Detection of viral sequences in metagenomic data

Abstract

Provided herein are methods and systems for detecting polynucleotide sequences encoding viral capsids in metagenomic data. The methods and systems disclosed herein may include a sequence alignment-based module, a gene-based data processing module, and further characterization of putative viral sequences to identify novel polynucleotide sequences encoding viral capsids in metagenomic data.

Description

[Technical Field]

[0001] The present disclosure relates to methods and systems for detecting polynucleotide sequences encoding viral capsids in metagenomic data.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 481,466, filed January 25, 2023, and European Patent Application No. 23315155.4, filed May 3, 2023, the contents of which are incorporated herein by reference in their entireties. [Background technology]

[0003] Although there are likely at least hundreds of millions of viral species on Earth, the central GenBank virus database contains sequences for only tens of thousands of viral species. Mining the virome has revealed thousands of novel viruses, and the rate of viral discovery has increased exponentially, driven primarily by the increasing availability of high-throughput sequencing. Historically, researchers have used metagenomic approaches to identify and characterize bacterial populations. However, it is becoming clear that such profiling can also detect viral sequences.

[0004] Naturally occurring novel AAV capsids can be detected in metagenomic data. Indeed, the increase in virus mining in metagenomic samples has accelerated recently as a result of improved data availability and decreased computational costs and time. Over 160 currently known AAV isolates have been identified from genomic samples derived from human or nonhuman primate (NHP) sources. AAVs have been classified into six genogroups (clades A-F) and two clonal isolates (AAV4 and AAV5) based on phylogeny and serology.

[0005] Computational approaches for identifying viral sequences from metagenomic data can be categorized into four broad categories based on their approach. First, there are sequence alignment-based approaches, in which viral metagenomic reads are determined based on sequence alignment. However, current viral genome databases are biased toward specific types of viruses whose hosts can be cultured in the laboratory, and current viral references provide an incomplete representation of the entire viral diversity. Second, there are gene-based similarity approaches, in which viral sequences are detected by comparing the genes of a query sequence with a viral gene database. However, gene-based similarity methods require a query sequence containing complete genes and therefore cannot predict sequences from non-coding regions. Third, there are genomic feature-based machine learning (ML) approaches, in which genomic features are used as inputs to train (ML) classifiers to distinguish between viral and microbial sequences. However, the manual curation and testing associated with these ML approaches are often crucial steps in the process of detecting genomic features. Fourth, there is the k-mer frequency-based ML approach, in which the frequency of a DNA "word" of length k found in known viral genomes is provided as input for training an ML classifier. However, viral sequences are known to exhibit homology with host genome sequences, likely as an evolutionary strategy to overcome host genomic defenses. This phenomenon of virus-host genome homology can cause classifiers based on k-mer frequency to perform poorly in terms of overall accuracy and recall. Furthermore, while several tools have been developed to detect viral sequences in metagenomic data, the pipelines are not specialized for AAV capsids, which may be particularly useful for applications including human gene therapy. Summary of the Invention [Problem to be solved by the invention]

[0006] Thus, there is a need for methods for detecting and identifying viral sequences encoding novel viral capsids, such as adeno-associated virus (AAV) capsids, in metagenomic data with increased sensitivity to rare sequences, increased computational speed, and reduced computational cost. [Means for solving the problem]

[0007] The present disclosure is based in part on the discovery that novel AAV isolates can be identified in publicly available metagenomic data based on their similarity to naturally isolated AAVs as a reference. Based on the identified sequences, the functional properties of the virus, e.g., capsid, depend on the AAV capsid amino acid sequence, capsid structure, and interactions with host cell factors. The present disclosure is directed to methods, systems, and computer programs for identifying novel AAV capsids in metagenomic data. Provided herein are methods for mining, for example, next-generation sequencing (NGS) data for putative Parvoviridae contigs, further extracting sequences encoding AAV-like capsid proteins from the identified contigs, and assessing the novelty of the identified sequences.

