Method for identification of biological entities comprising high-throughput sequencing, taxonomic assignment, genomic assembly, and functional annotation

Abstract

Provided herein are methods for identifying biological entities, such as microorganisms, in biological samples. The method may comprise obtaining a plurality of sequence reads via high throughput sequencing of nucleic acids from the biological samples, assembling the plurality of sequence reads into contigs, generating a consensus scaffold sequence corresponding to each biological entity from the contigs, calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity. Also provided herein are kits and systems for carrying out said methods.

Description

METHOD FOR IDENTIFICATION OF BIOLOGICAL ENTITIES COMPRISING HIGH-THROUGHPUT SEQUENCING, TAXONOMIC ASSIGNMENT, GENOMIC ASSEMBLY, AND FUNCTIONAL ANNOTATIONFIELD

[0001] The present disclosure relates generally to the field of bioinformatics, and more specifically to methods, kits, and systems for the detection of biological entities in biological samples using de novo assembly and functional annotation of nucleic acid sequences obtained from the sample.BACKGROUND

[0002] Microscopic biological entities such as microorganisms (Bacteria, Fungi, and Archaea) and agents such as viruses and viroids have inhabited the Earth since its origins, colonizing all kinds of environments. They play a vital role in all ecosystems and biogeochemical cycles of the planet. Moreover, they are key entities for the functioning of all biological systems and are in constant interaction with plants, animals, and humans, establishing beneficial and / or harmful relationships with them.

[0003] All the activity of a biological entity (related to its development or functioning in the environment) is encoded in its genome. The study of genes as a whole and their expression has led scientists to decipher a genome using modern techniques of sequencing, also known as NGS or more appropriately HTS (N_ext Generation Sequencing and High-Throughput Sequencing, respectively). These novel tools have implications of great utility in the fields of medicine, agriculture, and industry.

[0004] NGS / HTS techniques allow reading in parallel from millions to billions of DNA sequences or fragments (DNA reads) present in genomes and have revolutionized the way microscopic biological entities are studied, moving from an exclusively laboratoryenvironment to a computational one, with the unavoidable application of bioinformatics. In particular, these sequencing methods provide unprecedented capabilities for the detection (by presence or absence) of biological entities and for the diagnosis or recognition of their essential activities or interactions with higher organisms (plants, humans, and / or animals), which can be established in a beneficial way (e.g., promotion of growth in plants, synthesis of essentialmicronutrients for humans and animals, etc.) or a harmful way (e.g., infections or diseases caused by pathogens).

[0005] NGS / HTS methods allow for unbiased and hypothesis-free testing from environmental samples, and are becoming increasingly accessible, with sequencing prices decreasing drastically over the last decade. The detection of microorganisms and / or biological agents by NGS / HTS involves: (1) total nucleic acid extraction (DNA and RNA) from the material to be studied, (2) library preparation involving enrichment and / or depletion of host sequences (e.g., to increase viral RNA sequences or decrease highly abundant sequences like ribosomal RNA, respectively), (3) high-throughput sequencing, (4) raw data quality control and elimination of low-quality reads and adapter sequences, (5) removal of host reads, (6) mapping to a database containing sequences of biological agents and / or de novo assembly of reads, (7) read counting and / or coverage calculation, (8) identification of microorganisms and / or biological agents present through read cut-offs and coverage (Villamor et al., 2019. High throughput sequencing for plant virus detection and discovery. Phytopathology, 109(5), pp.716-725.). Specifically, the bioinformatic identification process is essential for accurate molecular diagnosis.

[0006] The potential of NGS / HTS methods has been widely recognized by professionals in medicine, the food industry, pharmacology, and several phytosanitary agencies, who, in turn, have held scientific committees and workshops to address how to implement these techniques and the challenges involved. In particular, some international organizations for the protection of plant diseases demonstrate great interest in improving the availability of diagnostic tools and are currently reviewing their diagnostic standards (Adams et al., 2018. The impact of high throughput sequencing on plant health diagnostics. E r J. Plant Pathol., 152, pp.909-919; Jones and Naidu, 2019. Global Dimensions of Plant Virus Diseases: Current Status and Future Perspectives. Annu. Rev. Virol. 6(1), pp.387-409).

[0007] The successful implementation of NGS / HTS-based diagnosis depends largely on the appropriate computational infrastructure and bioinformatics expertise (Jones et al., 2017. Viral Diagnostics in Plants Using Next Generation Sequencing: Computational Analysis in Practice. Front. Plant Sci. 8, p.1770; Umer et al., 2019. Genomic, Morphological and Biological Traits of the Viruses Infecting Major Fruit Trees. Viruses, 11(6), p.515; Kutnjak et al., 2021. A Primer on the Analysis of High-Throughput Sequencing Data for Detection of Plant Viruses. Microorganisms 9(4), pp.841). The majority of diagnostic tools for biological agents have focused on human clinical samples, such as VirusFinder and VERSE (Wang et al., 2013. VirusFinder: Software for Efficient and Accurate Detection of Viruses and Their IntegrationSites in Host Genomes through Next Generation Sequencing Data. PLoS ONE 8(5), c64465; Wang et ah, 2015. VERSE: A novel approach to detect virus integration in host genomes through reference genome customization. Genome Med. 7, p.2), VIP (Li et ah, 2016. VIP: an integrated pipeline for metagenomics of virus identification and discovery. Sci. Rep. 6), and VirusSeeker (Zhao et al., 2017. VirusSeeker, a computational pipeline for virus discovery and virome composition analysis. Virology. 18(503), pp.21-30), with a smaller proportion focused on plants, such as VirFind (Ho and Tzanetakis, 2014. Development of a virus detection and discovery pipeline using next generation sequencing. Virology, 471-473, pp.54-60), the VSD toolkit (Barrero et al., 2017. An internet-based bioinformatics toolkit for plant biosecurity diagnosis and surveillance of viruses and viroids. BMC Bioinform. 18(1), p.26), Virtool (Rott et al., 2017. Application of Next Generation Sequencing for Diagnostic Testing of Tree Fruit Viruses and Viroids. Plant Dis. 101(8), pp.1489-1499), and PVDP (Gutierrez et al., 2021. PVDP: A portable open source pipeline for detection of plant viruses in RNAseq data. A case study on potato viruses in Antioquia (Colombia). Physiol. Mol. Plant Pathol. 113, p.101604). However, there is a lack of standardization in the analysis processes, which even differ in the metrics used for detection, such as read counts (Soltani et al., 2021 Quality Assessment and Validation of High-Throughput Sequencing for Grapevine Virus Diagnostics. Viruses. 13(6), p.1130; Hanafi et al., 2022. Detection of Banana Mild Mosaic Virus in Musa In Vitro Plants: High-Throughput Sequencing Presents Higher Diagnostic Sensitivity Than (IC)-RT-PCR and Identifies a New Betaflexiviridae Species. Plants (Basel, Switzerland), 11(2), p.226), genome coverage (Visser et al., 2016. Next-generation sequencing for virus detection: covering all the bases. Virol. J. 13(1), p.85; Malapi-Wight et al., 2021. HTS-Based Diagnostics of Sugarcane Viruses: Seasonal Variation and Its Implications for Accurate Detection. Viruses. 13(8), 62T), or a combination of both criteria (Rott et al., 2017. Application of Next Generation Sequencing for Diagnostic Testing of Tree Fruit Viruses and Viroids. Plant Dis. 101(8), pp.1489-1499). Additionally, the harmonization of detection thresholds to establish NGS / HTS-based detection of biological agents is still pending (Ruiz-Garcia, et al., 2021. Characterization of Spanish Olive Virome by High Throughput Sequencing Opens New Insights and Uncertainties. Viruses. 13(11), p.2233), particularly in cases with low sequencing coverage.SUMMARY

[0008] An innovative contribution, which is provided herein, is the incorporation of functional elements encoded in the genome of a microorganism and / or biological agent into the identification method. That is, it is possible to identify enzymes that account for the functional activity of a microorganism and / or biological agent and combine it with metrics and cutoff criteria to make a highly accurate diagnosis. For example, the main genomes of plant viruses comprise RNA and have a basic replication mechanism that consists of the RdRp enzyme (RNA-dependent RNA polymerase) responsible for the transcription and replication of RNA viruses (Hull, 2014. Replication of Plant Viruses. Plant Virology, pp.341-421). In other words, the identification of such an essential biological functionality in viruses can help interpret cases of low sequencing coverage, as it implies the capacity for propagation and, therefore, potential infectivity.

[0009] In some aspects, provided herein is a methodology for the identification and / or detection of microscopic biological agents, particularly including (but not limited to) viral agents (viruses and viroids), based on massive nucleic acid sequencing and biological functionality criteria present in these biological agents. This methodology involves obtaining massive sequencing data from one or more samples using methods including (but not limited to) NGS or HTS technologies. The obtained data (e.g., reads of DNA or RNA) are processed, filtered, and purified, and then subjected to taxonomic classification or assignment. The data thus classified are then used for the total or partial assembly of genomes to obtain contigs. These contigs are compared against reference genomes, from which consensus sequences are obtained and used for completeness calculations and the detection of viral functional evidence (e.g., essential protein coding such as polymerase or replicase). Alternatively, taxonomic assignment can be carried out after assembly for classification of already assembled contigs, which are then compared against reference genomes. The identification method is completed by detecting the presence / absence of functional elements that account for the biological activity of the biological agent (e.g., replicases in the case of viruses). Finally, the combination of genome completeness and the presence of a functional element are used to identify with 99% efficiency and according to cut-off thresholds.

[0010] The following enumerated embodiments are representative of some aspects of the invention.1. A computer-implemented method for identifying a presence of one or more biological entities in a biological sample, the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained from the biological sample; assembling, using the one or more processors, the plurality of sequence reads into a plurality of contigs based on the sequence read data; generating, using the one or more processors, an assignment for each sequence read and / or each contig based on a comparison of the plurality of sequence reads and / or the plurality of contigs to a first reference sequence database comprising reference sequences for the one or more biological entities; aligning, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequence and generating a consensus scaffold sequence corresponding to each reference sequence; and identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.2. The computer- implemented method of embodiment 1, comprising: identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each of the one or more consensus scaffold sequences, and functionally annotating each of the one or more consensus scaffold sequences to identify a functional element of the corresponding biological entity.3. The computer- implemented method of embodiment 1 or 2, wherein the method comprises generating, using the one or more processors, an assignment for each sequence read based on a comparison of the plurality of sequence reads to the first reference sequence database comprising reference sequences for the one or more biological entities.4. The computer-implemented method of any one of embodiments 1-3, wherein the method comprises generating, using the one or more processors, an assignment for eachcontig based on a comparison of the plurality of contigs to the first reference sequence database comprising reference sequences for the one or more biological entities.5. The computer- implemented method of any one of embodiments 1-4, further comprising processing the plurality of sequence reads prior to assembling the plurality of sequence reads into contigs.6. The computer-implemented method of embodiment 5, wherein the processing comprises generating, using the one or more processors, a pre- assignment for each sequence read based on a comparison of the plurality of sequence reads to the first reference sequence database or a second reference sequence database, wherein the first or second reference sequence database comprises reference sequences for a plurality of taxa and pre-assigning each sequence read to a taxon based on the comparison.7. The computer- implemented method of embodiment 6, wherein the method further comprises identification and removal of one or more background sequence reads from the plurality of sequence reads based on the pre-assignment.8. The computer- implemented method of 7, wherein the background sequence reads comprise sequence reads corresponding to plant nucleic acid sequences, animal nucleic acid sequences, or a combination thereof.9. The computer- implemented method of embodiment 8, wherein the background sequence reads comprise sequence reads corresponding to plant nucleic acid sequences.10. The computer-implemented method of embodiment 8, wherein the background sequence reads comprise sequence reads corresponding to human nucleic acid sequences.11. The computer- implemented method of any one of embodiments 1-52, wherein the plurality of sequence reads comprises or consists essentially of sequence reads obtained from high-throughput sequencing.12. The computer- implemented method of embodiment 11, wherein the plurality of sequence reads are obtained from high-throughput sequencing using Illumina sequencing, Oxford Nanopore sequencing, Pacific Biosciences sequences, or any combination thereof.13. A computer-implemented method for identifying a presence of one or more biological entities in a biological sample, the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained from high-throughput sequencing of nucleic acids from the biological sample; processing and filtering, using the one or more processors, the sequence reads to generate filtered sequence read data for a plurality of filtered sequence reads;generating, using the one or more processors, an assignment for each filtered sequence read based on a comparison of the plurality of filtered sequence reads to a first reference sequence database comprising reference sequences for the one or more biological entities; assembling, using the one or more processors, the plurality of filtered sequence reads into a plurality of contigs based on the filtered sequence read data; aligning, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequences and generating a consensus scaffold sequence corresponding to each reference sequence; and identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.14. The computer- implemented method of any one of embodiments 1-13, wherein the method comprises obtaining the plurality of sequence reads from the biological sample.15. The computer-implemented method of any one of embodiments 1-14, wherein the method comprises sequencing a plurality of nucleic acid sequences extracted from the biological sample to obtain the plurality of sequence reads.16. The computer- implemented method of any one of embodiments 1-15, wherein the method comprises obtaining the biological sample from a host organism.17. The computer-implemented method of embodiment 16, wherein the host organism is an animal or a plant.18. The computer-implemented method of embodiment 17, wherein the host organism is a plant.19. The computer- implemented method of any one of embodiments 1-18, wherein the method further comprises generating, using the one or more processors, a report indicating the identified presence of the one or more biological entities in the biological sample.20. The computer- implemented method of embodiment 19, further comprising transmitting the report to a third party.21. The computer- implemented of embodiment 20, wherein the report is transmitted via a computer network or a peer-to-peer connection.22. The computer- implemented method of any one of embodiments 1-21, wherein the plurality of sequence reads comprise or consist essentially of reads obtained by high- throughput DNA sequencing.23. The computer- implemented method of any one of embodiments 1-21, wherein the plurality of sequence reads comprise or consist essentially of reads obtained by high- throughput RNA sequencing.24. The computer- implemented method of any one of embodiments 5-23, wherein the processing comprises generating, using the one or more processors, a pre- assignment for each sequence read based on a comparison of the plurality of sequence reads to the first reference sequence database or a second reference sequence database, wherein the first or second reference sequence database comprises reference sequences for a plurality of taxa and preassigning each sequence read to a taxon based on the comparison.25. The computer- implemented method of embodiment 24, wherein the first or second reference sequence database comprises reference sequences of the one or more biological entities and reference sequences of one or more additional organisms.26. The computer- implemented method of embodiment 25, wherein the one or more additional organisms comprise animals, plants, fungi, or a combination thereof.27. The computer- implemented method of embodiment 26, wherein the one or more additional organisms comprise one or more plants.28. The computer-implemented method of any one of embodiments 13-27, wherein the filtering comprises identifying and removing, using the one or more processors, one or more background sequence reads from the plurality of sequence reads based on the pre-assignment.29. The computer-implemented method of embodiment 28, wherein the one or more background sequence reads are identified and removed based on pre-assignment to the one or more additional organisms.30. The computer-implemented method of 28 or 29, wherein the background sequence reads comprise sequence reads corresponding to plant nucleic acid sequences, animal nucleic acid sequences, or a combination thereof.31. The computer- implemented method of embodiment 30, wherein the background sequence reads comprise sequence reads corresponding to plant nucleic acid sequences.32. The computer-implemented method of embodiment 30, wherein the background sequence reads comprise sequence reads corresponding to human nucleic acid sequences.33. The computer-implemented method of any one of embodiments 1-32, wherein the one or more biological entities comprise or consist essentially of microorganisms.34. The computer-implemented method of embodiment 14, wherein the microorganisms comprise commensal or beneficial microorganisms.35. The computer-implemented method of embodiment 14 or 34, wherein the microorganisms comprise pathogenic microorganisms.36. The computer- implemented method of embodiment 35, wherein the pathogenic microorganisms comprise bacteria, viruses, viroids, or any combination thereof.37. The computer-implemented method of embodiment 35, wherein the pathogenic microorganisms comprise viruses and / or viroids.38. The computer-implemented method of any one of embodiments 35-37, wherein the pathogenic microorganisms comprise pathogens of one or more plants and / or one or more animals.39. The computer- implemented method of embodiment 38, wherein the pathogenic microorganisms comprise pathogens of one or more animals.40. The computer-implemented method of embodiment 39, wherein the pathogenic microorganisms comprise pathogens of humans.41. The computer- implemented method of embodiment 38, wherein the pathogenic microorganisms comprise pathogens of one or more plants.42. The computer- implemented method of embodiment 41, wherein the pathogenic microorganisms comprise viruses and / or viroids that infect one or more plants.43. The computer- implemented method of any one of embodiments 1-42, wherein generating the assignment, generating the pre-assignment, or both is performed by one or more assignment algorithms.44. The computer- implemented method of embodiment 43, wherein the one or more assignment algorithms comprise one or more algorithms that generate assignments based on the comparison or search of small nucleotide sequences.45. The computer- implemented method of embodiment 43 or 44, wherein the one or more assignment algorithms comprise one or more algorithms that generate assignments based on sequence comparison through alignment.46. The computer- implemented method of any one of embodiments 43-45, wherein generating the assignment, generating the pre-assignment, or both is performed using two, three, or more assignment algorithms.47. The computer- implemented method of embodiment 46, wherein the assignment or pre-assignmentis generated based on the consensus of the two, three, or more assignment algorithms.48. The computer-implemented method of any one of embodiments 43-47, wherein the one or more assignment algorithms comprise Kraken2, Centrifuge, Minimap2, or a combination thereof.49. The computer-implemented method of any one of embodiments 1-48, wherein the first reference sequence database, the second reference sequence database, or both comprise nucleic acid sequences of bacteria, viruses, viroids, or any combination thereof.50. The computer-implemented method of any one of embodiments 1-48, wherein the first reference sequence database, the second reference sequence database, or both comprise nucleic acid sequences of viruses and viroids.51. The computer- implemented method of any one of embodiments 1-50, wherein the assembly of the plurality of sequence reads into contigs is performed using at least one de novo assembly algorithm.52. The computer- implemented method of embodiment 51, wherein the de novo assembly algorithm is designed for the assembly of short reads, long reads, or a combination thereof.53. The computer-implemented method of any one of embodiments 1-52, wherein one or more of the reference sequences comprise two or more segments.54. The computer-implemented method of any one of embodiments 1-53, wherein the one or more reference sequences comprise whole genome sequences.55. The computer-implemented method of embodiment 54, wherein one or more of the whole genome sequences comprise two or more segments, each segment representing a different chromosome.56. The computer-implemented method of any one of embodiments 1-55, wherein the one or more reference sequences comprise nucleic acid sequences of microorganisms.57. The computer-implemented method of embodiment 56, wherein the microorganisms comprise bacteria, viruses, viroids, or any combination thereof.58. The computer-implemented method of embodiment 56, wherein the microorganisms comprise viruses and / or viroids.59. The computer- implemented method of any one of embodiments 1 -Error! Reference source not found., wherein the completeness value for each consensus scaffold sequence is calculated based on the vertical coverage of the corresponding reference sequence by the consensus scaffold sequence.60. The computer-implemented method of any one of embodiments 1-59, wherein the completeness value for consensus scaffold sequence is calculated as the percentage ofnucleotide positions of the corresponding reference sequence having a vertical coverage of > 1 by the consensus scaffold sequence.61. The computer- implemented method of any one of embodiments 1-60, wherein calculating the completeness value for each consensus scaffold sequence comprises aligning the consensus scaffold sequence to the corresponding reference sequence, wherein the completeness value is calculated as the percentage of nucleotide positions of the reference sequence having a vertical coverage of > 1 by the aligned consensus scaffold sequence.62. The computer-implemented method of any one of embodiments 1-61, wherein the completeness value for each consensus scaffold sequence is calculated according to the equation:wherein CBi is the number of nucleotide positions of segment z of the corresponding reference sequence having a vertical coverage of > 1, and wherein SL, is the total number of nucleotide positions in segment z.63. The computer- implemented method of any one of embodiments 1-62, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.1 or 10%.64. The computer-implemented method of any one of embodiments 1-62, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.2 or 20%.65. The computer- implemented method of any one of embodiments 1-62, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.3 or 30%.66. The computer-implemented method of any one of embodiments 1-65, wherein the functional annotation of the consensus scaffold sequence indicates the presence of one or more functional elements of the corresponding biological entity.67. The computer- implemented method of embodiment 66, wherein the one or more functional elements comprise a nucleic acid sequence encoding a protein.68. The computer-implemented method of embodiment 67, wherein the protein is required for the survival of the biological entity, the replication of the biological entity, or both.69. The computer- implemented method of embodiment 66 or 67, wherein the protein is an enzyme.70. The computer-implemented method of embodiment 69, wherein the one or more functional elements comprise one or more nucleic acid sequences encoding a DNA replicase, an RNA replicase, a DNA polymerase, or an RNA polymerase.71. The computer- implemented method of embodiment 69, wherein the one or more functional elements comprise one or more nucleic acid sequences each encoding an RNA- dependent RNA polymerase (RdRp), an RNA-dependent DNA polymerase (RdDp), a DNA- dependent DNA polymerase (DdDp), a reverse transcriptase, or any combination thereof.72. The computer- implemented method of any one of embodiments 66-71, wherein a particular biological entity is identified as present in the biological sample if the one or more functional elements are present in the corresponding consensus scaffold sequence.73. The computer- implemented method of embodiments 66-71, wherein the biological entity is identified as present in the biological sample if the completeness value is greater than 0.1 and the one or more functional elements are present in the corresponding consensus scaffold sequence.74. A kit for identifying a presence of one or more biological entities in a biological sample, the kit comprising: one or more containers for holding one or more biological samples comprising nucleic acids; one or more solutions for the isolation and / or preservation of the nucleic acids within the one or more containers, wherein said one or more solutions comprise at least one of a lysis buffer solution and an elution buffer solution; and instructions for the using the kit to perform a method for identifying the presence of the one or more biological entities in the biological sample, the method comprising: extracting nucleic acids from the biological sample using the one or more containers and the one or more solutions; sequencing the extracted nucleic acids to obtain a plurality of sequence reads; receiving, at one or more processors, sequence read data for the plurality of sequence reads obtained from the biological sample;assembling, using the one or more processors, the plurality of sequence reads into a plurality of contigs based on the sequence read data; generating, using the one or more processors, an assignment for each sequence read and / or each contig based on a comparison of the plurality of sequence reads and / or the plurality of contigs to a first reference sequence database comprising reference sequences for the one or more biological entities; aligning, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequences and generating a consensus scaffold sequence corresponding to each reference sequence; and identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.75. A kit for identifying a presence of one or more biological entities in a biological sample, the kit comprising: one or more containers for holding one or more biological samples comprising nucleic acids; one or more solutions for the isolation and / or preservation of the nucleic acids within the one or more containers, wherein said one or more solutions comprise at least one of a lysis buffer solution and an elution buffer solution; and instructions for the using the kit to perform a method for identifying the presence of the one or more biological entities in the biological sample, the method comprising: extracting nucleic acids from the biological sample using the one or more containers and the one or more solutions; sequencing the extracted nucleic acids using high-throughput sequencing to obtain a plurality of sequence reads; receiving, at one or more processors, sequence read data for the plurality of sequence reads; processing and filtering, using the one or more processors, the sequence reads to generate filtered sequence read data for a plurality of filtered sequence reads; generating, using the one or more processors, an assignment for each filtered sequence read based on a comparison of the plurality of filtered sequence reads to afirst reference sequence database comprising reference sequences for the one or more biological entities; assembling, using the one or more processors, the plurality of filtered sequence reads into a plurality of contigs based on the filtered sequence read data; aligning, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequences and generating a consensus scaffold sequence corresponding to each reference sequence; and identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.76. The kit of embodiment 74 or 75, wherein the biological sample comprises nucleic acids derived from the biological entities and nucleic acids derived from plants, nucleic acids derived from animals, or a combination thereof.77. The kit of embodiment 76, wherein the biological sample comprises nucleic acids derived from the biological entities and nucleic acids derived from plants.78. The kit of embodiment 76 or 77, wherein the biological sample comprises nucleic acids derived from the biological entities and nucleic acids derived from humans.79. The kit of any one of embodiments 74-78, wherein the kit further comprises a binding matrix for the retention of genetic material.80. The kit of any one of embodiments 74-79, wherein the biological entities comprise or consist essentially of microorganisms.81. The kit of embodiment 80, wherein the microorganisms comprise commensal or beneficial microorganisms.82. The kit of embodiment 80 or 81, wherein the microorganisms comprise pathogenic microorganisms.83. The kit of embodiment 82, wherein the pathogenic microorganisms comprise bacteria, viruses, viroids, or any combination thereof.84. The kit of embodiment 82, wherein the pathogenic microorganisms comprise viruses and / or viroids.85. The kit of any one of embodiments 82-84, wherein the pathogenic microorganisms comprise pathogens of plants, pathogens of animals, or a combination thereof.86. The kit of embodiment 85, wherein the pathogenic microorganisms comprise pathogens of plants.87. The kit of embodiment 85 or 86, wherein the pathogenic microorganisms comprise pathogens of humans.88. The kit of any one of embodiments 82-87, wherein the pathogenic microorganisms comprise viruses and / or viroids that infect plants.89. The kit of any one of embodiments 74-88, wherein the plurality of sequence reads are obtained from high-throughput sequencing using Illumina sequencing, Oxford Nanopore sequencing, Pacific Biosciences sequences, or any combination thereof.90. The kit of any one of embodiments 74-89, wherein the method comprises calculating the completeness value for each consensus scaffold sequence based on the vertical coverage of the corresponding reference sequence by the consensus scaffold sequence.91. The kit of any one of embodiments 74-90, wherein the method comprises calculating the completeness value for each consensus scaffold sequence as the percentage of nucleotide positions of the corresponding reference sequence having a vertical coverage of > 1 by the consensus scaffold sequence.92. The kit of any one of embodiments 74-91, wherein the method comprises calculating the completeness value for each consensus scaffold sequence by aligning the consensus scaffold sequence to the corresponding reference sequence, wherein the completeness value is calculated as the percentage of nucleotide positions of the reference sequence having a vertical coverage of > 1 by the aligned consensus scaffold sequence.93. The kit of any one of embodiments 74-92, wherein the method comprises identifying a particular biological entity as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.1 or 10%.94. The kit of any one of embodiments 74-92, wherein the method comprises identifying a particular biological entity as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.2 or 20%.95. The kit of any one of embodiments 74-92, wherein the method comprises identifying a particular biological entity as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.3 or 30%.96. The kit of any one of embodiments 74-95, wherein the functional annotation of the consensus scaffold sequence indicates the presence of one or more functional elements of the corresponding biological entity.97. The kit of embodiment 96, wherein the one or more functional elements comprise a nucleic acid sequence encoding a protein.98. The kit of embodiment 97, wherein the protein is required for the survival of the biological entity, the replication of the biological entity, or both.99. The kit of embodiment 97-98, wherein the protein is an enzyme.100. The kit of embodiment 98, wherein the one or more functional elements comprise one or more nucleic acid sequences encoding a DNA replicase, an RNA replicase, a DNA polymerase, or an RNA polymerase.101. The kit of embodiment 98, wherein the one or more functional elements comprise one or more nucleic acid sequences each encoding an RNA-dependent RNA polymerase (RdRp), an RNA-dependent DNA polymerase (RdDp), a DNA-dependent DNA polymerase (DdDp), a reverse transcriptase, or any combination thereof.102. The kit of any one of embodiments 66-71, wherein the method comprises identifying a particular biological entity as present in the biological sample if the one or more functional elements are present in the corresponding consensus scaffold sequence.103. The kit of embodiments 74-102, wherein the method comprises identifying a particular biological entity as present in the biological sample if the completeness value is greater than 0.1 and the one or more functional elements are present in the corresponding consensus scaffold sequence.104. A system for identifying a presence of one or more biological entities in a biological sample, the system comprising: one or more processors; and a memory comprising instructions executable by the one or more processors that, when executed by the one or more processors, cause the system to: receive, at the one or more processors, sequence read data for a plurality of sequence reads obtained from the biological sample; assemble, using the one or more processors, the plurality of sequence reads into a plurality of contigs based on the sequence read data; generate, using the one or more processors, an assignment for each sequence read and / or each contig based on a comparison of the plurality of sequence reads and / or the plurality of contigs to a first reference sequence database comprising reference sequences for the one or more biological entities;align, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequences and generate a consensus scaffold sequence corresponding to each reference sequence; and identify, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.105. A system for identifying a presence of one or more biological entities in a biological sample, the system comprising: one or more processors; and a memory comprising instructions executable by the one or more processors that, when executed by the one or more processors, cause the system to: receive, at one or more processors, sequence read data for a plurality of sequence reads obtained from high-throughput sequencing of nucleic acids from the biological sample; process and filter, using the one or more processors, the sequence reads to generate filtered sequence read data for a plurality of filtered sequence reads; generate, using the one or more processors, an assignment for each filtered sequence read based on a comparison of the plurality of filtered sequence reads to a first reference sequence database comprising reference sequences for the one or more biological entities; assemble, using the one or more processors, the plurality of filtered sequence reads into a plurality of contigs based on the filtered sequence read data; align, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequences and generate a consensus scaffold sequence corresponding to each reference sequence; and identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each consensus scaffold sequence, and / orfunctionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.106. The system of embodiment 104 or 105, wherein the completeness value for each consensus scaffold sequence is calculated based on the vertical coverage of the corresponding reference sequence by the consensus scaffold sequence.107. The system of any one of embodiments 104-106, wherein the completeness value for consensus scaffold sequence is calculated as the percentage of nucleotide positions of the corresponding reference sequence having a vertical coverage of > 1 by the consensus scaffold sequence.108. The system of any one of embodiments 104-107, wherein calculating the completeness value for each consensus scaffold sequence comprises aligning the consensus scaffold sequence to the corresponding reference sequence, wherein the completeness value is calculated as the percentage of nucleotide positions of the reference sequence having a vertical coverage of > 1 by the aligned consensus scaffold sequence.109. The system of any one of embodiments 104-108, wherein the completeness value for each consensus scaffold sequence is calculated according to the equation:wherein CBi is the number of nucleotide positions of segment z of the corresponding reference sequence having a vertical coverage of > 1, and wherein SL, is the total number of nucleotide positions in segment z.110. The system of any one of embodiments 104-109, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.1 or 10%.111. The system of any one of embodiments 104-109, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.2 or 20%.112. The system of any one of embodiments 104-109, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.3 or 30%.113. The system of any one of embodiments 104-112, wherein the functional annotation of the consensus scaffold sequence indicates the presence of one or more functional elements of the corresponding biological entity.114. The system of embodiment 113, wherein the one or more functional elements comprise a nucleic acid sequence encoding a protein.115. The system of embodiment 114, wherein the protein is required for the survival of the biological entity, the replication of the biological entity, or both.116. The system of embodiment 113 or 114, wherein the protein is an enzyme.117. The system of embodiment 115, wherein the one or more functional elements comprise one or more nucleic acid sequences encoding a DNA replicase, an RNA replicase, a DNA polymerase, or an RNA polymerase.118. The system of embodiment 115, wherein the one or more functional elements comprise one or more nucleic acid sequences each encoding an RNA-dependent RNA polymerase (RdRp), an RNA-dependent DNA polymerase (RdDp), a DNA-dependent DNA polymerase (DdDp), a reverse transcriptase, or any combination thereof.119. The system of any one of embodiments 104-118, wherein a particular biological entity is identified as present in the biological sample if the one or more functional elements are present in the corresponding consensus scaffold sequence.120. The system of embodiments 104-118, wherein the biological entity is identified as present in the biological sample if the completeness value is greater than 0.1 and the one or more functional elements are present in the corresponding consensus scaffold sequence.DESCRIPTION OF FIGURES

