inference was performed using MrBayes v3.2 (Ronquist et al., 2012) at the CyberInfrastructure for Phylogenetic Research (CIPRES) Science Gateway (Miller et al., 2010) with default parameters. Subsequently, FigTree v1.4.3 (Rambaut, 2016) was used to visualize the resulting phylogenetic tree. The phylogenetic analysis placed the cox1 sequence from this H. humuli assembly (coded as ptg000050c_HCN) in a clade with other H. humuli sequences with a posterior probability value of 100% (Supplementary Fig. 4). Thus, confirming the identity of the species.

Genome assembly, annotation, and raw data for H. humuli can be found at NCBI under the BioProject number PRJNA1048471 (BioSample number SAMN38640877). This genome of H. humili generated using long reads offers a valuable genetic resource for future research efforts on the biology of H. humuli, discovery of novel effectors, genomic comparison among cyst forming nematodes, and many other aspects.

Acknowledgment

We thank the personnel at the nematology lab at the USDA-ARS-HCDPMRU for technical assistance to obtain the nematodes used for this study. We are also thankful with the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaigne, and Dr. Nathan Schroeder for making this sequencing effort possible.

Supplementary Figures

Figure S1.

Figure S1.

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Visualization of the meta-assembly of Heterodera humuli genome. The Hifiasm v.0.16.1-r375 algorithm was used to assemble the reads > 3 kb from the PacBio Sequel IIe system. The Blobplot (Laetsch and Blaxter, 2017) shows assembled contigs as circles with placement of circles on the x-axis representing the proportion of GC bases and the position on the y-axis representing the coverage of the contig with the raw data.

Figure S2.

Figure S2.

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Visualization of the final assembly of Heterodera humuli using reads from the PacBio Sequel IIe system. The Hifiasm v.0.16.1-r375 algorithm was used to assemble the reads. The blobplot (Laetsch and Blaxter, 2017) shows assembled contigs as circles with placement of circles on the x-axis representing the proportion of GC bases and the position on the y-axis representing the coverage of the contig with the raw data.

Figure S3.

Figure S3.

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BUSCO assessment results of Heterodera species (NCBI GenBank BioProject number) using both databases, A) eukaryota_odb10 and B) nematoda_odb10.

Figure S4.

Figure S4.

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Phylogenetic relationships between Heterodera species as inferred from Bayesian analysis of the partial cox1 gene sequences under the GTR + I + G model. Posterior probability values are given for appropriate clades. The bold name represents the H. humuli sequence derived from this study. Sequences were retrieved from GenBank, aligned, and trimmed with BioEdit v.7.0.5.3 (Hall, 1999). The best substitution model was determined using jModelTest 2.1.10 v20160303 (Darriba et al., 2012) and the Bayesian analysis was performed using MrBayes v3.2 (Ronquist et al., 2012) at the CIPRES Science Gateway. Finally, FigTree v1.4.3 (Rambaut, 2016) was used to visualize the resulting phylogenetic tree.