[0008] In one aspect, provided herein is a method for identifying novel viral polypeptide sequences by screening polynucleotide sequencing data. The method provided herein may include obtaining polynucleotide sequencing data including a plurality of sequencing reads, aligning each of the plurality of sequencing reads to one or more reference sequences, assembling a plurality of sequence contigs from the plurality of sequencing reads aligned to the one or more reference sequences, selecting one or more contigs including viral open reading frame (ORF) sequences based on the one or more reference sequences, and identifying one or more novel viral polypeptide sequences based on translated polypeptide sequences of the viral ORFs. In some embodiments, the one or more reference sequences are viral reference sequences. In some embodiments, the one or more viral reference sequences are Parvoviridae reference sequences. In some embodiments, the one or more viral reference sequences are adeno-associated virus (AAV) reference sequences. In some embodiments, the AAV reference sequence is a sequence encoding an AAV cap protein. In some embodiments, the polynucleotide sequencing data is metagenomic data. In some embodiments, the plurality of sequencing reads is generated by next-generation sequencing (NGS).

[0009] In some embodiments, the selecting step further comprises specifying a minimum circular contig length. In some embodiments, the minimum circular contig length is 4000 bp. In some embodiments, the selecting step further comprises specifying a minimum linear contig length. In some embodiments, the minimum linear contig length is 1500 bp. In some embodiments, the minimum circular contig length is 4000 bp and the minimum linear contig length is 1500 bp. In some embodiments, the identifying step further comprises specifying a minimum ORF length. In some embodiments, the minimum ORF length is 685 amino acids. In some embodiments, the identifying step further comprises a BLAST alignment analysis. In some embodiments, a BLAST alignment analysis is performed against previously annotated natural AAV isolates and / or previously discovered Parvoviridae amino acid sequences.

[0010] In another aspect, provided herein is a computer program product tangibly embodied in a computer-readable medium, the computer program product comprising instructions, when executed by one or more processors, configured to receive polynucleotide sequencing data comprising a plurality of sequencing reads; align each of the plurality of sequencing reads to one or more reference sequences; assemble a plurality of sequence contigs from the plurality of sequencing reads aligned to the one or more reference sequences; select one or more contigs comprising viral open reading frame (ORF) sequences based on the one or more reference sequences; and identify one or more novel viral polypeptide sequences based on the translated polypeptide sequences of the viral ORFs. In some embodiments, the one or more reference sequences are viral reference sequences. In some embodiments, the one or more viral reference sequences are Parvoviridae sequences. In some embodiments, the viral reference sequence is an adeno-associated virus (AAV) reference sequence. In some embodiments, the AAV reference sequence is a sequence encoding an AAV cap protein. In some embodiments, the polynucleotide sequencing data is metagenomic data. In some embodiments, the plurality of sequencing reads are generated by next generation sequencing (NGS).

[0011] In some embodiments, the selecting step further comprises specifying a minimum circular contig length. In some embodiments, the minimum circular contig length is 4000 bp. In some embodiments, the selecting step further comprises specifying a minimum linear contig length. In some embodiments, the minimum linear contig length is 1500 bp. In some embodiments, the minimum circular contig length is 4000 bp and the minimum linear contig length is 1500 bp. In some embodiments, the identifying step further comprises specifying a minimum ORF length. In some embodiments, the minimum ORF length is 685 amino acids. In some embodiments, the identifying step further comprises a BLAST alignment analysis. In some embodiments, a BLAST alignment analysis is performed against previously annotated natural AAV isolates and / or previously discovered Parvoviridae amino acid sequences.

[0012] The present disclosure offers several advantages. Provided herein is a custom AAV-specific pipeline for extracting and identifying novel AAVs in putative Parvoviridae-containing contigs. These methods and systems are AAV-specific and combine two computational pipelines to achieve computational speeds up to 500 times faster than previously available. These advantages enable accurate characterization of relatively rare novel AAVs in petabytes of metagenomic data, thereby expanding the repertoire of AAV isolates for human gene therapy.

[0013] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and are not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.