[0011] FIGURES 1A-1B. Description of the methodology described in the present disclosure. FIGURE 1A shows the methodology disclosed herein, which involves the taxonomic assignment of reads from nucleic acid sequencing data (e.g. Illumina and / or Oxford Nanopore) using at least one read assignment algorithm against a database of biological entity sequences. The mapped reads are then collected and used for de novo assembly using assembly algorithms. The resulting contigs are used for reference-based mapping to obtain a consensus scaffold sequence, which is used to calculate completeness. The methodology then allows for the determination of the presence of functional elements encoded in the consensus scaffold sequence (e.g. replicases and / or polymerases). Finally, both completeness and the presence of functional elements are used for identification of the biological entity / entities according to certain thresholds. FIGURE IB illustrates the processing of reads throughout the steps of the methodology.

[0012] FIGURES 2A-2D. Read assignment and completeness in synthetic data. Synthetic NGS / HTS data were generated to simulate real samples, corresponding to paired-end reads of 150 nucleotides. Panel-I comprising 11 viral genomes was used to build samples containing a maximum of 3% viral reads in 20 million total reads. The samples were built considering a homogenous distribution of viral reads (synhom) or based on an average distribution of real samples (synab) (Table 2 and Table 3). In all cases, 10 subsets of reads were randomly selected at different sequencing depths. FIGURE 2A shows the read assignments for the ACLSV, ApMV, CGRMV, CNRMV, CVA, and LchV-1 viral genomes. FIGURE 2B shows the read assignments for the LchV-2, PBNSPaV, PDV, PNRSV, and PPV viral genomes. FIGURE 2C shows the completeness for the ACLSV, ApMV, CGRMV, CNRMV, CVA, and LchV-1 viral genomes. FIGURE 2D shows the completeness for the LchV-2, PBNSPaV, PDV, PNRSV, and PPV viral genomes.

[0013] FIGURES 3A-3F. Simulated mutations in synthetic NGS / HTS data. Mutated viral genomes were simulated to generate sets of synthetic NGS / HTS data to evaluate the tolerance of read assignment to mutated viral variants. Each viral genome from Panel-I was randomly mutated at different rates indicated (5, 10, 15, 20, 25, and 30%) in each graph. Although 20 million reads were generated for each mutation rate, a 10X subsampling of 10 million reads was performed, so the bars represent an average number of assigned reads. The distribution of viral reads in this case was homogeneous. FIGURE 3A shows the assigned reads for each viral genome at the 5%, 10%, and 15% mutation rates. FIGURE 3B shows the assigned reads for each viral genome at the 20%, 25%, and 30% mutation rates. In FIGURES 3A-3B, the dotted line shows the expected number of assigned reads according to the distribution of reads. FIGURE 3C shows the completeness for each viral genome at the 5%, 10%, and 15% mutation rates. FIGURE 3D shows the completeness for each viral genome at the 20%, 25%, and 30% mutation rates. FIGURE 3E shows the number of replicases found for each viral genome at the 5%, 10%, and 15% mutation rates. FIGURE 3F shows the number of replicases found for each viral genome at the 20%, 25%, and 30% mutation rates.

[0014] FIGURES 4A-4C. Dependence of completeness on viral abundance, depth of sequencing, and identification of replicases. To determine the relationship between completeness and viral abundance, depth of sequencing, and the presence of replicases, synthetic NGS / HTS data were generated with different abundances of viral reads. Only viruses with the shortest and longest genome lengths from Panel-I (CVA and LChV-1, respectively) were analyzed. The heatmap represents the obtained completeness (indicated in each cell at thetop) both in the respective depth of sequencing and in the abundance (%) of viral reads (represented on the vertical axis as % of viral composition from 3% of viral reads at the corresponding depth of sequencing); the numbers within a cell, separated by a vertical bar symbol, indicate the number of replicases / polymerases found in the respective assembly in the 10 sub-sampled sets at 80% length coverage with 80% similarity (80 / 80) and at 90% length coverage with 90% similarity (90 / 90). FIGURE 4A shows the completeness obtained using the Centrifuge bioinformatic algorithm. FIGURE 4B shows completeness obtained using the Kraken2 bioinformatic algorithm. FIGURE 4C shows the completeness obtained using the Minimap2 bioinformatic algorithm.

[0015] FIGURES 5A-5B. Examples of reads mapped to viral genomes. FIGURE 5A shows the assignment and mapping of reads onto the ASGV genome (6,496 nucleotides in length, R2 dataset), revealing that they could not be assembled into a contiguous contig containing a replicase. FIGURE 5B shows the assignment and mapping of reads onto segment 2 (2,046 nucleotides in length) of the PNRSV genome (sample SS-L2), where the reads are concentrated mostly in the 5' region. In this case, there is no mapping of reads to the other two segments (the replicase is contained in segment 1).

[0016] FIGURE 6. Distances between measures of published datasets. Comparison of measures (accuracy, FDR, precision, sensitivity, and specificity) obtained under different completeness thresholds with measures obtained for replicase identification under the 80 / 80 scheme. The detailed description of these data is shown in Table 9.

[0017] FIGURES 7A-7F. Detection of viral pathogens in different infected Primus sp. plants according to Panel-I. A two-step singleplex RT-PCR analysis was performed for the 11 viruses (lanes 1-11) and the internal control (lane 12) using specific primers. Lane 1: ACLSV; lane 2: AMV; lane 3: CGRMV; lane 4: CNRMV; lane 5: CVA; lane 6: LChV-1; lane 7: LChV-2; lane 8: PBNSPaV; lane 9: PDV; lane 10: PNRSV; lane 11: PPV; and lane 12: PDS-1. MM: 100- nucleotide molecular weight marker. FIGURE 7A shows the SS-L1 leaf samples. FIGURE 7B shows the SS-L2 leaf samples. FIGURE 7C shows the SS-S1 leaf samples. The nonspecific bands observed in this sample (lane 3) do not interfere with the expected PCR fragment for the CGRMV virus, which has a length of 181 nucleotides. FIGURE 7D shows the SS-S2 leaf samples. FIGURE 7E shows the specific amplification products of the 11 viral pathogens. FIGURE 7F shows the corresponding no-template control (NTC) for each RT-PCR reaction.

[0018] FIGURES 8A-8D. Assignment of reads with NGS / HTS data from field samples. From sequencing data for cherry plant field samples at the bud development stage (SD-L1, SD-L2, SD-S 1, and SD-S2), 10 subsets of randomly selected reads were constructed at different depths of sequencing. Three algorithms were tested: Centrifuge, Kraken2, and Minimap2. All bars represent the average of 10 repetitions. Read assignment was performed at different depths of sequencing (shown in different scales on the y-axis) using Panel-I (11 viruses). FIGURE 8A shows the read assignments for the SD-L1 (left column) and SD-L2 (right column) cherry plant field samples, for the ACLSV, ApMV, CGRMV, CNRMV, CVA, and LchV-1 viral genomes. FIGURE 8B shows the read assignments for the SD-L1 (left column) and SD-L2 (right column) cherry plant field samples, for the LchV-2, PBNSPaV, PDV, PNRSV, and PPV viral genomes. FIGURE 8C shows the read assignments for the SD-S1 (left column) and SD-S2 (right column) cherry plant field samples, for the ACLSV, ApMV, CGRMV, CNRMV, CVA, and LchV-1 viral genomes. FIGURE 8D shows the read assignments for the SD-S1 (left column) and SD-S2 (right column) cherry plant field samples, for the LchV-2, PBNSPaV, PDV, PNRSV, and PPV viral genomes.

[0019] FIGURES 9A-9D. Completeness with NGS / HTS data from field samples. From sequencing data for cherry plant field samples at the bud development stage (SD-L1, SD-L2, SD-S 1, and SD-S2), 10 subsets of randomly selected reads were constructed at different depths of sequencing. Three algorithms were tested: Centrifuge, Kraken2, and Minimap2. All bars represent the average of 10 repetitions. The completeness value is obtained from the respective assembly of assigned reads. The cases of the 11 viruses from Panel-I are presented. FIGURE 9A shows the completeness for the SD-L1 (left column) and SD-L2 (right column) cherry plant field samples, for the ACLSV, ApMV, CGRMV, CNRMV, CVA, and LchV-1 viral genomes. FIGURE 9B shows the completeness for the SD-L1 (left column) and SD-L2 (right column) cherry plant field samples, for the LchV-2, PBNSPaV, PDV, PNRSV, and PPV viral genomes. FIGURE 9C shows the completeness for the SD-S 1 (left column) and SD-S2 (right column) cherry plant field samples, for the ACLSV, ApMV, CGRMV, CNRMV, CVA, and LchV-1 viral genomes. FIGURE 9D shows the completeness for the SD-S1 (left column) and SD-S2 (right column) cherry plant field samples, for the LchV-2, PBNSPaV, PDV, PNRSV, and PPV viral genomes.

[0020] FIGURES 10A-10D. Virus identification in field samples at the bud development stage using Panel-II. NGS / HTS data from the field samples (SD-L1, SD-L2, SD-S1, and SD-S2) were analyzed using the methodology disclosed here, using Panel-II. Left y-axis: number ofviral reads (different scales); right y-axis: completeness scale; circles: completeness values for each of the three tested algorithms; filled circles: identified replicase; dotted lines: completeness thresholds at 0.1 and 0.3. FIGURE 10A shows the read assignments and completeness for the SD-L1 (left column) and SD-L2 (right column) field samples, for the ACLSV, ApMV, CGRMV, CNRMV, CVA, and LchV-1 viral genomes. FIGURE 10B shows the read assignments and completeness for the SD-L1 (left column) and SD-L2 (right column) field samples, for the LchV-2, PBNSPaV, PDV, PNRSV, and PPV viral genomes. FIGURE 10C shows the read assignments and completeness for the SD-S1 (left column) and SD-S2 (right column) field samples, for the ACLSV, ApMV, CGRMV, CNRMV, CVA, and LchV-1 viral genomes. FIGURE 10D shows the read assignments and completeness for the SD-S1 (left column) and SD-S2 (right column) field samples, for the LchV-2, PBNSPaV, PDV, PNRSV, and PPV viral genomes.

[0021] FIGURES 11A-11D. Virus identification in field samples at the senescence stage using Panel-II. NGS / HTS data from the field samples (SS-L1, SS-L2, SS-S1, and SS-S2) were analyzed using the methodology disclosed here, using Panel-II. Left y-axis: number of viral reads (different scales); right y-axis: completeness scale; circles: completeness values for each of the three tested algorithms; filled circles: identified replicase; dotted lines: completeness thresholds at 0.1 and 0.3. FIGURE 11A shows the read assignments and completeness for the SS-S2 (left column) and SS-L2 (right column) field samples, for the ACLSV, ApMV, CGRMV, CNRMV, CVA, and LchV-1 viral genomes. FIGURE 11B shows the read assignments and completeness for the SS-S2 (left column) and SS-L2 (right column) field samples, for the LchV-2, PBNSPaV, PDV, PNRSV, and PPV viral genomes. FIGURE 11C shows the read assignments and completeness for the SS-S1 (left column) and SS-L1 (right column) field samples, for the ACLSV, ApMV, CGRMV, CNRMV, CVA, and LchV-1 viral genomes. FIGURE 11D shows the read assignments and completeness for the SS-S1 (left column) and SS-L1 (right column) field samples, for the LchV-2, PBNSPaV, PDV, PNRSV, and PPV viral genomes.DETAILED DESCRIPTION1. General description

[0022] The description of the various embodiments disclosed herein should not limit the present invention but rather instruct a skilled person in the art to carry out the different embodiments of the methodology explained here.

[0023] In some aspects, described herein is a methodology related to the identification of biological entities, particularly microscopic biological agents, exemplified by viral pathogens (e.g., viruses and viroids). The method involves the bioinformatic analysis of data obtained from high-throughput sequencing (NGS / HTS) and the detection of functional elements that can ultimately contribute to global security against the growing threats of disease proliferation caused by viral agents (Massart et al., 2017. A framework for the evaluation of biosecurity, commercial, regulatory, and scientific impacts of plant viruses and viroids identified by NGS technologies. Front. Microbiol. 8, p.45; Mehetre et al., 2021. Current Developments and Challenges in Plant Viral Diagnostics: A Systematic Review. Viruses. 13(3), p.412).

[0024] Viral diseases in plants are a major concern for farmers, researchers, and policymakers, as they can decimate food production and even eradicate entire species (Legg et al., 2000. Cassava mosaic virus disease in East Africa: a dynamic disease in a changing environment. Virus Res. 71(1-2), pp.135-149; Gonsalves et al., 2008. Papaya Ringspot Virus. Editor(s): Brian W.J. Mahy, Marc H.V. Van Regenmortel. Encyclopedia of Virology (Third Edition), pp.1-8. Academic Press; Moreno et al., 2008. Citrus tristeza virus: a pathogen that changed the course of the citrus Industry. Mol. Plant Pathol. 9, pp.251-268). One such case was the viral pathogen known as Citrus tristeza virus, which is estimated to have affected over 100 million plants in various countries worldwide (Jones R., 2021. Global Plant Virus Disease Pandemics and Epidemics. Plants. 10(2), p.233). Plant viruses and viroids are among the most important pathogens in agriculture, causing losses of over $30 billion annually (Rao and Reddy, 2020. Overview of yield losses due to plant viruses. Applied Plant Virology: Advances, Detection, and Antiviral Strategies. Ed. L. P. Awasthi (Academic Press), pp.531-562; Rodrfguez- Verastegui et al., 2022. Viruses Infecting Trees and Herbs That Produce Edible Fleshy Fruits with a Prominent Value in the Global Market: An Evolutionary Perspective. Plants. 11, p.203). In this context, accurate detection and timely diagnosis of viral biological agents (such as viruses and viroids in plants) are essential to prevent the spread of these pathogens due to global mobility of people and worldwide trade practices of plants, animals, and food products.

[0025] Currently, conventional methods are widely employed for virus detection in plants, such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (PCR, or quantitative qPCR). Both techniques demonstrate high levels of reproducibility, automation capability, and relatively low cost compared to industry standards (Boonham et al., 2014. Methods in virus diagnostics: from ELISA to next generation sequencing. Virus Res. 186, pp.20-31; Chauhan et al., 2019. A systematic review of conventional and advancedapproaches for the control of plant viruses. J Appl Biol Biotech. 7(04), pp.89-98). However, these methods have associated disadvantages. ELISA has limited detection sensitivity for cases of low viral abundance, while PCR-dependent methods are biased by the availability of sequences for assay design and may fail to detect nucleotide variants. In contrast, high- throughput sequencing (NGS / HTS) currently allows for unbiased identification of viral biological agents, although it is still in the process of being adopted as a routine technique. Its costs need to be reduced, and analysis criteria standardized to enable detection, particularly in cases of low abundance and / or low sequencing coverage of the biological agent (Jones et al., 2017. Viral Diagnostics in Plants Using Next Generation Sequencing: Computational Analysis in Practice. Front. Plant Sci. 8, p. 1770; Massart et al., 2017. A framework for the evaluation of biosecurity, commercial, regulatory, and scientific impacts of plant viruses and viroids identified by NGS technologies. Front. Microbiol. 8, p.45; Massart et al., 2019. Virus Detection by High-Throughput Sequencing of Small RNAs: Large-Scale Performance Testing of Sequence Analysis Strategies. Phytopathology. 109(3), pp.488-497; Mehetre et al., 2021. Current Developments and Challenges in Plant Viral Diagnostics: A Systematic Review. Viruses. 13(3), p.412). Specifically, low sequencing coverage presents a complex diagnostic challenge. It can be caused by various factors, including low viral titer, insufficient depth of sequencing, cross-contamination of samples, remnants of a past infection, and / or a latent phase of a virus.

[0026] For the identification and / or detection of pathogens, the methodology is based on the use of high-throughput nucleic acid sequencing techniques. This methodology involves obtaining sequencing data from one or more biological samples using NGS or HTS technologies, such as (but not limited to) those developed for platforms like Illumina, Oxford Nanopore, Pacific Biosciences, MGI, and others that provide information about the identity of nucleotide biological sequences. The disclosed methodology is shown in FIGURES 1A-1B and is described below.

[0027] Step 1 of the disclosed methodology involves processing, filtering, and purifying the reads obtained from high-throughput sequencing platforms (module 101). This step may include read decontamination, which can involve pre-classification or taxonomic assignment (module 101b) to discard reads or sequences derived from the host organism, as well as microorganisms that naturally coexist with the host organism (e.g., microorganisms present in the host's microbiome, including bacteriomes, archaeomes, mycobiomes, and viromes). On the other hand, Step 2 (module 102), which is optional, involves subjecting the decontaminatedreads or sequences to taxonomic classification or assignment. This step is performed using specifically designed algorithms (e.g., k-mer-based algorithms like Kraken2 or Centrifuge, or direct alignment algorithms like Minimap2, Blast, or Diamond), and multiple algorithms can be used to achieve more specific taxonomic assignment. This assignment is carried out by comparing the reads against a set of known sequences, which typically form a panel (database). The panel can be limited or consider the complete spectrum of known pathogen genomes (module 103). Step 3 (module 104) involves the assembly of reads into contigs (module 105), which can be performed using nucleic acid assemblers and / or suitable algorithms (e.g., SPAdes, Megahit). The assembly step can be performed on the entire set of reads in the sample(s) or on the reads already classified independently for each taxonomic category assigned. Step 4 (module 105b), performed if Step 2 is not carried out, involves the taxonomic assignment or classification of the contigs obtained in the assembly step. This assignment is carried out using the same types of algorithms mentioned in Step 2. In both of Steps 2 and 4, if multiple algorithms are used for assignment, the methodology may involve a consensus assignment of reads or contigs by two or more algorithms. Step 5 of the methodology involves comparing the assembled contigs with their respective reference genomes (module 106). This comparison includes aligning or overlaying the contigs so that if multiple nucleotide sequences align to a reference genome, a consensus scaffold sequence is generated. The consensus scaffold may contain nucleotide bases represented using the IUPAC (International Union of Pure and Applied Chemistry) symbol notation. Step 6 involves calculating completeness (module 107) to determine the percentage coverage of the consensus scaffold sequence on the respective reference genome. This calculation takes into account that the reference genome may be segmented, meaning it consists of distinct nucleic acid segments. The degree of completeness serves as evidence of the effective presence of the pathogen, based on certain completeness thresholds or limits. Step 7 involves the functional annotation of the consensus scaffold sequence (module 108), which determines the presence of functional elements encoded within the sequence. These functional elements can include proteins related to the survival or propagation of the pathogen, such as polymerases or replicases of the pathogen's genome, or proteins related to other vital functions encoded in the pathogen's genome (module 109). The presence of functional elements encoded in the consensus scaffold sequence is also evidence of the effective presence of the pathogen in the sequenced sample. The final step (module 110) involves interpreting both the degree of completeness (according to threshold values) and / or the effective presence of functional elements.2. Definitions

[0028] In order to provide greater clarity to the disclosure of the present inventions, the following are definitions of some relevant terms and concepts that will aid in understanding the content presented here.