References

  1. Arora D., Hernandez A.G., Walden K.K.O., Fields C.J., Yan G.. First draft genome assembly of root-lesion nematode Pratylenchus scribneri generated using long-read sequencing. International Journal of Molecular Sciences. 2023;24:7311. doi: 10.3390/ijms24087311. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bruna T., Hoff K.J., Lomsadze A., Stanke M., Borodovsky M.. BRAKER2: Automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database. NAR Genomics and Bioinformatics. 2021;3:lqaa108. doi: 10.1093/nargab/lqaa108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bruna T., Lomsadze A., Borodovsky M.. GeneMark-EP+: Eukaryotic gene prediction with self-training in the space of genes and proteins. NAR Genomics and Bioinformatics. 2020;2:lqaa026. doi: 10.1093/nargab/lqaa026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Buchfink B., Xie C., Huson D. H.. Fast and sensitive protein alignment using DIAMOND. Nature Methods. 2015;12:59–60. doi: 10.1038/nmeth.3176. [DOI] [PubMed] [Google Scholar]
  5. Cheng H., Concepcion G. T., Feng X., Zhang H., Li H.. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nature Methods. 2021;18:170–175. doi: 10.1038/s41592-020-01056-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Darling E., Núñez-Rodríguez L., Chung H., Zasada I., Quintanilla-Tornel M.. The hop cyst nematode, Heterodera humuli: History, distribution, and impact on global hop production. Phytopathology. 2023;113:142–149. doi: 10.1094/PHYTO-04-22-0121-RVW. [DOI] [PubMed] [Google Scholar]
  7. Darriba D., Taboada G.L., Doallo R., Posada D.. jModelTest 2: More models, new heuristics and parallel computing. Nature Methods. 2012;9:772. doi: 10.1038/nmeth.2109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Edgar R. C.. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–2461. doi: 10.1093/bioinformatics/btq461. [DOI] [PubMed] [Google Scholar]
  9. Gotoh O.. A space-efficient and accurate method for mapping and aligning cDNA sequences onto genomic sequence. Nucleic Acids Research. 2008;36:2630–2638. doi: 10.1093/nar/gkn105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Gremme G., Brendel V., Sparks M.E., Kurtz S.. Engineering a software tool for gene structure prediction in higher organisms. Information and Software Technology. 2005;47:965–978. [Google Scholar]
  11. Gurevich A., Saveliev V., Vyahhi N., Tesler G.. QUAST: Quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–1075. doi: 10.1093/bioinformatics/btt086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Handoo Z. A., Subbotin S. A. Perry R., Moens M., Jones J. Cyst nematodes. Wallingford: CABI; 2018. Taxonomy, identification and principal species; pp. 365–398. [Google Scholar]
  13. Hall T.A.. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series. 1999;41:95–98. [Google Scholar]
  14. Hoff K. J., Lange S., Lomsadze A., Borodovsky M., Stanke M.. BRAKER1: unsupervised RNASeq-based genome annotation with GeneMark-ET and AUGUSTUS. Bioinformatics. 2016;32:767–769. doi: 10.1093/bioinformatics/btv661. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hoff K. J., Lomsadze A., Borodovsky M., Stanke M. Gene prediction. New York: Humana; 2019. Whole-genome annotation with BRAKER; pp. 65–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Iwata H., Gotoh O.. Benchmarking spliced alignment programs including Spaln2, an extended version of Spaln that incorporates additional species-specific features. Nucleic Acids Research. 2012;40:e161–e161. doi: 10.1093/nar/gks708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Jones J., Haegeman A., Danchin E., Gaur H., Helder J., Jones M., Kikuchi T., Manzanilla R., Palomares J., Wesemael W., Perry N.. Review: Top 10 plant-parasitic nematodes in molecular plant pathology. Molecular Plant Pathology. 2013;14:946–961. doi: 10.1111/mpp.12057. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Laetsch D.R., Blaxter M.L.. BlobTools: Interrogation of genome assemblies. F1000Research. 2017;6:1287. [Google Scholar]
  19. Li H.. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34:3094–3100. doi: 10.1093/bioinformatics/bty191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lomsadze A., Ter-Hovhannisyan V., Chernoff Y. O., Borodovsky M.. Gene identification in novel eukaryotic genomes by self-training algorithm. Nucleic Acids Research. 2005;33:6494–6506. doi: 10.1093/nar/gki937. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Manni M., Berkeley M. R., Seppey M., Simão F. A., Zdobnov E.. M. BUSCO Update: Novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Molecular Biology and Evolution. 2021;38:4647–4654. doi: 10.1093/molbev/msab199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Miller M.A., Pfeiffer W., Schwartz T.. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. Proceedings of the Gateway Computing Environments Workshop. 2010:1–8. doi: 10.1109/GCE.2010.5676129. [DOI] [Google Scholar]
  23. Masonbrink R., Maier T. R., Muppirala U., Seetharam A. S., Lord E., Juvale P. S., Schmutz J., Johnson N. T., Korkin D., Mitchum M. G., Mimee B., Eves-van den Akker S., Hudson M., Severin A. J., Baum T. J.. The genome of the soybean cyst nematode (Heterodera glycines) reveals complex patterns of duplications involved in the evolution of parasitism genes. BMC Genomics. 2019;20:119. doi: 10.1186/s12864-019-5485-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Rambaut A. FigTree v1.4.3: Tree Figure Drawing Tool. 2016. Available at: http://tree.bio.ed.ac.uk/software/figtree/
  25. Ronquist F., Teslenko M., van der Mark P., Ayres D.L., Darling A., Höhna S., Larget B., Liu L., Suchard M.A., Huelsenbeck J.P.. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Systematic Biology. 2012;61:539–42. doi: 10.1093/sysbio/sys029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Siddique S., Radakovic Z. S., Hiltl C., Pellegrin C., Baum T. J., Beasley H., Bent A. F., Chitambo O., Chopra D., Danchin E. G. J., Grenier E., Habash S. S., Hasan M. S., Helder J., Hewezi T., Holbein J., Holterman M., Janakowski S., Koutsovoulos G. D., Kranse O.P., Lozano-Torres J.L., Maier T.R., Masonbrink R.E., Mendy B., Riemer E., Sobczak M., Sonawala U., Sterken M.G., Thorpe P., van Steenbrugge J.M.M., Zahid N., Grundler F., Eves-van den Akker S.. The genome and life stage-specific transcriptomes of a plant-parasitic nematode and its host reveal susceptibility genes involved in trans-kingdom synthesis of vitamin B5. Nature Communications. 2022;13:6190. doi: 10.1038/s41467-022-33769-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Smit A.F.A., Hubley R. RepeatModeler Open-1.0 (2008–2015) 2015a. http://www.repeatmasker.org.
  28. Smit A.F.A., Hubley R. RepeatMasker Open-4.0 (2013–2015) 2015b. http://www.repeatmasker.org.
  29. Stanke M., Diekhans M., Baertsch R., Haussler D.. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008;24:637–644. doi: 10.1093/bioinformatics/btn013. [DOI] [PubMed] [Google Scholar]
  30. Stanke M., Schöffmann O., Morgenstern B., Waack S.. Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources. BMC Bioinformatics. 2006;7:62. doi: 10.1186/1471-2105-7-62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Wram C., Hesse C., Handoo Z., Pacheco H., Zasada I.. Genome announcement: the draft genome of the carrot cyst nematode Heterodera carotae. Journal of Nematology. 2022;54:e2022–1. doi: 10.2478/jofnem-2022-0014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Wright D., Perry R. Perry R., Moens M. Plant Nematology. Wallingford: CABI; 2006. Reproduction, physiology and biochemistry; pp. 188–209. [Google Scholar]
  33. Thompson J., Higgins D., Gibson T.. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research. 1994;22:4673–4680. doi: 10.1093/nar/22.22.4673. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Vurture G. W., Sedlazeck F. J., Nattestad M., Underwood C. J., Fang H., Gurtowski J., Schatz M. C.. GenomeScope: Fast reference free genome profiling from short reads. Bioinformatics. 2017;33:2202–2204. doi: 10.1093/bioinformatics/btx153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Zasada I. A., Ingham R.E., Baker H., Phillips W.S.. Impact of Globodera ellingtonae on yield of potato (Solanum tuberosum) Journal of Nematology. 2019;51 doi: 10.21307/jofnem-2019-073. [DOI] [PMC free article] [PubMed] [Google Scholar]

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