[0014] Other features and advantages of the invention will become apparent from the following detailed description and drawings, and from the claims. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a schematic diagram generally illustrating the computational workflow disclosed herein, which may include: (1) processing millions of Sequencing Read Archive (SRA) sequencing reads, (2) filtering metagenomic sequencing reads, (3) filtering viral-derived sequencing reads, (4) sequence-based alignment to identify parvovirus-derived sequencing reads, and (5) identifying AAV-derived sequencing reads. [Figure 2] FIG. 1 is a flowchart summarizing post-mortem analytical steps for characterization of putative AAV cap ORFs to confirm novelty. [Figure 3] Figure 1 shows a phylogenetic tree of known AAV clades. The computational pipeline disclosed herein identified 44 novel AAV capsids, a subset of which form three previously undiscovered putative AAV branches, as shown in the phylogenetic tree diagram. [Figure 4] FIG. 1 is a diagram of a phylogenetic tree showing a subset of 163 isolates of AAV viruses, highlighting nine contigs close to AAV2 (clade B) that were identified as complete and novel by the computational pipeline disclosed herein. [Figure 5] FIG. 1 is a diagram of computer system components that can be used to implement a method for detecting polynucleotide sequences encoding viral capsids in metagenomic data. [Figure 6] 1 is a multiple sequence alignment (MSA) showing the partial amino acid sequences of nine novel putative AAV cap ORFs identified by the computational methods and systems disclosed herein aligned to the AAV2 VP1 reference sequence. Heterologous amino acid residues are highlighted with gray bars. [Figure 6-1] Same as above. DETAILED DESCRIPTION OF THE INVENTION

[0016] Adeno-associated virus Adeno-associated viruses (AAVs) are small viruses that infect humans and several other primate species. They belong to the genus Dependoparvovirus, which belongs to the family Parvoviridae. They are small, replication-defective, non-enveloped viruses, approximately 26 nm in diameter, with a linear, single-stranded DNA (ssDNA) genome of approximately 4.8 kilobases (kb). The genome contains inverted terminal repeats (ITRs) at both ends of the DNA strand and two open reading frames (ORFs), rep and cap. The rep ORF consists of four overlapping genes encoding the Rep proteins required for the AAV life cycle. The latter contain overlapping nucleotide sequences of the capsid proteins: VP1, VP2, and VP3, which interact to form a capsid with icosahedral symmetry.

[0017] The 5' end of the AAV genome contains two promoters, designated p5 and p19, from which two overlapping messenger ribonucleic acid (mRNA) sequences of different lengths can be produced. Each of these contains an intron that can be either spliced ​​out or not. Given these possibilities, four different mRNAs and, therefore, four different Rep proteins with overlapping sequences can be synthesized. Their names, Rep78, Rep68, Rep52, and Rep40, represent their sizes in kilodaltons (kDa). Rep78 and 68 specifically bind to the hairpin formed by the ITRs in a self-priming process and can cleave a specific region within the hairpin, a designated terminal resolution site. Rep proteins are also required for AAVS1-specific integration of the AAV genome. All four Rep proteins bind ATP and possess helicase activity.

[0018] The right side of the positive-sense AAV genome encodes overlapping sequences for three capsid proteins, VP1, VP2, and VP3, which are driven by a single promoter called p40. The molecular weights of these proteins are 87, 72, and 62 kD, respectively. The AAV capsid has an empty mass of approximately 3.8 MDa and is composed of a mixture of a total of 60 VP1, VP2, and VP3 monomers arranged in an icosahedral symmetry in a 1:1:10 ratio. The cap gene produces an additional nonstructural protein called the assembly-activating protein (AAP). This protein is produced from ORF2 and is essential for the capsid assembly process.

[0019] All three VPs are translated from a single mRNA. After synthesis, this mRNA can be spliced ​​in two different ways, excising either a longer or a shorter intron, resulting in the formation of two pools of mRNAs: 2.3 kb and 2.6 kb in length. Typically, the longer intron is preferred, especially in the presence of adenovirus; therefore, the 2.3 kb mRNA represents the so-called "major splice." In this form, the first AUG codon, which initiates VP1 protein synthesis, is excised, reducing the overall level of VP1 protein synthesis. The first AUG codon remaining in the major splice is the initiation codon for the VP3 protein. However, upstream of that codon in the same open reading frame is an ACG sequence (encoding threonine) enclosed in an optimal Kozak context. This contributes to the low level of synthesis of the VP2 protein, which, like VP1, is actually the VP3 protein with an additional N-terminal residue.