[0029] As used herein, “viral biological agents” typically refers to microscopic entities known as viruses and viroids. Viruses are infectious biological agents that consist of a segment of nucleic acid (DNA or RNA) surrounded by a protein coat. A virus cannot replicate on its own; instead, it must infect cells and utilize components of the host cell to produce copies of itself. On the other hand, viroids are infectious agents of lower genetic and structural complexity than viruses. Viroids do not encode any proteins and can infect and induce diseases in plants.

[0030] As used herein, a “pathogen” is generally understood to be any microscopic biological entity, whether it is a microorganism (bacteria, fungus, or archaea) or a viral agent, with the ability to establish harmful biological interactions with other higher organisms (humans, animals, and plants).

[0031] A “read” is generally understood as the representation of the nucleotide sequence obtained as the output of the sequencer (the machine that carries out the sequencing). Reads form the basis of next-generation sequencing (NGS / HTS) technologies. The length of the read depends on the technology used, for example, the Illumina platform can generate reads of up to 300 nucleotides, while Oxford Nanopore or Pacific Bioscience platforms can generate reads even beyond 1000 nucleotides.

[0032] A “contig” is generally understood as a continuous nucleotide sequence obtained from the assembly of reads. The assembly of reads involves overlapping reads based on nucleotide identity. The overlapping reads are merged to generate a longer continuous nucleotide sequence or contig. On the other hand, a “scaffold” typically refers to a non-contiguous series of genomic sequences consisting of sequences separated by known gap lengths. The linked sequences are typically contigs. The process of read (or contig) overlap can result in sections with variable nucleotide identity percentages. In such cases, the nucleotide identity at a position in the merged sequence can be determined through consensus, which is often based on the nucleotide with the highest frequency at that position, through a statistical measure for that position, or alternatively, considering nucleotide variation by annotating it with alternative nomenclature (e.g., IUPAC nucleotide notation).

[0033] As used herein, “completeness” is generally understood as the degree or percentage to which the “consensus scaffold sequence” overlaps or aligns with the respective reference genome. A consensus scaffold sequence is obtained from the consensus sequence resulting from the overlap or alignment of contigs with a reference genome or sequence. It is possible for the reference genome to contain chromosomes or be segmented. In cases of segmented reference genomes, the completeness calculation may take these segments into account. Therefore, completeness is a quantitative measure that can establish thresholds to determine the evidence of the presence or absence of a pathogen in the analyzed sample.

[0034] Usually, “vertical coverage” (also known as “depth of coverage”) refers to the number of times (on average) that a base at a certain position of a reference nucleotide sequence is represented when the reads from a sequencing process are overlapped or aligned, either as raw or filtered reads or as assembled contigs. In contrast to “horizontal coverage” (also known as “breadth of coverage”), which refers to the length of the assembly when considering the entire assembly, vertical coverage is expressed as IX, 2X, and so on, indicating how many times the sequence or genome of interest has been sequenced. On the other hand, as used herein, the term “depth of sequencing” typically refers to the total number of reads present in the sequenced sample. In this case, the reads may include sequences from various sources (host organism, pathogens, microbiomes, etc.). If the reads from a pathogen are filtered and this total number of reads is considered as the depth of sequencing of the pathogen in that sample, dividing that total by the length of the pathogen's genome results in the vertical coverage obtained in the sequencing of the sample for that pathogen.

[0035] As used herein, the term “functional annotation” typically refers to the identification of encoded functional elements within a nucleotide sequence.

[0036] As used herein, a “functional element” is generally understood to be a genetic element that enables the survival and / or the replication of the biological entity. Functional elements include protein-coding nucleotide sequence, or may be non-protein-coding nucleotide sequences that encode a functional nucleic acid element. For example, functional elements include protein coding sequences, promoter sequences, enhancer sequences, terminator sequences, origins of replication, nucleotide sequences encoding structural domains, such as those having particular secondary structure, and nucleotide sequences having ribozyme function. For clarity, functional elements include sequence domains of viroids, even though they are not protein-coding sequences.

[0037] The term "identification" as used herein typically refers to the detection of the presence of one or more biological entities (e.g., biological agents) in one or more samples containing nucleic acids, as evidenced by massive sequencing data. In this context, the term "diagnosis" can also be used synonymously with identification when it comes to detecting pathogenic agents.3. Detailed description

[0038] In the last two decades, NGS / HTS methods have become a reliable tool for the identification of viral agents in plants, including disease risk management, outbreak surveillance, and the adoption of new phytosanitary standards (Adams et al., 2018. The impact of high throughput sequencing on plant health diagnostics. Eur J. Plant Pathol., 152, pp.909- 919; Gauthier et al., 2022. Side-by-Side Comparison of Post-Entry Quarantine and High Throughput Sequencing Methods for Virus and Viroid Diagnosis. Biology, 11(2), pp. 263). Due to their unbiased nature, NGS / HTS-based diagnostic methodologies are capable of detecting multiple known and emerging viruses and viroids in infected material, even at very low concentrations (Hanafi et al., 2022. Detection of Banana Mild Mosaic Virus in Musa In Vitro Plants: High-Throughput Sequencing Presents Higher Diagnostic Sensitivity Than (IC)- RT-PCR and Identifies a New Betaflexiviridae Species. Plants (Basel, Switzerland), 11(2), p.226). They have proven to be a significant advancement for crops, imported plants, and germplasm, especially in cases where disease symptoms are absent, nonspecific, or triggered by multiple viruses (Massart et al., 2017. A framework for the evaluation of biosecurity, commercial, regulatory, and scientific impacts of plant viruses and viroids identified by NGS technologies. Front. Microbiol. 8, p.45; Mehetre et al., 2021. Current Developments and Challenges in Plant Viral Diagnostics: A Systematic Review. Viruses. 13(3), p.412). Import and export practices of plants require strict phytosanitary controls to prevent the spread of pathogens between countries. In some cases, quarantines of several years are required before authorizing the importation of plant material, creating a barrier to accelerating the transfer of newly developed varieties with improved traits. During quarantine, plants are monitored for evidence of viral symptoms and repeatedly analyzed to detect diseases using molecular methods directly targeting viral detection (Jones R., 2009. Plant virus emergence and evolution: origins, new encounter scenarios, factors driving emergence, effects of changing world conditions, and prospects for control. Virus Res. 141(2), pp.113-30; Massart et al., 2017. A framework for the evaluation of biosecurity, commercial, regulatory, and scientific impacts of plant viruses and viroids identified by NGS technologies. Front. Microbiol. 8, p.45). In the faceof a changing agricultural paradigm, the methods described herein address a long-felt need improvement in the analysis of NGS / HTS data and its accurate interpretation.

[0039] In some aspects, described herein is a methodology for the identification of biological entities, particularly viral and bacterial agents, based on NGS / HTS data that utilizes the concept of completeness and the identification of functional elements of the pathogen (e.g., replicase) to enhance viral detection. One of the main challenges in viral agent identification from NGS / HTS data is defining the presence of the viral agent when facing a limited number of reads from that agent. Furthermore, the detection of a pathogen does not necessarily imply an ongoing infection (Kiselev et al., 2020. Current Trends in Diagnostics of Viral Infections of Unknown Etiology. Viruses. 12(2), p.211). The methods of the present disclosure are based in part on the discovery that the detection of viral sequences along with their ability to encode fundamental biological functions, such as replication, can improve the certainty of detection by evaluating a functional aspect, in this case, viral biology.

[0040] To demonstrate a specific embodiment of the methods described herein, artificial or simulated NGS / HTS datasets of 20 million paired reads of 150 nucleotides were generated (Table 1) using the ART software (Huang et al., 2012. ART: a next-generation sequencing read simulator. Bioinformatics (Oxford, England), 28(4), pp.593-594) and seqkit (Shen et al., 2016. SeqKit: A Cross -Platform and Ultrafast Toolkit for FASTA / Q File Manipulation. PLOS ONE 11(10), e0163962). Two of these datasets were intended to simulate real field samples containing 11 viruses (Panel-I): Apple chlorotic leaf spot virus (ACUSV), Apple mosaic virus (ApMV), Cherry green ring mottle virus (CGRMV), Cherry necrotic rusty mottle virus (CNRMV), Cherry virus A (CVA), kittle cherry virus 1 (LChV-1), Plum bark necrosis stem pitting-associated virus (PBNSPaV), Prune dwarf virus (PDV), Prunus necrotic ringspot virus (PNRSV), Plum pox virus (PPV), and Little cherry virus 2 (LChV-2). In the first dataset (Synab data), the abundance of viral reads was based on experimental data (NCBI SRA ID: PRJNA848006), while for the second dataset (Synhom data), a uniform distribution of viral read abundance was assumed (Table 2 and Table 3). Subsampling was then applied to both datasets to obtain different depths of sequencing (100K, 500K, IM, 5M, 10M, and 15M reads) in a jackknife process (10 repetitions). Another dataset of 20 million paired reads (150 nucleotides) was generated (Mut data), where all viruses from Panel-I were randomly mutated at different rates (5, 10, 15, 20, 25, 30%). In this case, a jackknife process with subsampling of 10 million reads each was performed for 10 repetitions (using the repetition number as the seed for subsampling). The Mutation- Simulator software (version 2.0.3) (Kuhl et al., 2021.Mutation- Simulator: fine-grained simulation of random mutations in any genome. Bioinformatics (Oxford, England), 37(4), pp.568-569) was used to simulate mutations in the viruses. Besides, two additional datasets were constructed to further investigate the relationship between virus abundance and depth of sequencing. In this case, datasets of 20 million paired reads in total (150 nucleotides) were generated, containing a maximum of 3% viral reads from the shortest genome virus (CVA) and the longest genome virus (LChV-1) from Panel-I (Cva and Lchvl data). Subsets were generated containing 0.05%, 0.1%, 0.5%, 1%, 5%, and 10% representation of the previously defined viral reads (3% of the total reads). For example, a set of 20 million reads with 100% viral reads contained 3% of the total reads, which equated to 600,000 viral reads, while a viral abundance of 0.5% contained 3,000 reads. The remaining reads (contaminants), consisting of plant, human, bacterial, and random sequences in the same proportion, are listed in Table 2. To compensate for the decrease in the number of reads, the difference was replaced with bacterial reads. Each of these datasets was further subsampled in a jackknife process to obtain 10 subsets at different sequencing depths (100K, 500K, IM, 2M, 3M, 4M, 5M, 6M, 7M, 8M, 9M, and 10M random reads), containing a maximum of 3% viral reads from the shortest genome virus (CVA) and the longest genome virus (LChV-1) from Panel-I (Cva and Lchvl data). Subsets were generated containing 0.05%, 0.1%, 0.5%, 1%, 5%, and 10% representation of the previously defined viral reads (3% of the total reads). For example, a set of 20 million reads with 100% viral reads contained 3% of the total reads, which equated to 600,000 viral reads, while a viral abundance of 0.5% contained 3,000 reads. The remaining reads (contaminants), consisting of plant, human, bacterial, and random sequences in the same proportion, are listed in Table 2. To compensate for the decrease in the number of reads, the difference was replaced with bacterial reads. Each of these datasets was further subsampled in a jackknife process to obtain 10 subsets at different sequencing depths (100K, 500K, IM, 2M, 3M, 4M, 5M, 6M, 7M, 8M, 9M, and 10M random reads).Table 1. Summary of all datasets to validate the methods disclosed herein.Table 2. General distribution of reads in synthetic HTS data.Virus percentage is based on empirical distribution of samples LI, L2, SI, S2, which together yielded 3% of viral reads (average using Minimap2, Kraken2 and Centrifuge in the read assignment). Percentages of plant, human and bacteria reads are based on the same criterium. Random reads were generated to fulfill a total of 20 million reads.Table 3. Pavium panel-I and viral reads distribution in synthetic data.Synab percentages are based on empirical distribution of viruses in samples SD-L1, SD-L2, SD-S1, SD-S2, which together yielded a total of 3% of viral reads (average percentages using Minimap2, Kraken2 and Centrifuge in the read assignment), that is, a total of 600K reads in a sample of 20M reads. Synhom percentages are based on an homogeneous distribution of reads. “Abbrev.” represents each virus’s common abbreviation. “Length” is listed in basepairs (bp). “Seg.” represents number of segments.

[0041] For the read assignment (taxonomic classification of reads based on a panel of viral genomes), two widely used algorithms for metagenomic exploration were considered: Kraken2 (Wood et al., 2019. Improved metagenomic analysis with Kraken 2. Genome Biol 20, p.257.) and Centrifuge (Kim et al., 2016. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. 26(12), pp.1721-1729). These algorithms are known to be fast and sensitive according to the literature (Miossec et al., 2020. Evaluation of computational methods for human microbiome analysis using simulated data. PeerJ. 2020 Aug. 11;8, e9688). Minimap2, a classic algorithm used for read alignment comparison (Li H., 2018. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics (Oxford, England), 34(18), pp.3094-3100), was also used in the read assignment process. All algorithms were executed with default parameters, except for Kraken2, where the default k-mer length parameter (k) was reduced from 35 to 31 to increase its sensitivity.

[0042] For all groups of datasets (Table 1), ad hoc viral panels were created to establish the appropriate databases to be used by each algorithm. In the case of the simulated datasets (Synab, Synhom, Mut, Cva, Lchvl), the analysis was performed using the 11 viruses from Panel-I, and their respective databases included reference sequences from NCBI (Table 3). Furthermore, an extended database of this panel was constructed by incorporating differentisolates / variants of the 11 viruses. A total of 1,011 sequences were obtained from NCBI (including the 11 original reference sequences) by using all sequences with defined taxIDs and retaining only complete genomes, which were clustered using CD-HIT (Fu et al., 2012. CD- HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics (Oxford, England), 28(23), pp.3150-3152). Using a sequence identity threshold of 90%, 139 clusters were obtained, and their representative sequences became Panel-II (Table 4).

[0043] The reads assigned to a viral reference sequence were de novo assembled. SPAdes (Bankevich et al., 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 19(5), pp.455-477) was used to perform the assembly with Illumina reads (in '—sc' mode and with the '--careful' option), while Canu (Koren et al., 2017. Canu: Scalable and accurate long-read assembly via adaptive K-mer weighting and repeat separation. Genome Res. 27, gr.215087.116) was used to assemble Oxford Nanopore reads. In both cases, the contigs obtained in the assembly process were overlapped and aligned to the respective reference genome. The alignment was performed using Minimap2 (with the '-map- onf option for mapping contigs), and the genome coverage (percentage of the reference genome covered by the assembled contigs) was calculated using SAMtools / BCFtools (Danecek et al., 2021. Twelve years of SAMtools and BCFtools. GigaScience. 10(2), giab008).On the other hand, thedeteness value was calculated as follows:, where CBi is the number of nucleotide bases with depth > 1 considered for segment z of the reference genome, and SL, is the length of segment z of the reference genome (in the case of viruses, most of them have only one genomic segment, so n = 1 in these cases). Thus, completeness is a value that ranges from 0 to 1 (and can also be expressed as a percentage).

[0044] On the other hand, consensus scaffold regions were determined from overlapped or aligned contigs to the reference genome using SAMtools / BCFtools. Each consensus region was compared to a protein sequence database related to viral replication (e.g., replicase or polymerase). This database was created from another database called RVDB-prot (version 23.0, 2021-12; Bigot et al., 2020. RVDB-prot, a reference viral protein database and its HMM profiles. FlOOOResearch. 8, p.530) and contains 17,708 records. To build this repository, RVDB-prot was filtered using terms related to replicase or polymerase activity. The searched terms were: "replicase," "RNA dependent RNA polymerase," "RdRp," " RN A dependent DN Apolymerase," "RdDp," and "polymerase," which retrieved 13,576 sequences. Additionally, the term "reverse transcriptase" (which is a synonym for "RdDp") was searched, and 59,525 records were obtained. In this case, records belonging to "homo," "human," "hepatitis," and "HIV" were filtered out, resulting in 4,114 sequences that were incorporated into our protein repository. During the evaluation of the different panels, some viruses were unable to report the presence of a coding sequence related to replicase or polymerase activities. However, all these cases were manually inspected and found to encode either a protein with a name that did not contain any of the previously searched terms, one that was absent in RVDB-prot, or one that was part of a polyprotein. Finally, all of them were included in our replicase protein repository.

[0045] The comparison of consensus scaffold regions with the replicase repository was performed using Diamond (Buchfink et al., 2021. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat methods 18(4), pp.366-368) with its blastx module. Subsequently, the results were filtered based on similarity (80% or 90%) and alignment length (80% or 90%). The 80 / 80 scheme indicates that a replicase is identified in the respective consensus region if it has at least 80% similarity and 80% alignment length with the sequences (90 / 90 represents a stricter scheme).

[0046] Regarding the read assignment in the simulated data, when a homogeneous distribution is used (Synhom data in FIGURES 2A-2D), no differences are observed in the read assignment or completeness values among all viruses. As expected, a higher sequencing depth resulted in a greater number of assigned reads. These results are independent of the virus characteristics (e.g., length or number of segments) and the evaluated algorithm, and no differences are observed in terms of completeness values. Furthermore, complete viral genomes were assembled even with the lowest sequencing depth of 100,000 reads. On the other hand, in the simulated data with an empirical distribution (Synab data in FIGURES 2A-2D), the read assignment is proportional to the sequencing depth and viral abundance. The viruses used in the simulated data were CGRMV, CNRMV, CVA, PDV, and PNRSV (those with a percentage > 0 in Table 3). Therefore, the reads assigned to the LChV-1 genome are considered incorrect assignments. In fact, this virus did not show any assembly (completeness = 0) at each depth of sequencing, confirming the erroneous assignment (FIGURES 2A-2D).Table 4. Clustering of isolates and Pavium panel-II.Clustering of 1,011 sequences was performed using CD-HIT at 90% sequence identity. Abbrev.: Abbreviation.

[0047] Regarding the simulated data with mutations (Mut data), it was found that with a mutation rate of 10%, all algorithms were able to assign reads, and even with a 20% mutation rate, there is still an average of -2,900 out of 27,000 reads assigned to viral genomes (FIGURES 3A-3F). In this case, the read assignment is independent of the viruses, which are evenly distributed in the simulated samples. Among the evaluated algorithms, Centrifuge achieves better results in the read assignment process overall, especially at higher mutation rates. Minimap2, on the other hand, appears to be the least tolerant algorithm to mutations. Although an increase in the mutation rate seems to have a greater impact on read assignment, with a 20% mutation rate, the number of reads is still sufficient to assemble contigs, at least with Centrifuge and Kraken2 (completeness ~ 1).

[0048] To delve further into the presence of reads and examine the biological relevance of the assembled contigs at different sequencing depths and viral abundances, completeness valuesare analyzed in terms of the presence of replicases in the assembled contigs. This is done to verify if the assembled portion of the virus (consensus scaffold region or sequence) encodes a biologically relevant function to support the use of specific completeness thresholds for viral detection. The simulated data Cva and Lchvl show that all evaluated algorithms achieve similar levels of completeness values. At the lowest sequencing depth (100K), viral abundance is critical as the completeness value is only relevant starting from 5% of the viral composition (FIGURES 4A-4C). However, the completeness value increases at lower viral abundances as the depth of sequencing increases. With the lowest viral abundance (0.05%), the maximum completeness values obtained with 10 million total reads were 0.97 for CVA and 0.89 for LChV-1. In extreme scenarios (e.g., 0.05% of total viral reads), the depth of sequencing becomes critical. Therefore, with a total of 10 million reads, the number of viral reads is -150 (sufficient to assemble the longest virus), but with a total of 100,000 reads, this number is -1.5 reads, making it impossible to assemble a viral genome (hence completeness ~ 0). Furthermore, using the repository of viral proteins related to replication, the identification of replicases in the consensus scaffold regions or sequences is performed. A minimum of 90% alignment length and 90% sequence similarity between the scaffold sequences and the set of replicases results in a high correlation (R2> 0.9) between the completeness value and the presence of replicases, as expected when a full-length virus can be assembled (FIGURE 4A-4C). By reducing the parameters to 80% alignment length and 80% similarity, there is a subtle increase in replicase recovery at lower depth of sequencing and viral abundance. With these parameters, the correlation between the completeness value and the presence of replicases remains high (R2> 0.9). Overall, according to the simulations and regardless of the algorithm used, the identification of replicases starts at a completeness value of approximately 0.3 for the case of the lowest sequencing depth (100,000 total reads) or completeness value of approximately 0.4 for the case of the lowest viral abundance (0.05%), which essentially represents a minimum of 15 viral reads for CVA or 30 reads for LChV-1 to identify a replicase in some of the simulations (FIGURES 4A-4C). Interestingly, the genome of LChV-1 has a length of 16,934 nucleotides, and CVA has a length of 7,383 nucleotides, reflecting the requirement for double the number of reads for identification.

[0049] In the case of the Mut data, when evaluating the presence of replicases, it is expected that identification would be affected by the mutation rate, as changes in the nucleotide sequence can alter the encoded protein. Indeed, the identification of replicases is less favored at a mutation rate of 15% (FIGURES 3A-3F), but completeness values of 1 are still obtained, evenat a 20% mutation rate with Centrifuge and Kraken2. This allows establishing a sequence identity threshold of 90% for clustering and incorporating different viral isolates / strains into a reference database used in the read assignment process (as in the case of establishing Panel-II).

[0050] In another embodiment of the invention disclosed herein, the taxonomic assignment of both reads and contigs can be carried out by using a panel or reference database that includes the following viral pathogens: Abaca bunchy top virus, Abutilon Brazil virus, Abutilon golden mosaic Yucatan virus, Abutilon mosaic Bolivia virus, Abutilon mosaic Brazil virus, Abutilon mosaic virus, Aconitum latent virus, Actinidia chlorotic ringspot, Actinidia emaravirus 2, Actinidia seed borne latent virus, Actinidia virus 1, Actinidia virus A, Actinidia virus B, Actinidia virus X, Adonis mosaic virus, Aeonium ringspot virus, African cassava mosaic Burkina Faso virus, African cassava mosaic virus, African eggplant mosaic virus, African eggplant yellowing virus, Agave tequilana leaf virus, Ageratum conyzoides associated symptomless alphasatellite, Ageratum conyzoides symptomless alphasatellite, Ageratum enation alphasatellite, Ageratum enation virus, Ageratum leaf curl betasatellite, Ageratum leaf curl Cameroon alphasatellite, Ageratum leaf curl Cameroon betasatellite, Ageratum leaf curl Cameroon virus, Ageratum leaf curl Sichuan virus, Ageratum leaf curl virus, Ageratum yellow leaf curl betasatellite, Ageratum yellow vein alphasatellite, Ageratum yellow vein betasatellite, Ageratum yellow vein China alphasatellite, Ageratum yellow vein China betasatellite, Ageratum yellow vein China virus, Ageratum Yellow vein China virus, Ageratum yellow vein Hualian virus, Ageratum yellow vein India alphasatellite, Ageratum yellow vein India betasatellite, Ageratum yellow vein Pakistan alphasatellite, Ageratum yellow vein Singapore alphasatellite, Ageratum yellow vein Sri Lanka betasatellite, Ageratum yellow vein Taiwan virus, Ageratum yellow vein virus, Ageratum yellow vein virus satellite DNA beta, Aglaonema bacilliform virus, Air potato virus 1, Alfalfa dwarf virus, Alfalfa enamovirus 1, Alfalfa latent virus, Alfalfa leaf curl virus, Alfalfa mosaic virus, Alfalfa virus F, Alfalfa virus S, Algerian watermelon mosaic virus, Allamanda leaf curl virus, Allamanda leaf mottle distortion virus, Allium cepa amalgavirus 1, Allium cepa amalgavirus 2, Allium virus X, Alpinia oxyphylla mosaic virus, Alstroemeria mosaic virus, Alstroemeria necrotic streak virus, Alstroemeria virus X, Alstroemeria yellow spot virus, Alternanthera mild mosaic virus, Alternanthera mosaic virus, Alternanthera yellow vein betasatellite, Alternanthera yellow vein virus, Amaranthus leaf mottle virus, Amazon lily mild mottle virus, Ambrosia asymptomatic virus 1, American hop latent virus, American plum line pattern virus, Andean potato latent virus, Andean potato mild mosaic virus, Andrographis yellow vein leaf curl betasatellite, Andrographis yellow vein leafcurl virus, Angelica bushy stunt virus, Angelica virus Y, Angelonia flower break virus, Anthoxanthum odoratum amalgavirus 1, Apium virus Y, Apple, Apple chlorotic leaf spot virus, Apple dimple fruit viroid, Apple fruit crinkle viroid, Apple green crinkle associated virus, Apple hammerhead viroid, Apple luteovirus 1, Apple mosaic virus, Apple necrotic mosaic virus, Apple rubbery wood virus 1, Apple rubbery wood virus 2, Apple scar skin viroid, Apple stem grooving virus, Apple stem pitting virus, Apricot latent virus, Apricot pseudo, Apricot vein clearing associated virus, Arabidopsis halleri partitivirus 1, Arabis mosaic virus, Arabis mosaic virus small satellite RNA barley / CHE / 1991, Arachis pintoi virus, Arctopus echinatus, Areca palm necrotic ringspot virus, Areca palm necrotic spindle, Areca palm velarivirus 1, Arhar cryptic virus, Arracacha mottle virus, Arracacha virus 1, Arracacha virus B, Arracacha virus V, Artemisia virus A, Artichoke Italian latent virus, Artichoke latent virus, Artichoke mottled crinkle virus, Artichoke yellow ringspot virus, Asclepias asymptomatic virus, Asian prunus virus 1, Asian prunus virus 2, Asian prunus virus 3, Asparagus virus 1, Asparagus virus 2, Asparagus virus 3, Asystasia mosaic Madagascar virus, Atractylodes mild mottle virus, Atractylodes mottle virus, Australian grapevine viroid, Avocado sunblotch viroid, Avocado sunblotch viroid B, Avocado sunblotch viroid Sc, Avocado sunblotch viroid V, Axonopus compressus streak virus, Babaco mosaic virus, Bamboo mosaic virus, Bamboo mosaic virus satellite RNA, Banana bract mosaic virus, Banana bunchy top alphasatellite 3, Banana bunchy top virus, Banana mild mosaic virus, Banana streak CA virus, Banana streak GF virus, Banana streak IM virus, Banana streak MY virus, Banana streak OL virus, Banana streak UA virus, Banana streak UI virus, Banana streak UL virus, Banana streak UM virus, Banana streak virus, Banana streak virus Acuminata Yunnan, Banana streak VN virus, Barbacena virus Y, Barley dwarf virus, Barley mild mosaic virus, Barley stripe mosaic virus, Barley virus G, Barley yellow dwarf virus GAV, Barley yellow dwarf virus GPV, Barley yellow dwarf virus (ISOLATE MAV, Barley yellow dwarf virus (ISOLATE P, Barley yellow dwarf virus kerll, Barley yellow dwarf virus kerlll, Barley yellow dwarf virus MAV, Barley yellow dwarf virus PAS, Barley yellow dwarf virus PAV, Barley yellow dwarf virus SGV, Barley yellow mosaic virus, Barley yellow striate mosaic cytorhabdovirus, Basella alba alphaendornavirus 1, Basella rugose mosaic virus, Bean bushy stunt virus, Bean calico mosaic virus, Bean chlorosis virus, Bean chlorotic mosaic virus, Bean common mosaic necrosis virus, Bean common mosaic virus, Bean common mosaic virus strain Blackeye, Bean golden mosaic virus, Bean golden yellow mosaic virus, Bean latent virus, Bean leaf crumple virus, Bean leafroll virus, Bean necrotic mosaic virus, Bean pod mottle virus, Bean rugose mosaic virus, Bean white chlorosis mosaic virus, Bean yellow disorder virus, Bean yellow dwarf virus, Bean yellow mosaic Mexicovirus, Bean yellow mosaic virus, Beet black scorch virus, Beet black scorch virus satellite RNA, Beet chlorosis virus, Beet cryptic virus 1, Beet cryptic virus 2, Beet curly top Iran virus, Beet curly top virus, Beet mild curly top virus, Beet mild yellowing virus, Beet mosaic virus, Beet necrotic yellow vein virus, Beet pseudoyellows virus, Beet ringspot virus, Beet severe curly top virus, Beet soil, Beet virus Q, Beet western yellows virus, Beet yellow stunt virus, Beet yellows virus, Begomovirus, Begonia flower breaking virus, Belladonna mottle virus, Bellflower veinal mottle virus, Bellflower vein chlorosis virus, Bell pepper alphaendornavirus, Bell pepper mottle virus, Bendhi Yellow Vein Mosaic / Mesta Yellow Vein Mosaic alphasatellite, Bermuda grass latent virus, Bhendi yellow vein betasatellite, Bhendi yellow vein Bhubhaneswar virus, Bhendi yellow vein Delhi virus, Bhendi yellow vein Haryana virus, Bhendi yellow vein India betasatellite, Bhendi yellow vein India virus, Bhendi yellow vein mosaic alphasatellite, Bhendi yellow vein mosaic betasatellite, Bhendi yellow vein mosaic virus, Bhindi yellow vein alphasatellite, Bidens mosaic virus, Bidens mottle virus, Birch leaf roll, Bitter gourd leaf curl betasatellite, Bitter gourd yellow mosaic virus, Bitter gourd yellow vein virus, Blackberry chlorotic ringspot virus, Blackberry vein banding, Blackberry virus A, Blackberry virus E, Blackberry virus F, Blackberry virus S, Blackberry virus Y, Blackberry yellow vein, Blackcurrant, Black currant leaf chlorosis associated virus, Blackcurrant leafroll, Blackcurrant reversion virus, Black grass cryptic virus 2, Black medic leafroll alphasatellite 1, Black medic leaf roll virus, Black raspberry necrosis virus, Blainvillea yellow spot virus, Blechum interveinal chlorosis virus, Blechum yellow vein virus, Blueberry fruit drop associated virus, Blueberry latent virus, Blueberry leaf mottle virus, Blueberry mosaic associated ophiovirus, Blueberry mosaic associated virus, Blueberry necrotic ring blotch virus, Blueberry red ringspot virus, Blueberry scorch virus, Blueberry shock virus, Blueberry shoestring virus, Blueberry virus A, Blue squill virus A, Boerhavia yellow spot virus, Bougainvillea chlorotic vein banding virus, Brassica napus RNA virus 1, Brassica yellows virus, Broad, Broad bean mottle virus, Broad bean true mosaic virus, Broad bean wilt virus 1, Broad bean wilt virus 2, Brome mosaic virus, Brome streak mosaic virus, Bromus, Bromus catharticus striate mosaic virus, Brugmansia mild mottle virus, Brugmansia mosaic virus, Brugmansia suaveolens mottle virus, Burdock mottle virus, Butterfly flower mosaic virus, Cabbage cytorhabdovirus 1, Cabbage leaf curl Jamaica virus, Cabbage leaf curl virus, Cacao Bacilliform SriLanka Virus, Cacao mild mosaic virus, Cacao swollen shoot CD virus, Cacao swollen shoot CE virus, Cacao swollen shoot Ghana J virus, Cacao swollen shoot Ghana K virus, Cacao swollen shoot Ghana L virus, Cacao swollen shoot Ghana M virus, Cacao swollen shoot Ghana N virus, Cacao swollen shoot Ghana Q virus, Cacao swollen shoot Ghana Rvirus, Cacao swollen shoot Togo A virus, Cacao swollen shoot virus, Cacao yellow mosaic virus, Cacao yellow vein banding virus, Cactus mild mottle virus, Cactus virus X, Caladenia virus A, Calanthe mild mosaic virus, Calibrachoa mottle virus, Calla lily chlorotic spot virus, Callistephus mottle virus, Camellia chlorotic dwarf, Camellia lemon glow virus, Cannabis cryptic virus, Canna yellow mottle associated virus, Canna yellow mottle virus, Canna yellow streak virus, Cape gooseberry ilarvirus 1, Caper latent virus, Capraria yellow spot Yucatan virus, Capsicum chlorosis orthotospovirus, Capsicum chlorosis virus, Cardamom bushy dwarf alphasatellite, Cardamom bushy dwarf virus, Cardamom mosaic virus, Cardiospermum yellow leaf curl betasatellite, Carnation cryptic virus 3, Carnation etched ring virus, Carnation Italian ringspot virus, Carnation latent virus, Carnation mottle virus, Carnation necrotic fleck virus, Carnation ringspot virus, Carnation vein mottle virus, Carnation yellow fleck virus, Carrot Ch virus 1, Carrot Ch virus 2, Carrot cryptic virus, Carrot mottle mimic virus, Carrot mottle mimic virus satellite RNA, Carrot mottle virus, Carrot mottle virus satellite RNA, Carrot necrotic dieback virus, Carrot red leaf luteovirus associated RNA, Carrot red leaf virus, Carrot thin leaf virus, Carrot torradovirus 1, Carrot virus Y, Carrot yellow leaf virus, Cassava associated gemy circularvirus 1, Cassava brown streak virus, Cassava common mosaic virus, Cassava Ivorian bacilliform virus, Cassava mosaic Madagascar alphasatellite, Cassava mosaic Madagascar virus, Cassava satellite virus, Cassava vein mosaic virus, Cassava virus C, Cassava virus X, Catharanthus mosaic virus, Catharanthus yellow mosaic virus, Cathranthus yellow vein mosaic virus, Caucasus prunus virus, Cauliflower mosaic virus, Celery latent virus, Celery mosaic virus, Centrosema yellow spot virus, Cereal yellow dwarf virus RPS, Cereal yellow dwarf virus RPV, Cestrum yellow leaf curling virus, Chayote mosaic virus, Chayote yellow mosaic Benin betasatellite, Chayote yellow mosaic virus, Chenopodium leaf curl virus, Chenopodium quinoa mitovirus 1, Cherry associated luteovirus, Cherry green ring mottle virus, Cherry leaf roll virus, Cherry leaf scorch small circular viroid, Cherry mottle leaf virus, Cherry necrotic rusty mottle virus, Cherry rasp leaf virus, Cherry rusty mottle associated virus, Cherry small circular viroid, Cherry tomato leaf curl virus, Cherry twisted leaf associated virus, Cherry virus A, Cherry virus Trakiya, Chickpea chlorosis Australia virus, Chickpea chlorosis virus, Chickpea chlorotic dwarf Pakistan virus, Chickpea chlorotic dwarf virus, Chickpea chlorotic dwarf virus D, Chickpea chlorotic stunt virus, Chickpea redleaf virus, Chickpea redleaf virus 2, Chickpea yellow dwarf virus, Chickpea yellows virus, Chili leaf curl betasatellite, Chili leaf curl Bhatinda betasatellite, Chili leaf curl Sri Lanka betasatellite, Chili leaf curl virus, Chilli leaf curl Ahmedabad virus, Chilli leaf curl alphasatellite, Chilli leaf curl betasatellite, Chilli leaf curl Gonda virus, Chilli leaf curl India alphasatellite, Chilli leaf curlIndia virus, Chilli leaf curl Kanpur virus, Chilli leaf curl Multan alphasatellite, Chilli leaf curl Multan virus, Chilli leaf curl Oman virus, Chilli leaf curl Sri Lanka virus, Chilli leaf curl Vellanad virus, Chilli leaf curl virus, Chilli leaf curl Virus, Chilli ringspot virus, Chilli veinal mottle virus, Chinese artichoke mosaic virus, Chinese wheat mosaic virus, Chinese yam necrotic mosaic virus, Chino del tomate Amazonas virus, Chino del tomate virus, Chloris striate mosaic virus, Chocolate lily virus A, Chrysanthemum chlorotic mottle viroid, Chrysanthemum stem necrosis virus, Chrysanthemum stunt viroid, Chrysanthemum virus B, Chrysanthemum virus R, Citrus bark cracking viroid, Citrus bent leaf viroid, Citrus chlorotic dwarf associated virus, Citrus chlorotic spot virus, Citrus concave gum, Citrus dwarfing viroid, Citrus exocortis viroid, Citrus exocortis viroid ( CEV, Citrus exocortis viroid ( CEVdg, Citrus exocortis viroid Sj 1, Citrus exocortis viroid Sj 2, Citrus exocortis Yucatan viroid, Citrus gummy bark viroid, Citrus leaf blotch virus, Citrus leaf rugose virus, Citrus leprosis virus C, Citrus leprosis virus C2, Citrus leprosis virus cytoplasmic type 2, Citrus leprosis virus N, Citrus leprosis virus nuclear type, Citrus mosaic sadwavirus, Citrus mosaic sadwavirus strain ND, Citrus necrotic spot virus, Citrus psorosis ophiovirus, Citrus psorosis virus, Citrus ringspot virus, Citrus sudden death, Citrus tatter leaf virus, Citrus tristeza virus, Citrus variegation virus, Citrus vein enation virus, Citrus viroid Illa, Citrus viroid Illb, Citrus viroid IIIc, Citrus viroid V, Citrus viroid VI, Citrus yellow mosaic virus, Citrus yellow vein, Citrus yellow vein clearing virus, Clematis chlorotic mottle virus, Cleome golden mosaic virus, Cleome leaf crumple alphasatellite, Cleome leaf crumple virus, Clerodendron golden mosaic virus, Clerodendron yellow mosaic virus, Clerodendrum chlorotic spot virus, Clerodendrum golden mosaic China virus, Clerodendrum golden mosaic Jiangsu virus, Clitoria virus Y, Clitoria yellow mottle virus, Clover yellow mosaic virus, Clover yellow vein virus, Cluster bean endornavirus 1, Cnidium virus X, Cnidoscolus mosaic leaf deformation virus, Coccinia mosaic Tamil Nadu virus, Coccinia mottle virus, Cocksfoot mild mosaic virus, Cocksfoot mottle virus, Coconut cadang, Coconut foliar decay alphasatellite 2, Coconut foliar decay alphasatellite 3, Coconut foliar decay alphasatellite 4, Coconut foliar decay alphasatellite 5, Coconut foliar decay alphasatellite 6, Coconut foliar decay alphasatellite 7, Codiaeum leaf curl betasatellite, Coffee ringspot dichorhavirus, Cole latent virus, Coleus blumei viroid 1, Coleus blumei viroid 2, Coleus blumei viroid 3, Coleus blumei viroid 5, Coleus blumei viroid 6, Coleus vein necrosis virus, Colocasia bobone disease, Colombian datura virus, Colombian potato soil, Columnea latent viroid, Commelina yellow mottle virus, Common bean, Common bean mottle virus, Common bean severe mosaic virus, Corchorus golden mosaic virus, Corchorus yellow spot virus, Corchorus yellow vein mosaic betasatellite, Corchorus yellow vein mosaic virus,Corchorus yellow vein Vietnam virus, Corchorus yellow vein virus, Cordyline virus 1, Cordyline virus 2, Cordyline virus 3, Cordyline virus 4, Costus stripe mosaic virus, Cotton atypical red leaf virus, Cotton chlorotic spot virus, Cotton leaf crumple virus, Cotton leaf curl Alabad virus, Cotton leaf curl alphasatellite, Cotton leaf curl Bangalore betasatellite, Cotton leaf curl Bangalore virus, Cotton leaf curl betasatellite, Cotton leaf curl Burewala alphasatellite, Cotton leaf curl Burewala betasatellite, Cotton leaf curl Burewala virus, Cotton leaf curl Gezira alphasatellite, Cotton leaf curl Gezira beta, Cotton leaf curl Gezira betasatellite, Cotton leaf curl Gezira virus, Cotton leaf curl Kokhran virus, Cotton leaf curl Lucknow alphasatellite, Cotton leaf curl Multan alphasatellite, Cotton leaf curl Multan betasatellite, Cotton leaf curl Multan defective betasatellite, Cotton leaf curl Multan DNA betasatellite, Cotton leaf curl Multan virus, Cotton leaf curl Multan virus satellite U36, Cotton leaf curl Shahdadpur virus, Cotton leaf curl virus, Cotton leafroll dwarf virus, Cotton yellow mosaic virus, Cowpea aphid, Cowpea bright yellow mosaic virus, Cowpea chlorotic mottle virus, Cowpea mild mottle virus, Cowpea mosaic virus, Cowpea mottle virus, Cowpea polerovirus 1, Cowpea polerovirus 2, Cowpea severe leaf curl, Cowpea severe mosaic virus, Cow vetch latent virus, Crassocephalum yellow vein virus, Crimson clover cryptic virus 2, Croton golden mosaic virus, Croton yellow vein mosaic alphasatellite, Croton yellow vein mosaic betasatellite, Croton yellow vein mosaic virus, Croton yellow vein virus, Cucumber Bulgarian latent virus, Cucumber chlorotic leaf virus, Cucumber fruit mottle mosaic virus, Cucumber green mottle mosaic virus, Cucumber leaf spot virus, Cucumber mosaic virus, Cucumber mosaic virus satellite RNA, Cucumber mosaic virus satellite RNA CARNA, Cucumber mosaic virus ( strain Mf), Cucumber mosaic virus ( strain Pl, Cucumber mosaic virus (strain Ri, Cucumber necrosis virus, Cucumber vein, Cucumber vein yellowing virus, Cucumber yellows virus, Cucumis melo alphaendornavirus, Cucurbit aphid, Cucurbit aphid borne yellows virus associated RNA, Cucurbita yellow vein virus, Cucurbit chlorotic yellows virus, Cucurbit leaf crumple virus, Cucurbit mild mosaic virus, Cucurbit vein banding virus, Cucurbit yellow mosaic alphasatellite, Cucurbit yellow stunting disorder virus, Currant latent virus, Currant virus A, Cycad leaf necrosis virus, Cycas necrotic stunt virus, Cymbidium chlorotic mosaic virus, Cymbidium mosaic virus, Cymbidium ringspot virus, Cypripedium virus Y, Cyrtanthus elatus virus A, Dahlia latent viroid, Dahlia mosaic virus, Dalechampia chlorotic mosaic virus, Daphne mosaic virus, Daphne virus S, Daphne virus Y, Dapple apple viroid, Dapple peach fruit disease viroid, Dapple plum and peach fruit disease viroid, Dasheen mosaic virus, Datura leaf curl virus, Datura leaf distortion virus, Datura yellow vein nucleorhabdovirus, Deinbollia mosaic virus, Deinbollia mosaic virus DB_T0, Deltasatellitesat, Dendrobium chlorotic mosaic virus, Desmodium leaf distortion deltasatellite, Desmodium leaf distortion virus, Desmodium mottle virus, Diascia yellow mottle virus, Dicliptera yellow mottle virus, Digitaria ciliaris striate mosaic virus, Digitaria didactyla striate mosaic virus, Dill cryptic virus 1, Dill cryptic virus 2, Diodia vein chlorosis virus, Dioscorea bacilliform AL virus, Dioscorea bacilliform RT virus, Dioscorea bacilliform RT virus 1, Dioscorea bacilliform RT virus 2, Dioscorea bacilliform SN virus, Dioscorea bacilliform TR virus, Dioscorea bacilliform virus, Dioscorea mosaic associated virus, Dioscorea mosaic virus, Dioscorea nummularia, Diuris virus A, Diuris virus B, Diuris virus Y, Dolichos yellow mosaic virus, Donkey orchid symptomless virus, Donkey orchid virus A, Dracaena mottle virus, Drakaea virus A, Duranta leaf curl virus, Dweet mottle virus, East African cassava mosaic Cameroon virus, East African cassava mosaic Kenya virus, East African cassava mosaic Malawi virus, East African cassava mosaic virus, East African cassava mosaic virus (Uganda variant), East African cassava mosaic Zanzibar virus, East Asian Passiflora distortion virus, East Asian Passiflora virus, Eclipta yellow vein mosaic virus, Eclipta yellow vein virus, Eggplant latent viroid, Eggplant mosaic virus, Eggplant mottled crinkle virus, Eggplant mottled dwarf nucleorhabdovirus, Elderberry aureusvirus 1, Elderberry carlavirus A, Elderberry carlavirus B, Elderberry carlavirus C, Elderberry carlavirus D, Elderberry carlavirus E, Elderberry latent virus, Eleusine indica associated virus, Elm mottle virus, Emilia sonchifolia yellow vein Thailand virus, Emilia yellow vein Fujian betasatellite, Emilia yellow vein Fujian virus, Emilia yellow vein virus, Endive necrotic mosaic virus, Epirus cherry virus, Eragrostis curvula streak virus, Eragrostis minor streak virus, Eragrostis streak virus, Erectites yellow mosaic virus, Erectites yellow mosaic virus satellite DNA beta, Erysimum latent virus, Ethiopian tobacco bushy top virus, Ethiopian tobacco bushy top virus satellite RNA, Euonymus yellow mottle associated virus, Euonymus yellow vein virus, Eupatorium yellow vein betasatellite, Eupatorium yellow vein mosaic betasatellite, Eupatorium yellow vein mosaic virus, Eupatorium yellow vein virus, Euphorbia caput, Euphorbia heterophylla associated gemy circularvirus, Euphorbia leaf curl Guangxi virus, Euphorbia leaf curl virus, Euphorbia mosaic Peru virus, Euphorbia mosaic virus, Euphorbia ringspot virus, Euphorbia yellow leaf curl virus, Euphorbia yellow mosaic alphasatellite, Euphorbia yellow mosaic virus, Euphorbia yellow mosaic virus associated DNA 1, European mountain ash ringspot, Exomis microphylla associated virus, Faba bean necrotic stunt alphasatellite, Faba bean necrotic stunt alphasatellite 1, Faba bean necrotic stunt alphasatellite 2, Faba bean necrotic stunt virus, Faba bean necrotic yellows alphasatellite 2, Faba bean necrotic yellows Cl alphasatellite, Faba bean necrotic yellows C7 alphasatellite, Faba bean necrotic yellows C9 alphasatellite,Faba bean necrotic yellows virus, Faba bean necrotic yellows virus associated alphasatellite 1, Faba bean polerovirus 1, Faba bean yellow leaf virus, Fig badnavirus 1, Fig cryptic virus, Fig fleck, Fig mosaic emaravirus, Figwort mosaic virus, Fiji disease virus, Foxtail mosaic virus, Fragaria chiloensis latent virus, Frangipani mosaic virus, Freesia mosaic virus, Freesia sneak ophiovirus, French bean leaf curl betasatellite, French bean leaf curl virus, French bean severe leaf curl virus, Fritillary virus Y, Furcraea necrotic streak virus, Gaillardia latent virus, Garlic common latent virus, Garlic latent virus, Garlic mite, Garlic virus A, Garlic virus B, Garlic virus C, Garlic virus D, Garlic virus E, Garlic virus X, Gayfeather mild mottle virus, Gentian Kobu, Gentian ovary ringspot virus, Gloriosa stripe mosaic virus, Gloxinia tospovirus, Gomphocarpus mosaic virus, Gompholobium virus A, Gooseberry vein banding associated virus, Gossypium darwinii symptomless alphasatellite, Gossypium darwinii symptomless virus, Gossypium davidsonii symptomless alphasatellite, Gossypium mustelinum symptomless alphasatellite, Gossypium punctatum mild leaf curl virus, Grapevine Algerian latent virus, Grapevine Anatolian ringspot virus, Grapevine associated tymo, Grapevine asteroid mosaic associated virus, Grapevine badnavirus 1, Grapevine berry inner necrosis virus, Grapevine Bulgarian latent virus, Grapevine Cabernet Sauvignon reovirus, Grapevine chrome mosaic virus, Grapevine deformation virus, Grapevine enamovirus 1, Grapevine fabavirus, Grapevine fanleaf virus, Grapevine fanleaf virus satellite RNA, Grapevine fleck virus, Grapevine geminivirus, Grapevine geminivirus A, Grapevine latent viroid, Grapevine leafroll, Grapevine Pinot gris virus, Grapevine red blotch virus, Grapevine Red Globe virus, Grapevine redleaf Grapevine Roditis leaf discoloration, Grapevine rupestris stem pitting, Grapevine Rupestris stem pitting virus, Grapevine rupestris vein feathering virus, Grapevine satellite virus, Grapevine Syrah virus 1, Grapevine vein clearing virus, Grapevine viroid, Grapevine virus A, Grapevine virus B, Grapevine virus D, Grapevine virus E, Grapevine virus F, Grapevine virus G, Grapevine virus H, Grapevine virus I, Grapevine virus J, Grapevine virus K, Grapevine yellow speckle viroid 1, Grapevine yellow speckle viroid 2, Groundnut bud necrosis virus, Groundnut chlorotic fan, Groundnut ringspot and Tomato chlorotic spot virus reassortant, Groundnut ringspot tospovirus, Groundnut ringspot virus, Groundnut rosette assistor virus, Groundnut rosette virus, Groundnut rosette virus Malawi chlorotic satellite RNA, Groundnut rosette virus Nigerian green satellite RNA, Groundnut rosette virus Nigerian mild satellite RNA, Groundnut rosette virus satellite RNA, Groundnut rosette virus Yellow blotch satellite RNA, Groundnut yellow spot virus, Guar leaf curl alphasatellite, Habenaria mosaic virus, Hardenbergia mosaic virus, Hardenbergia virus A, Hedyotis yellow mosaic betasatellite, Helenium virus S, Helianthus annuus alphaendornavirus, Helleborus mosaicvirus, Helleborus net necrosis virus, Hemidesmus yellow mosaic virus, Henbane mosaic virus, Heracleum latent virus, Hibbertia virus Y, Hibiscus bacilliform virus GDI, Hibiscus chlorotic ringspot virus, Hibiscus golden mosaic virus, Hibiscus green spot virus 2, Hibiscus latent Fort Pierce virus, Hibiscus latent Singapore virus, Hibiscus leaf curl alphasatellite, Hibiscus vein enation betasatellite, High Plains wheat mosaic emaravirus, Hippeastrum latent virus, Hippeastrum mosaic virus, Holcus lanatus, Hollyhock leaf crumple virus, Hollyhock leaf crumple virus satellite DNA, Hollyhock leaf curl virus, Hollyhock yellow vein mosaic Islamabad virus, Hollyhock yellow vein mosaic virus, Hollyhock yellow vein virus, Hollyhock yellow vein virus associated symptomless alphasatellite, Honeysuckle ringspot virus, Honeysuckle yellow vein beta, Honeysuckle yellow vein betasatellite, Honeysuckle yellow vein mosaic beta, Honeysuckle yellow vein mosaic betasatellite, Honeysuckle yellow vein mosaic disease associated satellite DNA beta, Honeysuckle yellow vein mosaic virus, Honeysuckle yellow vein virus, Hop latent viroid, Hop latent virus, Hop mosaic virus, Hop stunt viroid, Hop trefoil cryptic virus 2, Hordeum vulgare alphaendornavirus, Horsegram yellow mosaic virus, Horseradish latent virus, Hosta virus X, Hot pepper alphaendornavirus, Hoya chlorotic spot virus, Hubei picorna, Hyacinth mosaic virus, Hybanthus yellow mosaic virus, Hydrangea chlorotic mottle virus, Hydrangea mosaic virus, Hydrangea ringspot virus, Hypericum japonicum associated circular DNA virus, Impatiens flower break virus, Impatiens necrotic spot tospovirus, Impatiens necrotic spot virus, Imperata yellow mottle virus, Indian cassava mosaic virus, Indian citrus ringspot virus, Indian peanut clump virus, Indian peanut clump virus H, Indian peanut clump virus L, Ipomea begomovirus satellite 1, Ipomoea yellow vein virus, Iranian johnsongrass mosaic virus, Iranian wheat stripe tenuivirus, Iresine viroid 1, Iris mild mosaic virus, Iris severe mosaic virus, Iris yellow spot virus, Ivy ringspot, Ixeridium yellow mottle virus 1, Ixeridium yellow mottle virus 2, Jacquemontia mosaic Yucatan virus, Jacquemontia yellow mosaic virus, Jacquemontia yellow vein virus, Japanese holly fern mottle virus, Japanese iris necrotic ring virus, Japanese soil, Japanese yam mosaic virus, Jasmine mosaic, Jasmine virus C, Jasmine virus H, Jasmine virus T, Jatropha curcas mosaic virus, Jatropha leaf crumple India virus, Jatropha leaf crumple virus, Jatropha leaf curl Gujarat virus, Jatropha leaf curl virus, Jatropha leaf yellow mosaic Katarniaghat virus, Jatropha mosaic India virus, Jatropha mosaic Nigeria virus, Jatropha mosaic virus, Jatropha yellow mosaic virus, Johnsongrass mosaic virus, Jujube mosaic, Juncus maritimus associated virus, Kalanchoe latent virus, Kalanchoe mosaic virus, Kalanchoe top, Kenaf leaf curl betasatellite, Kenaf leaf curl virus, Kennedya yellow mosaic virus, Keunjorong mosaic virus, Konjac mosaic virus, Kudzu mosaic virus, Kyuri green mottle mosaic virus, Lagenaria mild mosaic virus,Lagenaria siceraria endornavirus, Lamium leaf distortion virus, Lamium mild mosaic virus, Leek yellow stripe virus, Leonurus mosaic virus, Lettuce big, Lettuce chlorosis virus, Lettuce chordovirus 1, Lettuce infectious yellows virus, Lettuce Italian necrotic virus, Lettuce mosaic virus, Lettuce necrotic leaf curl virus, Lettuce necrotic stunt virus, Lettuce necrotic yellows virus, Lettuce ring necrosis virus, Lettuce virus X, Lettuce yellow mottle virus, Leucas z.eylanica yellow vein virus satellite DNA beta, Ligustrum necrotic ringspot virus, Ligustrum virus A, Lilac leaf chlorosis virus, Lilac ring mottle virus, Lily mottle virus, Lily symptomless virus, Lily virus X, Lily yellow mosaic virus, Limeum africanum associated virus, Lindernia anagallis yellow vein virus, Lindernia anagallis yellow vein virus satellite DNA beta, Lisianthus enation leaf curl virus, Lisianthus necrosis virus, Little cherry virus 1, Little cherry virus 2, Lolium latent virus, Lolium perenne, Longan witches broom, Lucerne transient streak virus, Lucerne transient streak virus satellite RNA, Lucky bamboo bacilliform virus, Ludwigia leaf curl distortion betasatellite, Ludwigia leaf distortion betasatellite, Ludwigia yellow vein Vietnam virus, Ludwigia yellow vein virus, Luffa aphid, Luffa begomovirus betasatellite, Luffa puckering and leaf distortion, Luffa yellow mosaic virus, Lupinus mosaic virus, Lychee viroid, Lychnis ringspot virus, Lycianthes yellow mosaic virus, Maclura mosaic virus, Macroptilium bright mosaic virus, Macroptilium common mosaic virus, Macroptilium golden mosaic virus, Macroptilium golden yellow mosaic virus, Macroptilium mosaic Puerto Rico virus, Macroptilium yellow mosaic virus, Macroptilium yellow net virus, Macroptilium yellow spot virus, Macroptilium yellow vein virus, Maize, Maize associated pteridovirus, Maize chlorotic dwarf virus, Maize chlorotic mottle virus, Maize dwarf mosaic virus, Maize Iranian mosaic nucleorhabdovirus, Maize mosaic nucleorhabdovirus, Maize necrotic streak virus, Maize ray ado fino virus, Maize rough dwarf virus, Maize sterile stunt virus, Maize streak dwarfing virus, Maize streak Reunion virus, Maize streak virus, Maize striate mosaic virus, Maize stripe tenuivirus, Maize white line mosaic virus, Maize yellow dwarf virus, Maize yellow dwarf virus RMV, Maize yellow mosaic virus, Maize yellow striate virus, Malachra yellow mosaic virus, Malachra yellow vein mosaic betasatellite, Malachra yellow vein mosaic virus, Mai de Rio Cuarto virus, Malus domestica virus A, Malvastrum bright yellow mosaic virus, Malvastrum leaf curl betasatellite, Malvastrum leaf curl deltasatellite, Malvastrum leaf curl Guangdong betasatellite, Malvastrum leaf curl Guangdong virus, Malvastrum leaf curl Philippines virus, Malvastrum leaf curl virus, Malvastrum yellow mosaic Cameroon alphasatellite, Malvastrum yellow mosaic Helshire virus, Malvastrum yellow mosaic Jamaica virus, Malvastrum yellow mosaic virus, Malvastrum yellow vein Baoshan virus, Malvastrum yellow vein betasatellite, Malvastrum yellow vein Cambodia virus, Malvastrum yellow vein Changa Manga virus,Malvastrum yellow vein Chitwan betasatellite, Malvastrum yellow vein Honghe virus, Malvastrum yellow vein Lahore virus, Malvastrum yellow vein virus, Malvastrum yellow vein Yunnan virus, Malvastrum yellow vein Yunnan virus satellite DNA beta, Malva vein clearing virus, Maracuja mosaic virus, Mashua virus Y, Medicago sativa alphapartitivirus 1, Medicago sativa amalgavirus 1, Melandrium yellow fleck virus, Melochia mosaic virus, Melochia yellow mosaic virus, Melon aphid, Melon chlorotic leaf curl virus, Melon chlorotic mosaic alphasatellite, Melon chlorotic mosaic virus, Melon chlorotic spot virus, Melon mild mottle virus, Melon necrotic spot virus, Melon partitivirus, Melon severe mosaic tospovirus, Melon yellowing, Melon yellow mosaic virus, Melon yellow spot virus, Merremia leaf curl virus, Merremia mosaic Puerto Rico virus, Merremia mosaic virus, Mesta