[0020] AAV has unique features that make it attractive as a vector for delivering foreign DNA into cells, for example, in gene therapy. AAV infection of cells in culture is non-cytopathic, and natural infection in humans and other animals is silent and asymptomatic. Furthermore, AAV can infect many mammalian cell types, enabling it to target many different tissues in vivo. Furthermore, AAV can transduce slowly dividing and non-dividing cells and persist essentially throughout the lifespan of those cells as transcriptionally active nuclear episomes (extrachromosomal elements). The AAV proviral genome is infectious as cloned DNA in a plasmid, allowing the construction of recombinant genomes. Furthermore, because signals directing AAV replication, genome encapsidation, and integration are contained within the ITRs of the AAV genome, part or all of the internal approximately 4.3 kb of the genome (encoding replication and structural capsid proteins, rep-cap) can be replaced with foreign DNA, such as a gene cassette containing a promoter, DNA of interest, and a polyadenylation signal. The rep and cap proteins can be delivered in trans. Another important feature of AAV is that it is a very stable and potent virus. It can easily withstand the conditions used to inactivate adenovirus (56–65°C for several hours), making cold storage of AAV less important. AAV can even be lyophilized. Finally, AAV-infected cells are not resistant to superinfection.

[0021] Calculation Workflow Systems and methods are provided herein for detecting polynucleotide sequences encoding viral capsids in metagenomic data. In some embodiments, the systems and methods include a computational workflow for processing NGS data generated from sequencing a metagenomic sample. In some embodiments, the viral capsid is encoded by a sequence characteristic of Parvoviridae viruses. In some embodiments, the viral capsid is encoded by a sequence characteristic of AAV. In some embodiments, the computational workflow disclosed herein begins with a sequence alignment-based approach to screen metagenomic data including NGS sequencing reads for Parvoviridae polynucleotide sequence signatures. In some embodiments, the sequence alignment-based approach is used to select NGS reads that include Parvoviridae polynucleotide sequence signatures. In some embodiments, the selected NGS reads that identify Parvoviridae reads are further processed by a gene-based data processing module to discover known or heterologous viral sequences from user-input contigs using sensitive models of hallmark viral genes, such as the AAV cap gene. In some embodiments, after the viral gene-based data processing module is used, Parvoviridae contigs are prioritized in further post-analysis based on additional criteria, such as the presence of AAV hallmark genes and / or BLAST alignments to known AAV-like genes. In some embodiments, after prioritizing the Parvoviridae contigs, cap gene open reading frames (ORFs) are identified. In some embodiments, to identify AAV-like genomic sequences, cap open reading frames are selected based on criteria including, for example, the length of the open reading frame, the presence of a start codon, and the presence of a stop codon.In some embodiments, a phylogenetic tree analysis is performed to identify Parvoviridae contigs similar to previously discovered naturally isolated AAVs. In some embodiments, to determine the novelty of the identified candidate sequences, the candidate sequences are aligned using BLAST analysis against various databases of known AAV sequences. Examples of databases of known AAV sequences include the NCBI VIRUS database, the NCBI NR (non-redundant protein) database, and the NCBI PATAA (patented protein sequences) database. In some embodiments, after determining the novelty of the identified candidate sequences encoding novel AAV capsids, the sequences encoding the novel AAV capsids are curated based on the metadata of the sequence read archive (SRA) from which the novel AAV-encoding sequences were identified to remove artifacts or false positives.