yellow vein mosaic alphasatellite, Mesta yellow vein mosaic Bahraich virus, Mesta yellow vein mosaic virus, Mexican papita viroid, Mikania micrantha mosaic virus, Milk vetch dwarf alphasatellite 3, Milk vetch dwarf Cl 0 alphasatellite, Milk vetch dwarf C2 alphasatellite, Milk vetch dwarf C3 alphasatellite, Milk vetch dwarf virus, Mimosa yellow leaf curl virus, Mimosa yellow leaf curl virus satellite DNA beta, Mint vein banding, Mint virus 1, Mint virus X, Mirabilis jalapa mottle virus, Mirabilis leaf curl betasatellite, Mirabilis leaf curl India virus associated betasatellite, Mirabilis leaf curl virus, Miscanthus streak virus, Moroccan pepper virus, Moroccan watermelon mosaic virus, Morogoro maize, Motherwort yellow mottle virus, Mulberry crinkle, Mulberry mosaic dwarf associated virus, Mulberry mosaic leaf roll associated virus, Mulberry vein banding virus, Mume virus A, Mungbean yellow mosaic India virus, Mungbean yellow mosaic India virus associated betasatellite, Mungbean yellow mosaic India virus Indonesia, Mungbean yellow mosaic virus, name, Nandina mosaic virus, Nanovirus, Narcissus common latent virus, Narcissus degeneration virus, Narcissus latent virus, Narcissus late season yellows virus, Narcissus mosaic virus, Narcissus mosaic virus strain New Zealand, Narcissus symptomless virus, Narcissus yellow stripe virus, Navel orange infectious mottling virus, Nectarine marafivirus M, Nectarine stem pitting associated virus, Nemesia ring necrosis virus, Nerine latent virus, Nerine virus X, Nerine yellow stripe virus, New World begomovirus associated satellite DNA isolate 177H3, New World begomovirus associated satellite DNA isolate 177N2, New World begomovirus associated satellite DNA isolate 177N4, New World begomovirus associated satellite DNA isolate 177N7, New World begomovirus associated satellite DNA isolate 228H1, New World begomovirus associated satellite DNA isolate 228H3, New World begomovirus associated satellite DNA isolate 228H5, New World begomovirus associated satellite DNA isolate 228H6, New World begomovirus associated satellite DNA isolate 228H6B, New World begomovirus associated satellite DNA isolate 404N1, New Worldbegomovirus associated satellite DNA isolate 404N2, New World begomovirus associated satellite DNA isolate 404N3, New World begomovirus associated satellite DNA isolate 412N1, New World begomovirus associated satellite DNA isolate 412N2, New World begomovirus associated satellite DNA isolate 412N3, New World begomovirus associated satellite DNA isolate 413N1, New World begomovirus associated satellite DNA isolate 413N2, New World begomovirus associated satellite DNA isolate 413N3, New World begomovirus associated satellite DNA isolate 413N5, New World begomovirus associated satellite DNA isolate 414N1, New World begomovirus associated satellite DNA isolate 414N2, New World begomovirus associated satellite DNA isolate 414N3, New World begomovirus associated satellite DNA isolate 424N1, New World begomovirus associated satellite DNA isolate 424N2, New World begomovirus associated satellite DNA isolate 424N3, New World begomovirus associated satellite DNA isolate Hl, Noni mosaic virus, Nootka lupine vein clearing virus, Northern cereal mosaic cytorhabdovirus, Oat blue dwarf virus, Oat chlorotic stunt virus, Oat dwarf virus, Oat golden stripe virus, Oat mosaic virus, Oat necrotic mottle virus, Oat sterile dwarf virus, Obuda pepper virus, Ocimum basilicum RNA virus 1, Ocimum basilicum RNA virus 2, Ocimum golden mosaic virus, Ocimum mosaic virus, Ocimum yellow vein virus, Odontoglossum ringspot virus, OkLCV satDNA 10, OkLCV satDNA 27, OkLCV satDNA 3, Okra enation leaf curl betasatellite, Okra enation leaf curl virus, Okra leaf curl alphasatellite, Okra leaf curl betasatellite, Okra leaf curl Cameroon virus, Okra leaf curl disease associated sequence, Okra leaf curl India virus, Okra leaf curl Mali virus satellite DNA beta, Okra leaf curl Oman betasatellite, Okra leaf curl Oman virus, Okra leaf curl virus, Okra mottle virus, Okra yellow crinkle Cameroon alphasatellite, Okra yellow crinkle virus, Okra yellow mosaic Mexico virus, Okra yellow vein disease associated sequence, Okra yellow vein mosaic virus, Olive latent virus 1, Olive latent virus 2, Olive leaf yellowing, Olive mild mosaic virus, Olive viral satellite RNA, Onion yellow dwarf virus, Ononis yellow mosaic virus, Opium poppy mosaic virus, Opuntia virus 1, Opuntia virus 2, Opuntia virus X, Orchid fleck dichorhavirus, Ornithogalum mosaic virus, Ornithogalum virus 2, Ornithogalum virus 3, Oryza rufipogon alphaendornavirus, Oryza sativa alphaendornavirus, Ourmia melon virus, Oxalis yellow vein virus, Pagoda yellow mosaic associated virus, Panax ginseng flexivirus 1, Panax notoginseng virus A, Panax notoginseng virus B, Panax virus Y, Panicum ecklonii, Panicum mosaic satellite virus, Panicum mosaic virus, Panicum streak virus, Papaya cytorhabdovirus, Papaya leaf crumple virus, Papaya leaf curl alphasatellite, Papaya leaf curl associated betasatellite, Papaya leaf curl betasatellite, Papaya leaf curl China betasatellite, Papaya leaf curl China virus, Papaya leaf curl China virus GX4, Papaya leaf curl China virus HeNZMl, Papaya leaf curl Faisalabadvirus, Papaya leaf curl Guandong virus, Papaya leaf curl Guangdong virus, Papaya leaf curl virus, Papaya leaf distortion mosaic virus, Papaya lethal yellowing virus, Papaya meleira virus, Papaya mosaic virus, Papaya ringspot virus, Papaya ringspot virus P, Papaya ringspot virus W, Paprika mild mottle virus, Parietaria mottle virus, Paris mosaic necrosis virus, Paris virus 1, Parsley severe stunt alphasatellite 3, Parsley severe stunt alphasatellite 4, Parsley severe stunt associated virus, Parsnip yellow fleck virus, Parthenium leaf curl alphasatellite, Paspalum dilatatum striate mosaic virus, Paspalum striate mosaic virus, Passiflora chlorosis virus, Passiflora edulis symptomless virus, Passiflora foetida virus Y, Passiflora latent virus, Passion fruit chlorotic mottle virus, Passion fruit green spot virus, Passionfruit leaf distortion virus, Passion fruit mosaic virus, Passionfruit severe leaf distortion virus, Passion fruit woodiness virus, Passion fruit yellow mosaic virus, Patchouli mild mosaic virus, Patrinia mild mottle virus, Pavonia mosaic virus, Pavonia yellow mosaic virus, Peace lily mosaic virus, Peach associated luteovirus, Peach chlorotic mottle virus, Peach latent mosaic viroid, Peach marafivirus D, Peach mosaic virus, Peach rosette mosaic virus, Peach virus D, Pea early, Pea enation mosaic virus 1, Pea enation mosaic virus 2, Pea enation mosaic virus satellite RNA, Pea leaf distortion betasatellite, Pea leaf distortion virus, Pea necrotic yellow dwarf alphasatellite 1, Pea necrotic yellow dwarf alphasatellite 3, Pea necrotic yellow dwarf virus, Peanut bud necrosis virus, Peanut chlorotic streak virus, Peanut clump virus, Peanut clump virus Ni, Peanut mottle virus, Peanut stripe virus, Peanut stunt virus, Peanut stunt virus satellite RNA, Pear black necrotic leaf spot virus, Pear blister canker viroid, Pear latent virus, Pea seed, Pea stem necrosis virus, Pea streak virus, Pea yellow stunt virus, Pecan mosaic, Pedilanthus leaf curl alphasatellite, Pedilanthus leaf curl virus, Pelargonium chlorotic ring pattern virus, Pelargonium flower break virus, Pelargonium leaf curl virus, Pelargonium line pattern virus, Pelargonium necrotic spot virus, Pelargonium ringspot virus, Pelargonium vein banding virus, Pelargonium zonate spot virus, Pennisetum mosaic virus, Pepino mosaic virus, Pepo aphid, Pepper blistering leaf virus, Pepper chat fruit viroid, Pepper chlorotic spot virus, Pepper cryptic virus 1, Pepper cryptic virus 2, Pepper enamovirus, Pepper golden mosaic virus, Pepper huasteco yellow vein virus, Pepper leaf curl Bangladesh virus, Pepper leaf curl Lahore virus, Pepper leaf curl Lahore Virus, Pepper leaf curl Pakistan virus, Pepper leaf curl virus, Pepper leaf curl virus isolate Varanasi, Pepper leaf curl virus satellite DNA beta, Pepper leaf curl Yunnan virus, Pepper leaf curl Yunnan virus satellite DNA beta, Pepper leafroll virus, Pepper mild mottle virus, Pepper mottle virus, Pepper ringspot virus, Pepper veinal mottle virus, Pepper vein yellows virus, Pepper vein yellows virus 2, Pepper vein yellows virus 5, Pepper virus A, Pepper yellow dwarf virus, Pepper yellow leaf curl Aceh virus, Pepper yellowleaf curl Indonesia virus, Pepper yellow leaf curl Thailand virus, Pepper yellow leaf curl virus, Pepper yellow leaf curl virus PSSWS, Pepper yellow leaf curl virus TDWS, Pepper yellow mosaic virus, Pepper yellow vein Mali virus, Peristrophe mosaic virus, Persea americana alphaendornavirus 1, Persimmon cryptic virus, Persimmon latent virus, Persimmon viroid, Persimmon viroid 2, Persimmon virus A, Persimmon virus B, Peru tomato mosaic virus, Petunia chlorotic mottle virus, Petunia vein clearing virus, Phaius virus X, Phaseolus vulgaris alphaendornavirus 1, Phaseolus vulgaris alphaendornavirus 2, Phaseolus vulgaris endornavirus 3, Phasey bean mild yellows virus, Phlox virus B, Phlox virus M, Phlox virus S, Physalis mottle virus, Physalis rugose mosaic virus, Physostegia chlorotic mottle virus, Pigeonpea sterility mosaic emaravirus 1, Pigeonpea sterility mosaic emaravirus 2, Pineapple bacilliform CO virus, Pineapple bacilliform ER virus, Pineapple mealybug wilt, Pinus nigra virus 1, Piper yellow mottle virus, Pistachio ampelovirus A, Pitaya virus X, Plantago asiatica mosaic virus, Plantago lanceolata latent virus, Platycodon mild mottle virus, Pleione flower breaking virus, Pleione virus Y, Plum bark necrosis stem pitting, Plumeria mosaic virus, Plum pox virus, Plum pox virus D, Plum pox virus strain Dideron, Plum pox virus strain El Amar, Plum pox virus strain Marcus, Poaceae associated gemy circularvirus 1, Poa semilatent virus, Poinsettia latent virus, Poinsettia mosaic virus, Pokeweed mosaic virus, Polygala garcinii associated virus, Polygonum ringspot tospovirus, Polygonum ringspot virus, Polyscias mosaic virus, Poplar mosaic virus, Poplar mosaic virus (ATCC PV257), Portulaca latent viroid, Potato apical leaf curl disease, Potato aucuba mosaic virus, Potato black ringspot virus, Potato latent virus, Potato leafroll virus, Potato mop, Potato necrosis virus, Potato rough dwarf virus, Potato spindle tuber viroid, Potato virus A, Potato virus B, Potato virus H, Potato virus M, Potato virus P, Potato virus S, Potato virus T, Potato virus U, Potato virus V, Potato virus X, Potato virus Y, Potato virus Y strain C, Potato virus Y strain N, Potato virus Y strain nnp, Potato virus Y strain NTN, Potato virus Y strain (N)W, Potato virus Y strain O, Potato virus Y strain Wilga, Potato virus Y strain Wilga 156var, Potato virus Y strain Z, Potato virus Y Wilga MV175, Potato virus Y Wilga MV99, Potato yellow dwarf nucleorhabdovirus, Potato yellow mosaic Panama virus, Potato yellow mosaic Trinidad virus, Potato yellow mosaic virus, Potato yellow vein virus, Potexvirus sp., Pothos latent virus, Pouzolzia golden mosaic virus, Pouzolzia mosaic Guangdong virus, Premna leaf curl virus, Primula malacoides virus China / Mar2007, Privet leaf blotch, Privet ringspot virus, Prune dwarf virus, Prunus geminivirus A, Prunus necrotic ringspot virus, Prunus virus F, Prunus virus T, Pumpkin polerovirus, Radish leaf curl betasatellite, Radish leaf curl virus, Radish leaf curl virus betasatellite, Radish mosaic virus, Ramie mosaic virus, Ramie mosaic Yunnan virus,Ranunculus mild mosaic virus, Ranunculus white mottle virus, Raphanus sativus cryptic virus1, Raphanus sativus cryptic virus 2, Raphanus sativus cryptic virus 3, Raspberry bushy dwarf virus, Raspberry latent virus, Raspberry leaf blotch emaravirus, Raspberry leaf mottle virus, Raspberry ringspot virus, Raspberry vein chlorosis virus, Rattail cactus necrosis, Redbud yellow ringspot, Red clover cryptic virus 1, Red clover cryptic virus 2, Red clover mottle virus, Red clover necrotic mosaic virus, Red clover nepovirus A, Red clover vein mosaic virus, Reed chlorotic stripe virus, Rehmannia mosaic virus, Rehmannia virus 1, Rhododendron virus A, Rhopalanthe virus Y, Rhynchosia golden mosaic Havana virus, Rhynchosia golden mosaic Sinaloa virus, Rhynchosia golden mosaic virus, Rhynchosia golden mosaic Yucatan virus, Rhynchosia mild mosaic virus, Rhynchosia rugose golden mosaic virus, Rhynchosia yellow mosaic betasatellite, Rhynchosia yellow mosaic India virus, Rhynchosia yellow mosaic virus, Ribes americanum virus A, Ribgrass mosaic virus, Rice black, Rice black streaked dwarf virus, Rice dwarf virus, Rice gall dwarf virus, Rice grassy stunt tenuivirus, Rice hoja blanca tenuivirus, Rice latent virus 1, Rice latent virus 2, Rice necrosis mosaic virus, Rice ragged stunt virus, Rice stripe mosaic virus, Rice stripe necrosis virus, Rice stripe tenuivirus, Rice tungro bacilliform virus, Rice tungro spherical virus, Rice virus A, Rice yellow mottle virus, Rice yellow stunt nucleorhabdovirus, Rosa multiflora cryptic virus, Rosa rugosa leaf distortion virus, Rose cryptic virus 1, Rose leaf curl betasatellite, Rose leaf curl virus, Rose leaf rosette, Rose rosette emaravirus, Rose spring dwarf, Rose yellow mosaic virus, Rose yellow vein virus, Rottboellia yellow mottle virus, Rubber viroid India / 2009, Rubus canadensis virus 1, Rubus yellow net virus, Rudbeckia flower distortion virus, Ryegrass mosaic virus, Ryegrass mottle virus, Saccharum streak virus, Saffron latent virus, Saguaro cactus virus, Salvia divinorum RNA virus 1, Salvia hispanica RNA virus 1, Sambucus virus S, Sarcochilus virus Y, Satellite maize white line mosaic virus, Satellite tobacco mosaic virus, Satellite tobacco necrosis virus2, Satellite tobacco necrosis virus C, Satsuma dwarf virus, Sauropus leaf curl disease associated DNA beta, Sauropus leaf curl virus, Sauropus yellowing virus, Scallion mosaic virus, Schefflera ringspot virus, Schlumbergera virus X, Senecio yellow mosaic virus, Senna leaf curl virus, Senna mosaic virus, Shallot latent virus, Shallot virus X, Shallot yellow stripe virus, Sida angular mosaic virus, Sida Brazil virus, Sida chlorotic vein virus, Sida ciliaris golden mosaic virus, Sida common mosaic virus, Sida golden mosaic Brazil virus, Sida golden mosaic Florida virus, Sida golden mosaic Honduras virus, Sida golden mosaic virus, Sida golden yellow vein virus, Sida leaf curl alphasatellite, Sida leaf curl virus, Sida micrantha mosaic virus, Sida mosaic Bolivia virus 2, Sida mosaic Sinaloa virus, Sida mottle Alagoas virus, Sida mottle virus, Sidastrum golden leaf spot virus, Sida yellow blotch virus, Sida yellowleaf curl virus, Sida yellow mosaic Alagoas virus, Sida yellow mosaic China satellite DNA beta, Sida yellow mosaic China virus, Sida yellow mosaic Yucatan virus, Sida yellow mottle virus, Sida yellow net virus, Sida yellow vein alphasatellite, Sida yellow vein China alphasatellite, Sida yellow vein mosaic alphasatellite, Sida yellow vein Vietnam alphasatellite, Sida yellow vein Vietnam virus, Sida yellow vein Vietnam virus satellite DNA beta, Sida yellow vein virus, Sida yellow vein virus satellite DNA beta, Siegesbeckia yellow vein betasatellite, Siegesbeckia yellow vein Guangxi virus, Siegesbeckia yellow vein Guangxi virus satellite DNA beta, Siegesbeckia yellow vein virus, Sinapis alba cryptic virus 1, Small begomovirus, Soil, Solanum mosaic Bolivia virus, Solanum nodiflorum mottle virus, Sonchus yellow net nucleorhabdovirus, Sophora alopecuroides yellow stunt alphasatellite la, Sophora alopecuroides yellow stunt alphasatellite 4, Sophora alopecuroides yellow stunt alphasatellite 7a, Sophora alopecuroides yellow stunt alphasatellite 8, Sophora alopecuroides yellow stunt alphasatellite 9, Sophora yellow stunt virus, Sorghum arundinaceum associated virus, Sorghum mosaic virus, South African cassava mosaic virus, Southern bean mosaic virus, Southern cowpea mosaic virus, Southern rice black, Southern tomato virus, Sowbane mosaic virus, Soybean blistering mosaic virus, Soybean chlorotic blotch virus, Soybean chlorotic mottle virus, Soybean chlorotic spot virus, Soybean crinkle leaf virus, Soybean dwarf virus, Soybean latent spherical virus, Soybean leaf, Soybean mild mottle virus, Soybean mosaic virus, Soybean mosaic virus 4469, Soybean mosaic virus N, Soybean Putnam virus, Soybean vein necrosis virus, Soybean yellow common mosaic virus, Soybean yellow mosaic virus, Soybean yellow mottle mosaic virus, Spartina mottle virus, Spilanthes yellow vein virus, Spinach amalgavirus 1, Spinach cryptic virus 1, Spinach curly top Arizona virus, Spinach latent virus, Spinach severe curly top virus, Spinach yellow vein Sikar virus, Spiranthes mosaic virus 3, Sporobolus striate mosaic virus 1, Sporobolus striate mosaic virus 2, Squash chlorotic leaf spot virus, Squash leaf curl China virus, Squash leaf curl Philippines virus, Squash leaf curl virus, Squash leaf curl Yunnan virus, Squash mosaic virus, Squash vein yellowing virus, Squash yellow mild mottle virus, Sri Lankan cassava mosaic virus, Stachytarpheta leaf curl virus, Stipagrostis associaed virus, Strawberry chlorotic fleck, Strawberry crinkle cytorhabdovirus, Strawberry crinkle virus, Strawberry cytorhabdovirus 1, Strawberry latent ringspot virus, Strawberry mild yellow edge virus, Strawberry mottle virus, Strawberry necrotic shock virus, Strawberry pallidosis, Strawberry polerovirus 1, Strawberry vein banding virus, Strawberry virus 1, Suakwa aphid, Subterranean clover mottle virus, Subterranean clover stunt alphasatellite 2, Subterranean clover stunt C2 alphasatellite, Subterranean clover stunt C6 alphasatellite, Subterranean clover stunt virus, Sudan watermelon mosaic virus, Sugar beetcyst nematode virus 1, Sugarcane bacilliform Guadeloupe A virus, Sugarcane bacilliform Guadeloupe D virus, Sugarcane chlorotic streak virus, Sugarcane mosaic virus, Sugarcane streak mosaic virus, Sugarcane streak Reunion virus, Sugarcane streak virus, Sugarcane striate virus, Sugarcane white streak virus, Sugarcane yellow leaf virus, Sunflower chlorotic mottle virus, Sunflower leaf curl Karnataka alphasatellite, Sunflower mild mosaic virus, Sunflower ring blotch virus, Sunn hemp leaf distortion virus, Sweet potato badnavirus B, Sweet potato C6 virus, Sweet potato chlorotic fleck virus, Sweet potato chlorotic stunt virus, Sweet potato collusive virus, Sweet potato feathery mottle virus, Sweet potato golden vein associated virus, Sweet potato golden vein Korea virus, Sweet potato latent virus, Sweet potato leaf curl Bengal virus, Sweet potato leaf curl Canary virus, Sweet potato leaf curl China virus, Sweet potato leaf curl deltasatellite 1, Sweet potato leaf curl deltasatellite 2, Sweet potato leaf curl Georgia virus, Sweet potato leaf curl Guangxi virus, Sweet potato leaf curl Henan4 virus, Sweet potato leaf curl Henan virus, Sweet potato leaf curl Hubei virus, Sweet potato leaf curl Korean virus, Sweet potato leaf curl Lanzarote virus, Sweet potato leaf curl Sao Paulo virus, Sweet potato leaf curl Shanghai virus, Sweet potato leaf curl Sichuan virus 1, Sweet potato leaf curl Sichuan virus 2, Sweet potato leaf curl South Carolina virus, Sweet potato leaf curl Spain virus, Sweet potato leaf curl Uganda virus, Sweet potato leaf curl virus, Sweet potato mild mottle virus, Sweet potato mosaic virus, Sweet potato pakakuy virus, Sweet potato symptomless virus 1, Sweet potato vein clearing virus, Sweet potato virus 2, Sweet potato virus C, Sweet potato virus G, Sweet potato yellow mottle virus, Switchgrass mosaic, Switchgrass mosaic virus, Synedrella leaf curl alphasatellite, Synedrella leaf curl virus, Synedrella yellow vein clearing virus, Tall oatgrass mosaic virus, Tamarillo leaf malformation virus, Tamus red mosaic virus, Taro bacilliform CH virus, Taro bacilliform virus, Tea plant line pattern virus, Tea plant necrotic ring blotch virus, Telfairia golden mosaic virus, Telosma mosaic virus, Temperate fruit decay, Tetterwort vein chlorosis virus, Thin paspalum asymptomatic virus, Thunberg fritillary mosaic virus, Ti ringspot, Tobacco bushy top virus, Tobacco bushy top virus isolate Baoshan satellite, Tobacco bushy top virus satellite, Tobacco curly shoot alphasatellite, Tobacco curly shoot betasatellite, Tobacco curly shoot virus, Tobacco etch virus, Tobacco leaf chlorosis betasatellite, Tobacco leaf curl betasatellite, Tobacco leaf curl Comoros virus, Tobacco leaf curl Cuba virus, Tobacco leaf curl disease associated sequence, Tobacco leaf curl Dominican Republic virus, Tobacco leaf curl Japan betasatellite, Tobacco leaf curl Japan virus, Tobacco leaf curl Kochi virus, Tobacco leaf curl Patna betasatellite, Tobacco leaf curl PUSA alphasatellite, Tobacco leaf curl Pusa virus, Tobacco leaf curl Thailand virus, Tobacco leaf curl virus, Tobacco leaf curl Yunnan betasatellite, Tobacco leaf curl Yunnan virus,Tobacco leaf curl Zimbabwe virus, Tobacco leaf rugose virus, Tobacco mild green mosaic virus, Tobacco mosaic virus, Tobacco mosaic virus strain Ohio V, Tobacco mosqueado virus, Tobacco necrosis virus A, Tobacco necrosis virus D, Tobacco rattle virus, Tobacco ringspot virus, Tobacco streak virus, Tobacco vein banding mosaic virus, Tobacco vein clearing virus, Tobacco vein distorting virus, Tobacco vein mottling virus, Tobacco virus 1, Tobacco virus 2, Tobacco yellow crinkle virus, Tobacco yellow dwarf virus, Tomato apical leaf curl virus, Tomato apical stunt viroid, Tomato apical stunt viroid Sjl, Tomato aspermy virus, Tomato associated geminivirus 1, Tomato begomovirus satellite DNA beta, Tomato black ring virus, Tomato blistering mosaic virus, Tomato bright yellow mosaic virus, Tomato bright yellow mottle virus, Tomato brown rugose fruit virus, Tomato bushy stunt virus, Tomato chino La Paz virus, Tomato chlorosis virus, Tomato chlorotic dwarf viroid, Tomato chlorotic leaf curl virus, Tomato chlorotic leaf distortion virus, Tomato chlorotic mottle Guyane virus, Tomato chlorotic mottle virus, Tomato chlorotic spot tospovirus, Tomato chlorotic spot virus, Tomato chocolate spot virus, Tomato common mosaic virus, Tomato curly stunt virus, Tomato dwarf leaf virus, Tomato enation leaf curl virus, Tomato golden leaf distortion virus, Tomato golden leaf spot virus, Tomato golden mosaic virus, Tomato golden mottle virus, Tomato golden vein virus, Tomato infectious chlorosis virus, Tomato interveinal chlorosis virus, Tomato latent virus, Tomato leaf curl alphasatellite, Tomato leaf curl Anand alphasatellite, Tomato leaf curl Anjouan virus, Tomato leaf curl Arusha virus, Tomato leaf curl Bangalore virus, Tomato leaf curl Bangladesh betasatellite, Tomato leaf curl Bangladesh virus, Tomato leaf curl Barka virus, Tomato leaf curl betasatellite, Tomato leaf curl Burkina Faso virus, Tomato leaf curl Cameroon alphasatellite, Tomato leaf curl Cameroon virus, Tomato leaf curl Cebu virus, Tomato leaf curl China betasatellite, Tomato leaf curl China virus, Tomato leaf curl Comoros virus, Tomato leaf curl Cotabato virus, Tomato leaf curl deltasatellite, Tomato leaf curl Diana virus, Tomato leaf curl Gandhinagar betasatellite, Tomato leaf curl Gandhinagar virus, Tomato leaf curl Ghana virus, Tomato leaf curl Guangdong virus, Tomato leaf curl Guangxi virus, Tomato leaf curl Gujarat virus, Tomato leaf curl Hainan virus, Tomato leaf curl Hainan virus HaNHK7, Tomato leaf curl Hajipur betasatellite, Tomato leaf curl Hanoi virus, Tomato leaf curl Java betasatellite, Tomato leaf curl Java virus, Tomato leaf curl Joydebpur beta, Tomato leaf curl Joydebpur betasatellite, Tomato leaf curl Joydebpur virus, Tomato leaf curl Karnataka betasatellite, Tomato leaf curl Karnataka virus, Tomato leaf curl Karnataka virus 2, Tomato leaf curl Karnataka virus 3, Tomato leaf curl Kerala virus, Tomato leaf curl Kumasi virus, Tomato leaf curl Laguna betasatellite, Tomato leaf curl Laos betasatellite, Tomato leaf curl Laos virus, Tomato leaf curl Liwa virus, Tomato leaf curl Madagascar virus, Tomato leafcurl Mahe virus, Tomato leaf curl Malaysia betasatellite, Tomato leaf curl Mali virus, Tomato leaf curl Mayotte virus, Tomato leaf curl Mindanao virus, Tomato leaf curl Moheli virus, Tomato leaf curl Namakely virus, Tomato leaf curl Nepal betasatellite, Tomato leaf curl New Delhi betasatellite, Tomato leaf curl New delhi virus, Tomato leaf curl New Delhi virus, Tomato leaf curl New Delhi virus 2, Tomato leaf curl New Delhi virus 4, Tomato leaf curl New Delhi virus 5, Tomato leaf curl Nigeria virus, Tomato leaf curl Oman virus, Tomato leaf curl Pakistan alphasatellite, Tomato leaf curl Pakistan betasatellite, Tomato leaf curl Palampur virus, Tomato leaf curl Patna betasatellite, Tomato leaf curl Patna virus, Tomato leaf curl Philippine betasatellite, Tomato leaf curl Philippines virus, Tomato leaf curl Pune virus, Tomato leaf curl purple vein virus, Tomato leaf curl Ranchi betasatellite, Tomato leaf curl Ranchi virus, Tomato leaf curl Seychelles virus, Tomato leaf curl Sinaloa virus, Tomato leaf curl Sri Lanka virus, Tomato leaf curl Sudan virus, Tomato leaf curl Sulawesi virus, Tomato leaf curl Taiwan virus, Tomato leaf curl Taiwan virus ZJWZ5, Tomato leaf curl Togo betasatellite, Tomato leaf curl Toliara virus, Tomato leaf curl Uganda virus, Tomato leaf curl Vietnam virus, Tomato leaf curl Vietnam virus satellite DNA beta DX2, Tomato leaf curl Vietnam virus satellite DNA beta VR2, Tomato leaf curl Vietnam virus satellite DNA beta VRQ, Tomato leaf curl Virudhunagar alphasatellite, Tomato leaf curl virus, Tomato leaf curl Yemen betasatellite, Tomato leaf curl Yunnan betasatellite, Tomato leaf deformation virus, Tomato leaf distortion virus, Tomato marchitez virus, Tomato mild mosaic virus, Tomato mild mottle virus, Tomato mild yellow leaf curl Aragua virus, Tomato mosaic Havana virus, Tomato mosaic leaf curl virus, Tomato mosaic severe dwarf virus, Tomato mosaic Trujillo virus, Tomato mosaic virus, Tomato mottle leaf curl virus, Tomato mottle leaf curl Zulia virus, Tomato mottle mosaic virus, Tomato mottle Taino virus, Tomato mottle virus, Tomato mottle wrinkle virus, Tomato necrotic dwarf virus, Tomato necrotic streak virus, Tomato necrotic stunt virus, Tomato pseudo, Tomato ringspot virus, Tomato rugose mosaic virus, Tomato rugose yellow leaf curl virus, Tomato severe leaf curl Kalakada virus, Tomato severe leaf curl virus, Tomato severe rugose virus, Tomato spotted wilt tospovirus, Tomato torrado virus, Tomato twisted leaf virus, Tomato vein clearing leaf deformation virus, Tomato yellow leaf curl Al, Tomato yellow leaf curl Axarquia virus, Tomato yellow leaf curl betasatellite, Tomato yellow leaf curl China alphasatellite, Tomato yellow leaf curl China betasatellite, Tomato yellow leaf curl China virus, Tomato yellow leaf curl China virus satellite DNA beta, Tomato yellow leaf curl Guangdong virus, Tomato yellow leaf curl Indonesia virus, Tomato yellow leaf curl Kanchanaburi virus, Tomato yellow leaf curl Malaga virus, Tomato yellow leaf curl Mali virus, Tomato yellow leaf curl Oman virus, Tomato yellow leaf curl Sardinia virus, Tomato22yellow leaf curl Saudi virus, Tomato yellow leaf curl Shandong betasatellite, Tomato yellow leaf curl Shuangbai virus, Tomato yellow leaf curl Thailand betasatellite, Tomato yellow leaf curl Thailand virus, Tomato yellow leaf curl Vietnam betasatellite, Tomato yellow leaf curl Vietnam virus, Tomato yellow leaf curl virus, Tomato yellow leaf curl virus JSNJ1, Tomato yellow leaf curl virus SDJN2, Tomato yellow leaf curl virus SH3, Tomato yellow leaf curl virus ZJHZ1, Tomato yellow leaf curl virus ZJHZ12, Tomato yellow leaf curl Yunnan alphasatellite, Tomato yellow leaf curl Yunnan betasatellite, Tomato yellow leaf curl Yunnan virus, Tomato yellow leaf deformation dwarf virus, Tomato yellow leaf distortion deltasatellite 2, Tomato yellow leaf distortion virus, Tomato yellow margin leaf curl virus, Tomato yellow mottle, Tomato yellow mottle virus, Tomato yellow ring virus, Tomato yellow spot alphasatellite, Tomato yellow spot alphasatellite 2, Tomato yellow spot virus, Tomato yellow vein streak virus, Tomato zonate spot virus, Tospovirus kiwifruit / YXW / 2014, Tradescantia mild mosaic virus, Trailing lespedeza virus 1, Trifolium, Triticum mosaic virus, Triumfetta yellow mosaic virus, Tropical soda apple mosaic virus, Tuberose mild mosaic virus, Tuberose mild mottle virus, Tulip breaking virus, Tulip mosaic virus, Tulip virus X, Turnip crinkle virus, Turnip curly top virus, Turnip leaf roll virus, Turnip mosaic virus, Turnip mosaic virus (isolate UK1), Turnip ringspot virus, Turnip rosette virus, Turnip vein, Turnip yellow mosaic virus, Turnip yellows virus, Turtle grass virus X, Twisted, TYLCAxV, TYLCCNV, Ugandan cassava brown streak virus, unidentified, Urochloa hoja blanca tenuivirus, Urochloa streak virus, Vallota mosaic virus, Vanilla distortion mosaic virus, Vanilla latent virus, Vanilla virus X, Velvet bean golden mosaic virus, Velvet bean severe mosaic virus, Velvet tobacco mottle virus, Velvet tobacco mottle virus Satellite RNA, Verbena latent virus, Verbena mottle virus, Verbena virus Y, Verbesina encelioides leaf curl alphasatellite, Vernonia crinkle betasatellite, Vernonia crinkle virus, Vernonia yellow vein betasatellite, Vernonia yellow vein Fujian alphasatellite, Vernonia yellow vein Fujian betasatellite, Vernonia yellow vein Fujian virus, Vernonia yellow vein virus, Vicia cryptic virus, Vicia cryptic virus M, Viciafaba alphaendornavirus, Vigna yellow mosaic virus, Vinca leaf curl virus, Wasabi mottle virus, Water chestnut soymovirus 1, Watermelon bud necrosis virus, Watermelon chlorotic stunt virus, Watermelon mosaic virus, Watermelon silver mottle tospovirus, Watermelon virus A, West African Asystasia virus 1, West African Asy stasia virus 2, Wheat dwarf India virus, Wheat dwarf virus, Wheat dwarf virus Bgl 7, Wheat dwarf virus H07, Wheat leaf yellowing, Wheat spindle streak mosaic virus, Wheat streak mosaic virus, Wheat stripe mosaic virus, Wheat yellow dwarf virus, Wheat yellow mosaic virus, Wheat yellow striate virus, White clover cryptic virus 1, White clover cryptic virus 2, White clover mosaic virus, White clover mottle virus, Whitefly, Whitefly associated Guatemalaalphasatellite 1, Whitefly associated Guatemala alphasatellite 2, Whitefly associated Puerto Rico alphasatellite 1, White lupin mosaic virus, Wild melon vein banding virus, Wild onion symptomless virus, Wild potato mosaic virus, Wild tomato mosaic virus, Wild vitis virus 1, Winged bean alphaendornavirus 1, Wissadula golden mosaic St Thomas Virus, Wissadula golden mosaic virus, Wissadula yellow mosaic virus, Wisteria badnavirus 1, Wisteria vein mosaic virus, Wound tumor virus, Wuhan aphid virus 2, Wuhan insect virus 21, Xanthium symptomless alphasatellite, Yacon necrotic mottle virus, Yacon virus A, Yambean mosaic virus, Yam chlorotic necrosis virus, Yam chlorotic necrotic mosaic virus, Yam latent virus, Yam mild mosaic virus, Yam mosaic virus, Yam spherical virus, Yam virus X, Yellow oat grass mosaic virus, Yellow tailflower mild mottle virus, Yerba mate, Yerba mate alphaendornavirus, Yerba mate chlorosis, Youcai mosaic virus, Zantedeschia mild mosaic virus, Zinnia leaf curl virus, Zostera marina amalgavirus 1, Zostera marina amalgavirus 2, Zucchini green mottle mosaic virus, Zucchini lethal chlorosis virus, Zucchini shoestring virus, Zucchini tigre mosaic virus, Zucchini yellow fleck virus, Zucchini yellow mosaic virus, Zygocactus virus X, or any other emerging virus.

[0051] Some embodiments of the methodology disclosed herein can be implemented, in whole or in part, through a machine that is configured to receive a computer-readable medium containing the necessary instructions. These instructions can be executed by computerexecutable components integrated within various platforms such as applications, applets, hosts, servers, networks, websites, communication services, communication interfaces, and the hardware, firmware, or software elements of personal computers or mobile devices. Additionally, other embodiments encompass systems and methods that can be embodied, at least partially, within a machine that is configured to receive a computer-readable medium storing the required instructions. These instructions can be executed by computer-executable components integrated with apparatuses and networks similar to those described earlier. The computer-readable medium can be stored on a range of suitable storage devices, including RAMs, ROMs, flash memory, EEPROMs, optical devices (such as CD, DVD, Blu-Ray), hard drives, floppy drives, cloud storage, or any other appropriate device. While the computerexecutable component is typically a processor, it is also possible for the instructions to be executed by any dedicated hardware device that is suitable for the task.4. Examples4.1 Identification of viral agents in publicly available NGS / HTS datasets.

[0052] Various external datasets containing NGS / HTS data were examined (Table 1). A total of 32 datasets were tested, including: 10 datasets from a challenge to identify viruses in NGS / HTS data under different conditions (Tamisier et al., 2021. Semi- artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection. Peer community Journal. 1(1), pp.1-15; semi-artificial paired-end (PE) or single-end (SE) short reads); 1 dataset from an analysis using a plant transcriptome to identify viruses (Jo et al., 2016. Additional file 2: of Integrated analyses using RNA-Seq data reveal viral genomes, single nucleotide variations, the phylogenetic relationship, and recombination for Apple stem grooving virus. doi:10.6084 / m9. figshare. c.3617363_D2.vl; RNAseq, SE reads); 1 dataset from a study of the pepper virome (Jo et al., 2017. The pepper virome: natural co-infection of diverse viruses and their quasispecies. BMC genomics. 18(1), p.453; RNAseq, PE reads); 1 dataset from a study of small RNAs produced by Dicer-like enzymes as a defense strategy in plants infected by a virus (Barrero et al., 2017. An internet-based bioinformatics toolkit for plant biosecurity diagnosis and surveillance of viruses and viroids. BMC Bioinform. 18(1), p.26; sRNAseq, SE reads); 14 datasets from a report describing the use of Oxford Nanopore MinlON for detecting and genotyping potato viruses (Della-Bartola et al., 2020. Characterization of Potato Virus Y Isolates and Assessment of Nanopore Sequencing to Detect and Genotype Potato Viruses. Viruses. 12(4), p.478; RNAseq, ONT reads); 1 dataset used for reporting the genome sequence of a virus using Oxford Nanopore (Leiva et al., 2020. Nanopore-Based Complete Genome Sequence of a Sri Lankan Cassava Mosaic Virus (Geminivirus) Strain from Thailand. Microbiol. Resour. Announc. 9(6), e01274-19; DNA, ONT reads); and 1 dataset from a study demonstrating the use of MinlON to detect and characterize viruses infecting water yam plants (Filloux et al., 2018. Nanopore-based detection and characterization of yam viruses. Sci. Rep. 8, p.17879; RNAseq, ONT). All datasets combined included a total of 46 different viruses to be detected and 62 cases (a case is defined as a "virus in a dataset"), which were divided into three groups (some viruses are included in more than one dataset): Viromock, datasets V1-V10 (18 viruses; 15 cases); SmallRNA, datasets R1-R3 (21 viruses and 1 viroid; 22 cases); and Nanopore, datasets N1-N19 (10 viruses; 25 cases).

[0053] For this example, viral panels and their respective reference databases were constructed for each group based on the viruses to be detected in each group (Table 5). Therefore, the Viromock panel was created (18 viruses: BPEV, BSV, BYDV, CMV, CTV, CVEV, EMDV, GRBaV, GRLaV2, GRSPaV, GRVFV, LChV-1, PBNSPaV, PepMV, PFBV, PiVB, PVY, and TSWV), the SmallRNA panel (21 viruses: AGCaV, ALPV, ASGV, ASPV, BPEV, CLCuV,CYVMV, GalLV, GFkV, GLRaV-3, GRSPaV, GVB, PepLCBV, PepLCVB, PeSV, PrVT, ToLCBDB, ToLCGV, ToLCJoV, ToLCRnV, and TVCV), and the Nanopore panel (10 viruses: CMV, DBV, EACMV, PLRV, PVS, PVX, PVY, SLCMV, YCNMV, and YMMV). In the case of the Viromock dataset group, the viruses present in datasets V11-V18 were included in the panel, but the datasets were not examined as they were generated for the identification of viral isolates / variants and did not contain contaminant sequences.

[0054] To evaluate the performance of the proposed methodology in this invention, several parameters were calculated: sensitivity, specificity, precision, accuracy, and false discovery rate (FDR). These measures were determined as follows: Sensitivity = TP / TP+FN, Specificity = TN / TN+FP, Precision = TP / TP+FP, Accuracy = TP+TN / TP+TN+FP+FN, and FDR = FP / TP+FP, where TP (true positives) is the number of correctly detected cases, FP (false positives) is the number of incorrectly detected cases, FN (false negatives) is the number of cases that were not detected but should have been, and TN (true negatives) is the number of cases that were correctly not detected because they were not present. Since only the viruses that should be detected were reported alongside the datasets, the values of FP and TN were estimated based on the remaining viruses in the respective panel. For example, in the Viromock dataset group, there were 15 true positive cases to be detected, but the read assignment identified an additional 51 cases, so it was assumed that there could be a maximum of 51 TN cases in these datasets. The aforementioned measures were determined for read assignment (TP is considered when there is at least one assigned read) and for replicase identification (TP is considered when there is a match with the 80 / 80 or 90 / 90 scheme). In the case of completeness, the measures were calculated for the thresholds 0.1 (TP is considered when the completeness value > 0.1), 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0. Finally, to estimate the similarity between the measures for completeness and replicase identification, the Euclidean distance d between the completeness measures and the 80 / 80 scheme was determined as:I 2 d(m) = Zs(.mcs ~m80 / 80s ) ■> where mccan be sensitivity, specificity, precision, accuracy, or FDR for the completeness value, mso / so is the corresponding measure for replicase identification under the 80 / 80 scheme, and 5 considers the three algorithms (Centrifuge, Kraken2, and Minimap2).