[0022] Disclosed herein is a computational pipeline for identifying novel AAV capsids from a pool of candidate Parvoviridae contigs. The pipeline disclosed herein can include parameters including the length of the AAV capsid protein, a filter for extracting the entire ORF of the candidate contig, and a filter for retrieving only complete ORFs (starting with M (start codon, methionine) and ending with a stop codon) that have not previously been defined to specifically search for AAV capsids. In some embodiments, the pipeline further includes a BLAST search to determine whether novel putative AAV cap sequences are detected in either databases or patent databases. In some embodiments, the pipeline further includes aligning the candidate sequences encoding the complete AAV cap onto a phylogenetic tree of 160 known natural AAV isolates.

[0023] Sequence alignment In some embodiments, the computational methods disclosed herein include a sequence alignment-based module for screening metagenomic data containing NGS sequencing reads for Parvoviridae polynucleotide sequence signatures. In some embodiments, the computational pipeline begins with this sequence alignment-based approach. In some embodiments, this aspect of the computational method relies on mapping NGS sample reads to a reference panviral genome. In some embodiments, samples with any reads that map to an AAV genome / gene present are identified using a sequence alignment-based approach.

[0024] In some embodiments, the computational workflow begins with a sequence alignment-based module that screens metagenomic data containing NGS sequencing reads for Parvoviridae polynucleotide sequence signatures to pre-screen publicly available SRAs for NGS reads containing Parvoviridae sequences. This pre-screening step advantageously reduces computational time by reducing the number of metagenomic-derived NGS reads that need to be screened by downstream modules to identify novel AAV-related sequences. Downstream steps in the pipeline, such as gene-based similarity approaches, can be computationally intensive due to the de novo contig assembly step involved. This is a computationally intensive step for AAV-related sequences, and by definition, these viruses are relatively rare in metagenomic data, e.g., <0.2%, and therefore the frequency of novel, previously unannotated AAVs in metagenomic datasets is even rarer. For example, in a previously published Seratus study (Edgar RC, et al. Petabase-scale sequence alignment catalyses viral discovery. Nature. 2022 Feb;602(7895):142-147, which is incorporated herein by reference in its entirety), among the approximately 5,800,000 SRAs in a publicly available SRA dataset, approximately 9,000 SRAs consisted of Parvoviridae sequence reads (Seratus scores between 20 and 100, with 100 being the maximum), or approximately 0.15% of the reads. As another example, Hsu et al. profiled approximately 800 human tissue biopsies for long-read sequencing and discovered one novel AAV-associated sequence, or approximately 0.125%. These previously published results suggest that detecting AAV using only a de novo contig assembly pipeline of random SRA pools would require screening millions of SRAs, potentially resulting in prohibitively high computational costs.

[0025] In some embodiments, the Serratus cloud computing infrastructure is used to screen metagenomic data containing NGS sequencing reads for Parvoviridae polynucleotide sequence signatures (Edgar RC, et al. Petabase-scale sequence alignment catalyses viral discovery. Nature. 2022 Feb;602(7895):142-147, which is incorporated herein by reference in its entirety. Serratus is a bioinformatics platform that screens sequences from the NCBI SRA (National Center for Biotechnology Information Sequence Read Archive) for similarity to known viruses (an alignment-based approach). The published Serratus project analyzed 5.7 million biologically diverse samples (10.2 petabase). In some embodiments, to reduce the test sample size for the computational pipelines disclosed herein, computational tools, such as Serratus, are used to extract samples in which Parvoviridae reads are identified in publicly available large-scale metagenomic data.

[0026] Gene-based similarity approach In some embodiments, the computational methods disclosed herein further include a gene-based data processing module that uses a sensitive model of Hallmark viral genes, e.g., the AAVCAP gene, to discover known or heterologous viral sequences from user-input contigs. For example, contigs containing NGS reads in which Parvoviridae reads are identified by a sequence alignment-based module can be further processed by the gene-based data processing module to identify and select NGS reads that are likely to contain novel AAV capsid genes. In some embodiments, the gene-based data processing module is Cenote-Taker2 (Tisza MJ, et al. Cenote-Taker 2 democratizes virus discovery and sequence annotation. Virus Evol. 2020 Dec 30;7(1):veaa100, incorporated herein by reference in its entirety). Cenote-Taker2 is a virus discovery and annotation tool, i.e., an assembly-based approach, that uses a sensitive model of Hallmark viral genes to discover known or heterologous viral sequences from user-input contigs. In some embodiments, to identify all possible viruses among metagenomic NGS reads, Cenote-Taker2 is used to identify and select NGS reads that contain genes encoding AAV capsids.

[0027] In some embodiments, the functionality and parameters of a gene-based data processing module, such as Cenote-Taker2, are customized to facilitate the identification of NGS reads containing AAV capsid-encoding genes and to increase computational efficiency. In some embodiments, these parameters may include, for example, a minimum linear contig length, a minimum circular contig length, and a minimum number of hallmark genes.

[0028] The length of the complete AAV2 wild-type genome (GenBank accession: AF043303.1) is approximately 4.7 kbp. The complete VP1, the longest cap ORF, is 2205 bp. By default, the Cenote-Taker2 code available on GitHub sets both parameters (minimum linear contig length and minimum circular contig length) to 1000 bp. This setting results in the identification and abundance of multiple short contigs containing sequences encoding phage, non-parvovirus, or incomplete AAV cap genes. Including these unnecessary results significantly increases computational time and computational cost.

[0029] In some embodiments, to reduce computation time and increase computational efficiency in the gene-based data processing module of a computational pipeline for identifying NGS reads containing AAV capsid-encoding genes, the minimum circular contig length is set to 2000 bp, 2500 bp, 3000 bp, 3500 bp, 4000 bp, 4500 bp, or 5000 bp. In some embodiments, the minimum circular contig length is set to 2000 bp, 2500 bp, 3000 bp, 3500 bp, 4000 bp, 4500 bp, or 5000 bp, while the minimum linear contig length is maintained at 1500 bp. In a preferred embodiment, the minimum circular contig length is set to 4000 bp, the minimum linear contig length is set to 1500 bp, and the number of hallmark genes is set to 0. These parameters improve the efficiency of eliminating contigs containing phage sequences and sequences encoding incomplete AAV capsid genes, reducing computational time and cost. By removing multiple short contigs containing phage sequences, non-parvovirus sequences, or sequences encoding incomplete AAVcap genes, computational resources can be allocated to post-hoc analysis of identified sequences encoding complete AAVcap genes.

[0030] In some embodiments, additional criteria are utilized to identify contigs containing NGS reads representing genes encoding novel AAV capsids from the output of the gene-based data processing module. The criteria may include, for example, the presence of associated hallmark genes. In some embodiments, the associated hallmark genes for identifying genes encoding novel AAV capsids from the output of the gene-based data processing module may include long cap (VP1), short cap (VP2 or 3), or nonstructural AAV proteins. In some embodiments, the nonstructural AAV proteins may include PDB:3KIC_N-Capsid-Proteins-Parvo and / or CDD:pfam00740-Minor-Capsid-Proteins.

[0031] In some embodiments, as a component of the gene-based data processing module, BLAST alignment is used to match putative contigs to AAV-like genes using publicly available viral reference databases. In some embodiments, putative contigs are aligned against a database of reference AAV cap ORFs using BLAST top amino acid sequence similarity to proteins annotated with the "adeno-associated" description. This BLAST alignment step can advantageously increase the specificity of the gene-based data processing module for identifying novel AAV capsid-encoding genes among contigs that may contain genes encoding capsid sequences from other Parvoviridae species. In addition, some contigs may contain putative AAV cap ORFs that do not closely match genes in previously designated AAV cap hallmarks, but BLAST alignment with customized parameters can identify similarities with AAV cap hallmark genes.

[0032] In some embodiments, the gene-based data processing module, e.g., Cenote-Taker2, produces an intermediate file and a final contig summary file. Based on the intermediate file, annotations and sequences of the contigs in the summary file can be extracted. In some embodiments, the step of extracting the putative AAV cap ORF includes: (1) obtaining a contig list from the final contig summary file output by the gene-based data processing module; (2) obtaining annotations of the contig list from step (1), where the annotations may include genomic coordinates, protein ID numbers, and protein product names of the translated ORFs. In some embodiments, based on a string match of "cap / cap" in the annotation file, the protein product name and genomic coordinates of the translated putative cap ORF are extracted; and (3) obtaining the polypeptide sequence of the putative AAV cap ORF.