[0055] According to the performance on the Viromock datasets (Table 6), the methodology described in this invention achieved the detection of viral reads, assembled partial or complete viral genomes, and detected replicases in 14 out of 15 cases under the criterion of detectionwith at least 2 out of the 3 algorithms (93% in both 80 / 80 and 90 / 90 schemes). Case V3- GRLaV2 did not meet the criterion as replicases were only detected in the assembly from reads assigned by one of the algorithms (Centrifuge, in both schemes) due to lower completeness values. In the same dataset, cases V3-GRSPaV and V3-GRVFV failed in detection with reads assigned by Minimap2. In these three cases, the completeness values were below 0.2, but replicases were detected above this threshold.

[0056] Regarding the SmallRNA dataset group (Table 7), 4 cases successfully completed all the steps of the methodology (read assignment, assembly, and replicase detection) with at least 2 out of the 3 algorithms (cases Rl-PepLCBV, Rl-BPEV, Rl-GalLV, and Rl-TVCV). The BPEV case showed a low number of reads but sufficient to assemble a region to identify a replicase (Centrifuge and Kraken2). The GalLV case can be considered an artifact since the actual replicase is encoded in the first 6,000 nucleotides of the 5' region of the reference genome (a segment of 8,659 nucleotides in length), but a shorter replicase was found at the end of the 3' region (detected at very low completeness values). In the TVCV case, low completeness values were obtained; however, a contig could be assembled around the position 4,300 of the reference genome (which has a length of 7,767 nucleotides) containing the replicase. In the CLCuV case, there were a large number of assigned reads; however, only a contig overlapping with the central region of the reference genome could be assembled, and the replicase in this virus is located in the 3' region (which is why replicases were not detected). A similar situation was observed in the CYVMV case, where no contigs could be assembled for the 3' region of the reference genome where the replicase is located. In the cases of PepLCVB and ToLCDBB, despite relatively high completeness values, replicases were not found. This was expected since it has been reported that betasatellite viruses rely entirely on other viruses for their replication, movement, and transmission (Shafiq et al., 2020. DNA Plant Viruses: Biochemistry, Replication, and Molecular Genetics. Applied Plant Virology: Advances, Detection, and Antiviral Strategies. Academic Press, pp.169-182). In the case of ToLCJoV, the methodology could only be completed under the 80 / 80 scheme, but it can be considered erroneous since the actual replicase of the virus is in the 3' region, which could not be assembled (the assembled contigs are assigned to the 5' half of the reference genome, which contains a replicase encoded in the 3' to 5' direction). In the case of ToLCRnV, the methodology could detect replicases only under the 80 / 80 scheme for Centrifuge and Minimap2. This is because the consensus scaffold sequence for the reads assigned by Centrifuge or Minimap2 was longer than that for the reads assigned by Kraken2, thus encoding a replicase.Table 5. External datasets and viruses to be detected.Table 5A: Viromock Group (Tamisier et al. 2021)Table 5B: SmallRNA Group (Jo et al. 2017, Jo et al. 2016, and Barrero et al. 2017)Table 5C: Nanopore Group (Della-Bartola et al. 2020, Leiva et al. 2020, Boykin et al.2018, and Filloux et al. 2018)All the 32 datasets sum up 41 different viruses to be detected.Length is reported as the total length in the case of viruses with multiple segments.In the case of viruses with multiple segments, proteins related to replication (e.g. replicase) are found in the first sequence of the accession list. Seg.; Segments. RL / A: read length / average; Ref.: Reference; Conc.:Concentration;Table 6. Performance of pipeline on Viromock DatasetsViruses that represent true positives to be detected in the respective dataset are highlighted with bold font. VGAC: Virus genome assembly coverage; C: Centrifuge; K: Kraken2; M: Minimap2; Abbrev.: Abbreviation.Table 7. Performance of pipeline on SmallRNA Datasets.Viruses that represent true positives to be detected in the respective dataset are highlighted with bold font VGAC: Virus genome assembly coverage; C: Centrifuge; K: Kraken2; M: Minimap2; Abbrev.: Abbreviation.

[0057] No detections could be made in the R2 or R3 datasets despite the reads being assigned with all algorithms in the first dataset and only with Centrifuge in the second. All these cases were manually inspected to verify if the assembly was not accomplished, e.g., due to read length or because the reads were located in a region of the reference genome that did not contain at least 80% of a replicase (e.g., the case R2-ASGV for Minimap2 represented in FIGURES 5A-5B).

[0058] According to the performance of the Nanopore dataset group (Table 8), the methodology was able to assign viral reads, assemble partial or complete viral genomes, and identify replicases in 24 out of 25 cases (96%) using the criterion of 2 out of 3 algorithms and both schemes (80 / 80 and 90 / 90). In this group, there was one case with no read assignment (case N19-DB V). All cases were also manually inspected, and it was found that some replicases were detected in assemblies with lower completeness values. For example, in cases N3-PVY (completeness ~ 0.5-0.7), Nl l-PVY (completeness ~ 0.5), and N12-PLRV (completeness ~ 0.3), the 3' regions of the viruses, which encode their respective replicases, could be assembled. In the case of N15-SLCMV (a two-segment virus), the reads assigned by Kraken2 could assemble one of the virus segments (hence, completeness ~ 0.5), which happened to be the one encoding the replicase. In the case of N16-CMV (a two-segment virus), the reads assigned byMinimap2 could assemble both virus segments (hence, completeness > 0.9), and the reads assigned by Centrifuge and Kraken2 could assemble one segment (completeness ~ 0.5). However, all three assemblies encoded the replicase (in this case, the number of reads was less than 60, but with an average length of -3,200 nucleotides; Table 5). Finally, in the case of N17- EACMV (a two-segment virus), despite the number of reads assigned by Minimap2, only one segment (the one lacking the replicase) could be assembled (hence, completeness ~ 0.5). The N19 dataset was further analyzed to confirm the lack of reads assigned to DBV. Although it has been reported to have 156 ONT reads (Filloux et al., 2018. Nanopore-based detection and characterization of yam viruses. Sci. Rep. 8, p.17879), it was not possible to assign them with any of the three algorithms, nor when using the Diamond and Blast tools.

[0059] The overall performance of the external datasets is presented in Table 9. All three algorithms demonstrated high sensitivity at the read assignment level (average > 0.9), but specificity varied among them, with Minimap2 achieving the highest degree (average = 0.82 compared to 0.26-0.36 achieved by the other two algorithms). Similar patterns were observed, where Minimap2 outperformed Centrifuge and Kraken2 in terms of precision (0.81 vs. 0.55- 0.56) and accuracy (0.85 vs. 0.61-0.60). This was consistent with the false discovery rate (FDR), where the pattern was reversed, with Minimap2 showing the lowest rate (0.19 vs. 0.45- 0.44). Regarding the completeness metrics, sensitivity and precision values tended to decrease as the thresholds became stricter, while specificity, precision, and FDR tended to improve, reaching maximum (1) and minimum (0) values, respectively. In terms of replicase detection, both schemes (80 / 80 and 90 / 90) showed identical measurement values in the Viromock and Nanopore dataset groups, while the use of either scheme had a greater impact on the SmallRNA dataset group (e.g., the average sensitivity in the 80 / 80 scheme was 0.21, while in the 90 / 90 scheme, it was 0.14). Overall, these measurements provide further justification for the high performance of the Viromock (Illumina datasets) and Nanopore dataset groups (Table 9), in which the disclosed methodology was able to fully identify over 93% of the tested cases. However, the 80 / 80 scheme was the most suitable for replicase detection, as it allowed for higher performance even with strict parameters (80% similarity and 80% alignment length). In this regard, a comparison of replicase identification measures under this scheme of using completeness values at different thresholds was performed to find the cutoff point at which similar performance measures are obtained. By this measurement, it was possible to indicate the minimum completeness value at which replicases can be identified. To identify this completeness cutoff, the Euclidean distance between the measures (sensitivity, specificity,accuracy, precision, and FDR) was calculated. A minimum distance was found at completeness > 0.2 for the Viromock dataset groups, completeness > 0.4 for the SmallRNA dataset groups, completeness > 0.1 for the Nanopore dataset groups, and completeness > 0.3 considering all dataset groups (FIGURE 6).Table 8. Performance of pipeline on Nanopore Datasets.Viruses that represent true positives to be detected in the respective dataset are highlighted with bold font. VGAC: Virus genome assembly coverage; C: Centrifuge; K: Kraken2; M: Minimap2; Abbrev.: Abbreviation.

[0060] In conclusion, the validation with these external datasets shows that the methodology disclosed herein has high sensitivity and diagnostic specificity for total RNA sequencing data. Conversely, small RNA data showed poor performance in viral detection at the completeness level. Additionally, Nanopore sequencing data was used to assess the applicability of our methodology to long reads, resulting in a 96% concordance compared to previous publications (Boykin et al., 2018. Real time portable genome sequencing for global food security. bioRxiv, Della-Bartola et al., 2020. Characterization of Potato Virus Y Isolates and Assessment of Nanopore Sequencing to Detect and Genotype Potato Viruses. Viruses. 12(4), p.478; Filloux et al., 2018. Nanopore-based detection and characterization of yam viruses. Sci. Rep. 8, p.17879.; and Leiva et al., 2020. Nanopore-Based Complete Genome Sequence of a Sri Lankan Cassava Mosaic Virus (Geminivirus) Strain from Thailand. Microbiol. Resour. Announc. 9(6),eO 1274- 19). Long-read sequencing platforms have an advantage over short-read sequencing in terms of completeness value since, despite lower basecalling quality, longer reads enable the complete reconstruction of a viral genome with fewer reads (e.g., CMV in the N16 sample). The overall performance aligns with the type of dataset analyzed, meaning that the use of small RNA data appears to have a significant impact on sensitivity and precision when it comes to completeness calculation or replicase identification. This also reflects the fact that a higher completeness threshold was needed to obtain replicase identification in the small RNA dataset (> 0.4, FIGURE 6), contrasting with total RNA reads from Nanopore or Illumina (as low as > 0.1, FIGURE 6), which report lower cutoff values. Certainly, completeness values are affected by the methodology's ability to assemble viruses (with correct read assignment), hence the higher thresholds in that type of data. In any case, the use of the completeness value allows for achieving maximum specificity and minimum FDR even with a completeness > 0.1, which eliminates false positives and false negatives from the minimum cutoff of the tested completeness value. This is particularly evident in the performance for the Viromock and Nanopore dataset groups. Considering only the total RNA sequencing data (Viromock and Nanopore datasets), a completeness cutoff value > 0.1 results in an average sensitivity of 96%, specificity of 99%, and FDR of 1%, demonstrating that the methodology proposed in this invention provides reliable detection.Table 9A. Performance on external datasets - individual datasetsC: Centrifuge; K: Kraken2; M: Minimap2; Ave: Average value; Sen: Sensibility; Spc: Specificity; Pre: Precision; Acc: Accuracy; Com: Completeness; Dist.: distance between completeness and 80 / 80 schema; Para.: Parameter; Read Assi.: Read Assignment; G.: Group.Table 9B. Performance on external datasets - Average and Average Viromock-NanoporeC: Centrifuge; K: Kraken2; M: Minimap2; Ave: Average value; Sen: Sensibility; Spc: Specificity; Pre: Precision; Acc: Accuracy; Com: Completeness; Dist.: distance between completeness and 80 / 80 schema; Para.: Parameter; Read Assi.: Read Assignment; G.: Group.4.2 Identification of viral agents in field samples

[0061] Sweet cherries (Prunus avium) are one of the main stone fruit cultivars grown in central- southern Chile. For this example, a sweet cherry orchard located in the O'Higgins region of Chile was selected. Plant material was randomly collected from four different specimens of Prunus avium that were over 5 years old. In this orchard, the elite varieties Lapins and Santina were the most common cherry varieties. Therefore, each sample was labeled as LI, L2, SI, and S2, respectively. To assess differences in viral diagnosis of the plants due to seasonal conditions (factors such as changes in temperature, light, and / or plant nutrition), samples were taken from the same plants during two different major growth stages, as reported for Prunus sp.: Stage 3 or bud development (SD) and Stage 9 or senescence (SS, onset of dormancy) (Fadon et al., 2015. Flower development in sweet cherry framed in the BBCH scale. Sci. Hortic., 192, pp.141-147.). Each growth stage corresponds to spring and late summer, respectively. A total of 8 leaves (4 apical and 4 mid-aged leaves of the same size) were sampled from the canopy of an individual tree and placed in RNAlater solution (Invitrogen). Subsequently, the samples were transported to the laboratory in refrigerated containers and stored at -80°C in RNAlater solution until further use.

[0062] For the extraction of genetic material (total DNA and RNA), 4 randomly selected preserved leaves were grouped together and ground in a liquid nitrogen-cooled ceramic mortar. 100 mg of the ground sample were processed using i) a lysis buffer solution, ii) a nucleic acid binding matrix, and iii) an elution buffer solution (following the instructions provided by the manufacturer of the SpectrumTM Plant Total RNA kit (Sigma-Aldrich, STRN50). The concentration and quality of the total RNA were evaluated using a microplate reader (BioTek Synergy Hl, Santa Clara, California, USA), fluorometry (Promega Quantus fluorometer, Madison, Wisconsin, USA), and the integrity was verified through agarose gel electrophoresis (Rio, 2015. Denaturation and electrophoresis of RNA with formaldehyde. Cold Spring Harbor Protocols, pp.219-22.). Purified extracts of the total genetic material (DNA and RNA) were stored in an elution buffer solution at -80°C until further processing.

[0063] For RNA sequencing, 2 pg of total RNA from each field sample (SD-L1, SD-L2, SD- Sl, and SD-S2) were sent to Novogene Corporation Inc. (USA), and samples (SS-L1, SS-L2, SS-S1, and SS-S2) were sent to Macrogen Co., Ltd. (Korea). Ribosomal RNA removal was performed using Ribo-Zero Plant (Illumina, USA), and library construction was carried out using the NEBNext Ultra RNA Library Prep kit (New England Biolabs, USA). The libraries were then sequenced on a NovaSeq6000 using a 150-nucleotide paired-end read cycle.

[0064] The presence of viral pathogens in each individual sample collected during the shoot development stage (SD-L1, SD-L2, SD-S1, and SD-S2) and the senescence stage (SS-L1, SS- L2, SS-S1, and SS-S2) was confirmed by RT-PCR. A two-step RT-PCR for SD samples was performed by the Virology Laboratory, University of Chile (N. Fiore, personal communication, October 21, 2020), referred to as "external PCR" here. On the other hand, for samples obtained during the senescence stage (SS-L1, SS-L2, SS-S 1, and SS-S2), the presence of viral pathogens was determined through a two-step RT-PCR conducted by the inventors. This method was optimized to detect the 11 viruses in Panel-I, and the results were recorded as "internal PCR". Primers for this panel were exclusively designed for this invention using a local viral sequence database and the OligoPerfect™ design software (ThermoFisher Scientific) (Table 10). Phytoene desaturase 1 (PDS1), a plant gene that shows constitutive expression, was used as an internal control for RNA extraction and reverse transcription in the internal PCR. Additionally, eleven purified amplicons were used as positive controls to confirm the amplification of sequences of appropriate size (FIGURE 7E).

[0065] cDNA synthesis was performed using 70 ng of total RNA. The reverse transcription (RT) mix contained 200 units of Moloney murine leukemia virus reverse transcriptase (MMLV) (Promega), 20 units of RNasin (Promega), 1 mM dNTPs, and 1 pM random hexamers (Promega). The reaction was carried out in a final volume of 20 pL and incubated at 37 °C for 60 min, followed by enzyme inactivation at 70 °C for 5 min. The PCR mix (final volume of 25 pL) contained 1 pL of cDNA, IX (2.5 pL) GoTaq G2 Flexi Buffer (Promega), 0.15 pM of each primer (Table 10), 3 mM MgC12, 0.2 mM dNTPs, and 1.25 units of GoTaq G2 Flexi DNA polymerase (Promega). The cycling conditions for all primer pairs consisted of an initial denaturation at 95 °C for 2 min, followed by 35 cycles at 95 °C for 15 s, 60 °C for 30 s, 72 °C for 1 min, and a final extension at 72 °C for 5 min. PCR products were analyzed by agarose gel electrophoresis using 3% agarose in IX TBE buffer and stained with SybrSafe 1:10,000 v / v (Invitrogen Life Technologies).

[0066] To evaluate viral abundance in the field samples, read assignment was performed using three algorithms: Centrifuge, Kraken2, and Minimap2. The samples from the four cherry tree specimens (SD-L1, SD-L2, SD-S1, and SD-S2) collected during the shoot development stage were sequenced, producing 17M-22M paired reads each (Table 1), and a read subsampling was performed to assess the relationship between read assignment and depth of sequencing (subsets of 100K, 500K, IM, 5M, 10M, and 15M total reads). Read assignment was carried out using a reference database constructed from Panel-I, which determines the presence of the virusesACLSV, ApMV, CGRMV, CNRMV, CVA, LChV-1, LChV-2, PBNSPaV, PDV, PNRSV, and PPV.Table 10. List of viruses of Pavium panel-I and primers to perform RT-PCR internal.PDS1: plant constitutive gene as positive control. Amplicon size is in basepairs (bp).

[0067] According to the read assignment process, the number of mapped reads increased linearly with sequencing depth (FIGURES 8A-8D). Differences were observed depending on the algorithm used: Centrifuge and Kraken2 showed higher read assignment compared to Minimap2. Additionally, there was read assignment by only one software in the case of CGRMV in sample LI, where reads were assigned by Centrifuge but not by Kraken2 or Minimap2 (FIGURES 8A-8D). Similar discrepancies were observed in the cases of ACLSV for SD-L1 (assigned reads only by Kraken2), ApMV for SD-L1 (by Centrifuge), LChV-1 for SD-L1 (by Kraken2), LChV-1 for SD-L2 (by Kraken2), ACLSV for SD-S1 (by Centrifuge),ApMV for SD-S1 (by Centrifuge), and LChV-1 for SD-S 1 (by Kraken2), albeit with a relatively low number of reads (FIGURES 8A-8D). As shown in FIGURES 9A-9D, viral detection depends on the sample and the abundance of viral sequences, for example, the cases of CNRMV for SD-L1 (completeness = 1.0 at IM reads), CGRMV for SD-S1 (completeness ~ 0.72 at 15M reads), CNRMV for SD-S1 (completeness ~ 0.90 at 15M reads), PDV for SD- S 1 (completeness ~ 0.60 at 15M reads), CNRMV for SD-L2 (completeness = 1.0 at 15M reads), CGRMV for SD-S2 (completeness ~ 0.10 at 15M reads), and CNRMV for SD-S2 (completeness ~ 0.85 at 15M reads). In all cases where only one algorithm could assign reads and the completeness was ~ 0 (e.g., CGRMV for SD-L1) (FIGURES 9A-9D), the assigned reads could not be assembled into contigs.

[0068] Regarding the RT-PCRs, a total of 88 RT-PCR analyses (external and internal) accounted for the detection of the 11 viruses. The summary of the results, including internal PCR, external PCR, and viral detection through the methodology proposed in this invention, is shown in Table 11. The diagnosis performed by the methodology used the following criteria based on what was described in previous sections: a completeness > 0.3 for a positive viral detection, a completeness between 0.1 and 0.3 for a positive viral detection only when a replicase can be identified, a negative detection for samples with completeness < 0.1, and finally, agreement between 2 out of the 3 algorithms used. The Panel-II was used as the reference database in this case. The detailed results of the diagnosis performed by the methodology of the invention are shown in FIGURES 10A-10D (outbreak development stage, SD) and FIGURES 11A-11D (senescence stage, SS). A high coherence is observed between the NGS / HTS-based detection and the RT-PCR analyses, where 82 out of 88 (93.2%) matches were obtained. The inconsistencies observed among the RT-PCR analyses were only found for sample S2, which tested positive for the CNRMV virus in the SD stage through external PCR but negative in the SS stage through internal PCR. This was also the case for PDV. Regarding our methodology, the CNRMV virus tested positive in both samples, regardless of the seasonal conditions. On the other hand, the diagnosis by our methodology matched each RT-PCR test in the case of PDV, considering the seasonality of the sampling. Contrasting the RT-PCR results obtained in the SD and SS stages, some discrepancies were found in 2 out of 44 cases (4.5%): CNRMV in the SS-S2 sample and PDV in the SS-S2 sample. These results show that the sampling season affects the sensitivity of the diagnosis, particularly with higher sensitivity during the outbreak development stage.

[0069] Notably, the diagnosis by the disclosed methodology proved to be more sensitive than the RT-PCR methods (internal and external), as samples SD-L1, SD-S1, and SD-S2 tested positive for ApMV (FIGURES 10A-10D), while none of the laboratories were able to detect it (Table 11). This was particularly evident considering the number of reads assigned by at least two out of the three algorithms, ranging from 10,000 to 20,000 reads in samples SD-L1 and SD-S2, and between 1,500 and 2,500 reads in sample SD-S1. Replicases were also identified by two out of the three algorithms with a completeness between 0.1 and 0.3. Furthermore, the diagnosis for CNRMV in the SS-S2 sample was positive according to our methodology (completeness = 1 with replicase identification), while for PDV (SS-S1 sample) and PNRSV (SS-L2 sample) it was negative (completeness = 0 in both cases). Although the number of reads for PNRSV (SS-L2 sample) reached over 4,000 reads assigned by two out of the three algorithms, they were unable to assemble a contig with completeness higher than 0.1 or containing a replicase (FIGURES 5A-5B). The specific cases of CNRMV, PDV, and PNRSV point towards a difference in sensitivity or amplification issues likely due to discrepancies in the primer binding region (false negative) or cross-contamination during sample and reagent handling (false positive). In fact, for PDV or PNRSV, the inspection of sequences revealed that the hybridization regions contained at least one nucleotide misaligned with the primers used.Table 11. Pipeline validation by RT-PCR.

[0070] When comparing only the seasonal differences in the diagnosis obtained through our methodology, 6 cases were detected in SD but not in SS: ApMV in LI, L2, and SI; PDV in SI and S2, and PNRSV in L2. For all these cases, the completeness value was below the minimum threshold, and the assigned reads were drastically reduced between the two seasons. Furthermore, in the cases of SD-S 1 and SD-S2 for PDV and SD-L2 for PNRSV, they were diagnosed as positive based on completeness > 0.3, but replicases were not identified. These results once again demonstrate that the bud development stage allows for greater sensitivity in viral detection using NGS / HTS-based diagnosis.

[0071] Finally, it is important to highlight the use of different viral panels in the diagnostic process. According to the results described so far, the completeness values were not sufficientto diagnose LChV-1 as positive in the SD-S2 sample when Panel-I was used (FIGURES 9A- 9D), while it appeared positive when Panel-II was used (FIGURES 10A-10D). Sequence analysis revealed that the LChV-1 reference genome in the first panel shares 76% sequence identity with the respective reference genome selected in the second panel, which explains the differences in diagnosis.

[0072] In conclusion, this example demonstrates that field sample analysis using sequencing and RT-PCR shows that viral diagnosis based on NGS / HTS is more sensitive than RT-PCR, and that seasonal sampling influences viral abundance. Comparisons between RT-PCR panels showed that for spring and summer samples, viral detection by RT-PCR corresponded to 93.2% of NGS / HTS-based detection using the methodology described here, with differences mainly attributed to false negatives and false positives in RT-PCR detection. Moreover, issues related to PCR design bias are evident when primers contain discrepancies with viral variants. Additionally, seasonal sampling affects the ability to detect 6 viruses in the summer through NGS / HTS. However, all the undetectable viruses (ApMV, PDV, PNRSV) belong to the Illarvirus genus, which is known for its lability and described as thermolabile (Hull, 2014. Replication of Plant Viruses. Plant Virology, pp.341-421). The cases of PDV and PNRSV are particularly interesting as no replicases are identified in the SD stage. For PNRSV, approximately 4,000 reads are assigned, but they do not assemble into a contig with sufficient completeness to be diagnosed as positive. The reads are assigned to a specific region at the 5' end of one of the viral genome RNA segments. Possible explanations for this include an endogenous viral element in the plant genome, a past infection that had already been overcome, or host sequences assigned to the virus. However, this case and the ApMV cases emphasize the advantages of an understanding of viral physiology and the use of essential viral functions during detection for a specific diagnosis.***

[0073] One embodiment of the methods described herein utilizes a predefined panel of viral references for diagnostic purposes. This is because the methods described herein may be used as a viral diagnostic method for phytosanitary detection rather than for discovery. Compiling a viral sequence database using published reference sequences may not be sufficient for identification, as databases are generally biased towards records from certain geographic locations or specific pathogens. The inclusion of other isolates or viral variants to generate an expanded database (e.g., Panel-II) allows successful identification by the methodology. Despite this improvement in diagnosis (in terms of read assignment), more misassigned cases can beobtained as a side effect. The methodology proves to be robust enough when considering the completeness values and / or replicase identification, as misassigned reads cannot assemble into contigs. This may be beneficial to consider when considering conducting the diagnosis in particular geographic contexts, as viral isolates or variants can be endemic or affect specific plant varieties, thus the construction of a reference panel may benefit from taking these distinctive characteristics into account. Additionally, viral variants can be different enough to have an impact on the diagnosis.

[0074] These examples demonstrate that the use of completeness is a more robust measure than read assignment for viral detection. Read assignment can be prone to generating false positives in circumstances where the host has derived viral-origin sequences, such as endogenous viral elements (Massart et al., 2019. Virus Detection by High-Throughput Sequencing of Small RNAs: Large-Scale Performance Testing of Sequence Analysis Strategies. Phytopathology. 109(3), pp.488-497). Additionally, the number of reads is directly influenced by sequencing depth and library preparation bias, and direct comparisons between datasets may benefit from normalization. On the other hand, the use of completeness is a better unifying measure independent of the sequencing technology used since sequencing depth can differ by several orders of magnitude between platforms and laboratories, while completeness is the result of an assembly, which also takes into account read length. Completeness, rather than sequencing coverage, has been shown herein to be a better metric as the latter can exhibit artificially high numbers when reads only cover a small portion of the genome or when contigs correspond to different viral variants, which can also exacerbate detection.

[0075] On the other hand, the identification of functional elements (e.g., replicases) in the assembled contigs allows for investigating the biological implications of different completeness thresholds. Defining thresholds for viral detection is extremely challenging and involves selecting a value that maximizes both sensitivity and specificity. It has been found herein that replicases can be consistently identified only above a completeness value of 0.3, which allows us to empirically define this threshold as a level that enables a biological interpretation of the metric. Furthermore, the presence of replicases (or any other essential function) is already an excellent criterion for viral diagnosis even in cases of even lower completeness values.

Claims

CLAIMS1. A computer-implemented method for identifying a presence of one or more biological entities in a biological sample, the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained from the biological sample; assembling, using the one or more processors, the plurality of sequence reads into a plurality of contigs based on the sequence read data; generating, using the one or more processors, an assignment for each sequence read and / or each contig based on a comparison of the plurality of sequence reads and / or the plurality of contigs to a first reference sequence database comprising reference sequences for the one or more biological entities; aligning, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequence and generating a consensus scaffold sequence corresponding to each reference sequence; and identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.

2. The computer- implemented method of claim 1, comprising: identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each of the one or more consensus scaffold sequences, and functionally annotating each of the one or more consensus scaffold sequences to identify a functional element of the corresponding biological entity.

3. The computer- implemented method of claim 1 or 2, wherein the method comprises generating, using the one or more processors, an assignment for each sequence read based on a comparison of the plurality of sequence reads to the first reference sequence database comprising reference sequences for the one or more biological entities.

4. The computer-implemented method of any one of claims 1-3, wherein the method comprises generating, using the one or more processors, an assignment for each contig based on a comparison of the plurality of contigs to the first reference sequence database comprising reference sequences for the one or more biological entities.

5. The computer- implemented method of any one of claims 1-4, further comprising processing the plurality of sequence reads prior to assembling the plurality of sequence reads into contigs.

6. The computer-implemented method of claim 5, wherein the processing comprises generating, using the one or more processors, a pre-assignment for each sequence read based on a comparison of the plurality of sequence reads to the first reference sequence database or a second reference sequence database, wherein the first or second reference sequence database comprises reference sequences for a plurality of taxa and pre-assigning each sequence read to a taxon based on the comparison.

7. The computer- implemented method of claim 6, wherein the method further comprises identification and removal of one or more background sequence reads from the plurality of sequence reads based on the pre-assignment.

8. The computer- implemented method of 7, wherein the background sequence reads comprise sequence reads corresponding to plant nucleic acid sequences, animal nucleic acid sequences, or a combination thereof.

9. The computer- implemented method of claim 8, wherein the background sequence reads comprise sequence reads corresponding to plant nucleic acid sequences.

10. The computer-implemented method of claim 8, wherein the background sequence reads comprise sequence reads corresponding to human nucleic acid sequences.

11. The computer- implemented method of any one of claims 1-10, wherein the plurality of sequence reads comprises or consists essentially of sequence reads obtained from high- throughput sequencing.

12. The computer- implemented method of claim 11, wherein the plurality of sequence reads are obtained from high-throughput sequencing using Illumina sequencing, Oxford Nanopore sequencing, Pacific Biosciences sequences, or any combination thereof.

13. A computer-implemented method for identifying a presence of one or more biological entities in a biological sample, the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained from high-throughput sequencing of nucleic acids from the biological sample;processing and filtering, using the one or more processors, the sequence reads to generate filtered sequence read data for a plurality of filtered sequence reads; generating, using the one or more processors, an assignment for each filtered sequence read based on a comparison of the plurality of filtered sequence reads to a first reference sequence database comprising reference sequences for the one or more biological entities; assembling, using the one or more processors, the plurality of filtered sequence reads into a plurality of contigs based on the filtered sequence read data; aligning, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequences and generating a consensus scaffold sequence corresponding to each reference sequence; and identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.

14. The computer- implemented method of any one of claims 1-13, wherein the method comprises obtaining the plurality of sequence reads from the biological sample.

15. The computer-implemented method of any one of claims 1-14, wherein the method comprises sequencing a plurality of nucleic acid sequences extracted from the biological sample to obtain the plurality of sequence reads.

16. The computer- implemented method of any one of claims 1-15, wherein the method comprises obtaining the biological sample from a host organism.

17. The computer-implemented method of claim 16, wherein the host organism is an animal or a plant.

18. The computer-implemented method of claim 17, wherein the host organism is a plant.

19. The computer- implemented method of any one of claims 1-18, wherein the method further comprises generating, using the one or more processors, a report indicating the identified presence of the one or more biological entities in the biological sample.

20. The computer- implemented method of claim 19, further comprising transmitting the report to a third party.

21. The computer- implemented of claim 20, wherein the report is transmitted via a computer network or a peer-to-peer connection.

22. The computer- implemented method of any one of claims 1-21, wherein the plurality of sequence reads comprise or consist essentially of reads obtained by high-throughput DNA sequencing.

23. The computer- implemented method of any one of claims 1-21, wherein the plurality of sequence reads comprise or consist essentially of reads obtained by high-throughput RNA sequencing.

24. The computer-implemented method of any one of claims 5-23, wherein the processing comprises generating, using the one or more processors, a pre- assignment for each sequence read based on a comparison of the plurality of sequence reads to the first reference sequence database or a second reference sequence database, wherein the first or second reference sequence database comprises reference sequences for a plurality of taxa and pre-assigning each sequence read to a taxon based on the comparison.

25. The computer- implemented method of claim 24, wherein the first or second reference sequence database comprises reference sequences of the one or more biological entities and reference sequences of one or more additional organisms.

26. The computer- implemented method of claim 25, wherein the one or more additional organisms comprise animals, plants, fungi, or a combination thereof.

27. The computer- implemented method of claim 26, wherein the one or more additional organisms comprise one or more plants.

28. The computer-implemented method of any one of claims 13-27, wherein the filtering comprises identifying and removing, using the one or more processors, one or more background sequence reads from the plurality of sequence reads based on the pre-assignment.

29. The computer-implemented method of claim 28, wherein the one or more background sequence reads are identified and removed based on pre-assignment to the one or more additional organisms.

30. The computer-implemented method of 28 or 29, wherein the background sequence reads comprise sequence reads corresponding to plant nucleic acid sequences, animal nucleic acid sequences, or a combination thereof.

31. The computer- implemented method of claim 30, wherein the background sequence reads comprise sequence reads corresponding to plant nucleic acid sequences.

32. The computer-implemented method of claim 30, wherein the background sequence reads comprise sequence reads corresponding to human nucleic acid sequences.

33. The computer-implemented method of any one of claims 1-32, wherein the one or more biological entities comprise or consist essentially of microorganisms.

34. The computer-implemented method of claim 14, wherein the microorganisms comprise commensal or beneficial microorganisms.

35. The computer-implemented method of claim 14 or 34, wherein the microorganisms comprise pathogenic microorganisms.

36. The computer- implemented method of claim 35, wherein the pathogenic microorganisms comprise bacteria, viruses, viroids, or any combination thereof.

37. The computer-implemented method of claim 35, wherein the pathogenic microorganisms comprise viruses and / or viroids.

38. The computer-implemented method of any one of claims 35-37, wherein the pathogenic microorganisms comprise pathogens of one or more plants and / or one or more animals.

39. The computer- implemented method of claim 38, wherein the pathogenic microorganisms comprise pathogens of one or more animals.

40. The computer-implemented method of claim 39, wherein the pathogenic microorganisms comprise pathogens of humans.

41. The computer- implemented method of claim 38, wherein the pathogenic microorganisms comprise pathogens of one or more plants.

42. The computer- implemented method of claim 41, wherein the pathogenic microorganisms comprise viruses and / or viroids that infect one or more plants.

43. The computer- implemented method of any one of claims 1-42, wherein generating the assignment, generating the pre- assignment, or both is performed by one or more assignment algorithms.

44. The computer- implemented method of claim 43, wherein the one or more assignment algorithms comprise one or more algorithms that generate assignments based on the comparison or search of small nucleotide sequences.

45. The computer- implemented method of claim 43 or 44, wherein the one or more assignment algorithms comprise one or more algorithms that generate assignments based on sequence comparison through alignment.

46. The computer- implemented method of any one of claims 43-45, wherein generating the assignment, generating the pre-assignment, or both is performed using two, three, or more assignment algorithms.

47. The computer-implemented method of claim 46, wherein the assignment or pre- assignmentis generated based on the consensus of the two, three, or more assignment algorithms.

48. The computer-implemented method of any one of claims 43-47, wherein the one or more assignment algorithms comprise Kraken2, Centrifuge, Minimap2, or a combination thereof.

49. The computer-implemented method of any one of claims 1-48, wherein the first reference sequence database, the second reference sequence database, or both comprise nucleic acid sequences of bacteria, viruses, viroids, or any combination thereof.

50. The computer-implemented method of any one of claims 1-48, wherein the first reference sequence database, the second reference sequence database, or both comprise nucleic acid sequences of viruses and viroids.

51. The computer-implemented method of any one of claims 1-50, wherein the assembly of the plurality of sequence reads into contigs is performed using at least one de novo assembly algorithm.

52. The computer- implemented method of claim 51, wherein the de novo assembly algorithm is designed for the assembly of short reads, long reads, or a combination thereof.

53. The computer-implemented method of any one of claims 1-52, wherein one or more of the reference sequences comprise two or more segments.

54. The computer-implemented method of any one of claims 1-53, wherein the one or more reference sequences comprise whole genome sequences.

55. The computer-implemented method of claim 54, wherein one or more of the whole genome sequences comprise two or more segments, each segment representing a different chromosome.

56. The computer- implemented method of any one of claims 1-55, wherein the one or more reference sequences comprise nucleic acid sequences of microorganisms.

57. The computer-implemented method of claim 56, wherein the microorganisms comprise bacteria, viruses, viroids, or any combination thereof.

58. The computer-implemented method of claim 56, wherein the microorganisms comprise viruses and / or viroids.

59. The computer- implemented method of any one of claims 1-58, wherein the completeness value for each consensus scaffold sequence is calculated based on the vertical coverage of the corresponding reference sequence by the consensus scaffold sequence.

60. The computer-implemented method of any one of claims 1-59, wherein the completeness value for consensus scaffold sequence is calculated as the percentage of nucleotide positions of the corresponding reference sequence having a vertical coverage of > 1 by the consensus scaffold sequence.

61. The computer- implemented method of any one of claims 1-60, wherein calculating the completeness value for each consensus scaffold sequence comprises aligning the consensus scaffold sequence to the corresponding reference sequence, wherein the completeness value is calculated as the percentage of nucleotide positions of the reference sequence having a vertical coverage of > 1 by the aligned consensus scaffold sequence.

62. The computer-implemented method of any one of claims 1-61, wherein the completeness value for each consensus scaffold sequence is calculated according to the equation:wherein CBi is the number of nucleotide positions of segment z of the corresponding reference sequence having a vertical coverage of > 1, and wherein SL, is the total number of nucleotide positions in segment z.

63. The computer- implemented method of any one of claims 1-62, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.1 or 10%.

64. The computer-implemented method of any one of claims 1-62, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.2 or 20%.

65. The computer- implemented method of any one of claims 1-62, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.3 or 30%.

66. The computer- implemented method of any one of claims 1-65, wherein the functional annotation of the consensus scaffold sequence indicates the presence of one or more functional elements of the corresponding biological entity.

67. The computer- implemented method of claim 66, wherein the one or more functional elements comprise a nucleic acid sequence encoding a protein.

68. The computer-implemented method of claim 67, wherein the protein is required for the survival of the biological entity, the replication of the biological entity, or both.

69. The computer- implemented method of claim 66 or 67, wherein the protein is an enzyme.

70. The computer-implemented method of claim 69, wherein the one or more functional elements comprise one or more nucleic acid sequences encoding a DNA replicase, an RNA replicase, a DNA polymerase, or an RNA polymerase.

71. The computer- implemented method of claim 69, wherein the one or more functional elements comprise one or more nucleic acid sequences each encoding an RNA-dependent RNA polymerase (RdRp), an RNA-dependent DNA polymerase (RdDp), a DNA-dependent DNA polymerase (DdDp), a reverse transcriptase, or any combination thereof.

72. The computer- implemented method of any one of claims 66-71, wherein a particular biological entity is identified as present in the biological sample if the one or more functional elements are present in the corresponding consensus scaffold sequence.

73. The computer- implemented method of claims 66-71, wherein the biological entity is identified as present in the biological sample if the completeness value is greater than 0.1 and the one or more functional elements are present in the corresponding consensus scaffold sequence.

74. A kit for identifying a presence of one or more biological entities in a biological sample, the kit comprising: one or more containers for holding one or more biological samples comprising nucleic acids; one or more solutions for the isolation and / or preservation of the nucleic acids within the one or more containers, wherein said one or more solutions comprise at least one of a lysis buffer solution and an elution buffer solution; and instructions for the using the kit to perform a method for identifying the presence of the one or more biological entities in the biological sample, the method comprising: extracting nucleic acids from the biological sample using the one or more containers and the one or more solutions; sequencing the extracted nucleic acids to obtain a plurality of sequence reads; receiving, at one or more processors, sequence read data for the plurality of sequence reads obtained from the biological sample; assembling, using the one or more processors, the plurality of sequence reads into a plurality of contigs based on the sequence read data; generating, using the one or more processors, an assignment for each sequence read and / or each contig based on a comparison of the plurality of sequence reads and / or the plurality of contigs to a first reference sequence database comprising reference sequences for the one or more biological entities;aligning, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequences and generating a consensus scaffold sequence corresponding to each reference sequence; and identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.

75. A kit for identifying a presence of one or more biological entities in a biological sample, the kit comprising: one or more containers for holding one or more biological samples comprising nucleic acids; one or more solutions for the isolation and / or preservation of the nucleic acids within the one or more containers, wherein said one or more solutions comprise at least one of a lysis buffer solution and an elution buffer solution; and instructions for the using the kit to perform a method for identifying the presence of the one or more biological entities in the biological sample, the method comprising: extracting nucleic acids from the biological sample using the one or more containers and the one or more solutions; sequencing the extracted nucleic acids using high-throughput sequencing to obtain a plurality of sequence reads; receiving, at one or more processors, sequence read data for the plurality of sequence reads; processing and filtering, using the one or more processors, the sequence reads to generate filtered sequence read data for a plurality of filtered sequence reads; generating, using the one or more processors, an assignment for each filtered sequence read based on a comparison of the plurality of filtered sequence reads to a first reference sequence database comprising reference sequences for the one or more biological entities; assembling, using the one or more processors, the plurality of filtered sequence reads into a plurality of contigs based on the filtered sequence read data;aligning, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequences and generating a consensus scaffold sequence corresponding to each reference sequence; and identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.

76. The kit of claim 74 or 75, wherein the biological sample comprises nucleic acids derived from the biological entities and nucleic acids derived from plants, nucleic acids derived from animals, or a combination thereof.

77. The kit of claim 76, wherein the biological sample comprises nucleic acids derived from the biological entities and nucleic acids derived from plants.

78. The kit of claim 76 or 77, wherein the biological sample comprises nucleic acids derived from the biological entities and nucleic acids derived from humans.

79. The kit of any one of claims 74-78, wherein the kit further comprises a binding matrix for the retention of genetic material.

80. The kit of any one of claims 74-79, wherein the biological entities comprise or consist essentially of microorganisms.

81. The kit of claim 80, wherein the microorganisms comprise commensal or beneficial microorganisms.

82. The kit of claim 80 or 81, wherein the microorganisms comprise pathogenic microorganisms.

83. The kit of claim 82, wherein the pathogenic microorganisms comprise bacteria, viruses, viroids, or any combination thereof.

84. The kit of claim 82, wherein the pathogenic microorganisms comprise viruses and / or viroids.

85. The kit of any one of claims 82-84, wherein the pathogenic microorganisms comprise pathogens of plants, pathogens of animals, or a combination thereof.

86. The kit of claim 85, wherein the pathogenic microorganisms comprise pathogens of plants.

87. The kit of claim 85 or 86, wherein the pathogenic microorganisms comprise pathogens of humans.

88. The kit of any one of claims 82-87, wherein the pathogenic microorganisms comprise viruses and / or viroids that infect plants.

89. The kit of any one of claims 74-88, wherein the plurality of sequence reads are obtained from high-throughput sequencing using Illumina sequencing, Oxford Nanopore sequencing, Pacific Biosciences sequences, or any combination thereof.

90. The kit of any one of claims 74-89, wherein the method comprises calculating the completeness value for each consensus scaffold sequence based on the vertical coverage of the corresponding reference sequence by the consensus scaffold sequence.

91. The kit of any one of claims 74-90, wherein the method comprises calculating the completeness value for each consensus scaffold sequence as the percentage of nucleotide positions of the corresponding reference sequence having a vertical coverage of > 1 by the consensus scaffold sequence.

92. The kit of any one of claims 74-91, wherein the method comprises calculating the completeness value for each consensus scaffold sequence by aligning the consensus scaffold sequence to the corresponding reference sequence, wherein the completeness value is calculated as the percentage of nucleotide positions of the reference sequence having a vertical coverage of > 1 by the aligned consensus scaffold sequence.

93. The kit of any one of claims 74-92, wherein the method comprises identifying a particular biological entity as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.1 or 10%.

94. The kit of any one of claims 74-92, wherein the method comprises identifying a particular biological entity as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.2 or 20%.

95. The kit of any one of claims 74-92, wherein the method comprises identifying a particular biological entity as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.3 or 30%.

96. The kit of any one of claims 74-95, wherein the functional annotation of the consensus scaffold sequence indicates the presence of one or more functional elements of the corresponding biological entity.

97. The kit of claim 96, wherein the one or more functional elements comprise a nucleic acid sequence encoding a protein.

98. The kit of claim 97, wherein the protein is required for the survival of the biological entity, the replication of the biological entity, or both.

99. The kit of claim 97-98, wherein the protein is an enzyme.

100. The kit of claim 98, wherein the one or more functional elements comprise one or more nucleic acid sequences encoding a DNA replicase, an RNA replicase, a DNA polymerase, or an RNA polymerase.

101. The kit of claim 98, wherein the one or more functional elements comprise one or more nucleic acid sequences each encoding an RNA-dependent RNA polymerase (RdRp), an RNA-dependent DNA polymerase (RdDp), a DNA-dependent DNA polymerase (DdDp), a reverse transcriptase, or any combination thereof.

102. The kit of any one of claims 66-71, wherein the method comprises identifying a particular biological entity as present in the biological sample if the one or more functional elements are present in the corresponding consensus scaffold sequence.

103. The kit of claims 74-102, wherein the method comprises identifying a particular biological entity as present in the biological sample if the completeness value is greater than 0.1 and the one or more functional elements are present in the corresponding consensus scaffold sequence.

104. A system for identifying a presence of one or more biological entities in a biological sample, the system comprising: one or more processors; and a memory comprising instructions executable by the one or more processors that, when executed by the one or more processors, cause the system to: receive, at the one or more processors, sequence read data for a plurality of sequence reads obtained from the biological sample; assemble, using the one or more processors, the plurality of sequence reads into a plurality of contigs based on the sequence read data; generate, using the one or more processors, an assignment for each sequence read and / or each contig based on a comparison of the plurality of sequence reads and / or the plurality of contigs to a first reference sequence database comprising reference sequences for the one or more biological entities; align, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequences and generate a consensus scaffold sequence corresponding to each reference sequence; and identify, using the one or more processors, the presence of the one or more biological entities in the biological sample based on:calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.

105. A system for identifying a presence of one or more biological entities in a biological sample, the system comprising: one or more processors; and a memory comprising instructions executable by the one or more processors that, when executed by the one or more processors, cause the system to: receive, at one or more processors, sequence read data for a plurality of sequence reads obtained from high-throughput sequencing of nucleic acids from the biological sample; process and filter, using the one or more processors, the sequence reads to generate filtered sequence read data for a plurality of filtered sequence reads; generate, using the one or more processors, an assignment for each filtered sequence read based on a comparison of the plurality of filtered sequence reads to a first reference sequence database comprising reference sequences for the one or more biological entities; assemble, using the one or more processors, the plurality of filtered sequence reads into a plurality of contigs based on the filtered sequence read data; align, using the one or more processors, contigs assigned to each of the one or more biological entities to the corresponding reference sequences and generate a consensus scaffold sequence corresponding to each reference sequence; and identifying, using the one or more processors, the presence of the one or more biological entities in the biological sample based on: calculating a completeness value for each consensus scaffold sequence, and / or functionally annotating each consensus scaffold sequence to identify a functional element of the corresponding biological entity.

106. The system of claim 104 or 105, wherein the completeness value for each consensus scaffold sequence is calculated based on the vertical coverage of the corresponding reference sequence by the consensus scaffold sequence.

107. The system of any one of claims 104-106, wherein the completeness value for consensus scaffold sequence is calculated as the percentage of nucleotide positions of the corresponding reference sequence having a vertical coverage of > 1 by the consensus scaffold sequence.

108. The system of any one of claims 104-107, wherein calculating the completeness value for each consensus scaffold sequence comprises aligning the consensus scaffold sequence to the corresponding reference sequence, wherein the completeness value is calculated as the percentage of nucleotide positions of the reference sequence having a vertical coverage of > 1 by the aligned consensus scaffold sequence.

109. The system of any one of claims 104-108, wherein the completeness value for each consensus scaffold sequence is calculated according to the equation:wherein CBi is the number of nucleotide positions of segment z of the corresponding reference sequence having a vertical coverage of > 1, and wherein SL, is the total number of nucleotide positions in segment z.

110. The system of any one of claims 104-109, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.1 or 10%.

111. The system of any one of claims 104-109, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.2 or 20%.

112. The system of any one of claims 104-109, wherein a particular biological entity is identified as present in the biological sample if the completeness value for the corresponding consensus scaffold sequence is greater than 0.3 or 30%.

113. The system of any one of claims 104-112, wherein the functional annotation of the consensus scaffold sequence indicates the presence of one or more functional elements of the corresponding biological entity.

114. The system of claim 113, wherein the one or more functional elements comprise a nucleic acid sequence encoding a protein.

115. The system of claim 114, wherein the protein is required for the survival of the biological entity, the replication of the biological entity, or both.

116. The system of claim 113 or 114, wherein the protein is an enzyme.

117. The system of claim 115, wherein the one or more functional elements comprise one or more nucleic acid sequences encoding a DNA replicase, an RNA replicase, a DNA polymerase, or an RNA polymerase.

118. The system of claim 115, wherein the one or more functional elements comprise one or more nucleic acid sequences each encoding an RNA-dependent RNA polymerase (RdRp), an RNA-dependent DNA polymerase (RdDp), a DNA-dependent DNA polymerase (DdDp), a reverse transcriptase, or any combination thereof.

119. The system of any one of claims 104-118, wherein a particular biological entity is identified as present in the biological sample if the one or more functional elements are present in the corresponding consensus scaffold sequence.

120. The system of claims 104-118, wherein the biological entity is identified as present in the biological sample if the completeness value is greater than 0.1 and the one or more functional elements are present in the corresponding consensus scaffold sequence.

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