[0033] Post-hoc analysis: Identification of a novel complete AAV cap In some embodiments, the computational workflow includes prioritizing contigs containing complete putative AAV cap ORFs and further post-hoc characterization of the putative AAV cap ORFs to confirm novelty. A flowchart summarizing these post-hoc analysis steps is shown in Figure 2. Factors contributing to the prioritization of contigs containing complete putative AAV cap ORFs may include the presence of previously annotated AAV hallmark genes. Previously annotated AAV hallmark genes may include, for example, PDB:3KIC_N-Capsid-Protein-Parvo and / or CDD:pfam00740-Minor-Capsid-Protein. Additional factors contributing to contig prioritization may include the length of the putative AAV cap ORF. For example, putative ORFs can be extracted by filtering for ORFs longer than 500, 550, 600, 650, or 700 amino acids. In a preferred embodiment, putative ORFs longer than 685 amino acids are extracted. In some embodiments, the putative ORFs are filtered for ORFs that begin with a start codon, i.e., a codon encoding a methionine amino acid residue. In some embodiments, the putative ORFs are filtered for ORFs that end with a stop codon.

[0034] In some embodiments, factors for assessing the novelty of an identified putative AAV cap ORF include phylogenetic tree analysis. In some embodiments, the phylogenetic tree analysis includes previously annotated natural AAV isolates and / or previously discovered Parvoviridae amino acid sequences. In some embodiments, factors for assessing the novelty of an identified AAV sequence include BLAST alignment analysis against previously annotated natural AAV isolates and / or previously discovered Parvoviridae amino acid sequences. In some embodiments, the BLAST analysis includes the NCBI virus database; the NCBI RefSeq Non-Redundant Protein database; and / or the NCBI PATAA patent protein sequence database.

[0035] Computer implementation of the method 5 is a diagram of computer system 500 components that can be used to implement a sequence alignment-based module, a genetic-based data processing module, and / or prioritization of contigs containing complete putative AAV cap ORFs and further post-hoc characterization of the putative AAV cap ORFs to confirm novelty. The components of computer system 500 can perform the steps of (1) a sequence alignment-based module, (2) a genetic-based data processing module, and / or (3) prioritization of contigs containing complete putative AAV cap ORFs and further post-hoc characterization of the putative AAV cap ORFs to confirm novelty separately, sequentially, or in combination.

[0036] Computing device 500 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Computing device 550 is intended to represent various forms of mobile devices, such as personal digital assistants, mobile phones, smartphones, and other similar computing devices. Additionally, computing device 500 or 550 may include a universal serial bus (USB) flash drive. The USB flash drive may store an operating system and other applications. The USB flash drive may include input / output components, such as a wireless transmitter or a USB connector that can be inserted into a USB port of another computing device. The components, their connections and relationships, and their functions illustrated herein are intended to be exemplary only and are not intended to limit the implementation of the methods and configurations described and / or claimed herein.

[0037] The computing device 500 includes a processor 502, memory 504, a storage device 506, a high-speed interface 508 connecting to the memory 504 and a high-speed expansion port 510, a low-speed bus 514, and a low-speed interface 512 connecting to the storage device 506. The components 502, 504, 506, 508, 510, and 512 are interconnected using various buses, may be implemented on a common motherboard, or in other suitable manners. The processor 502 processes instructions for execution within the computing device 500, including instructions stored in the memory 504 or the storage device 506, and can display graphical information for a GUI on an external input / output device, such as a display 516 coupled to the high-speed interface 508. Other implementations may use multiple processors and / or multiple buses, along with multiple memories and types of memory, as appropriate. Multiple computing devices 500 may also be connected, each providing a portion of the necessary operations, for example, as a server bank, a cluster of blade servers, or a multiprocessor system.

[0038] Memory 504 stores information internal to computing device 500. In one implementation, memory 504 is one or more volatile memory units. In another embodiment, memory 504 is one or more non-volatile memory units. Memory 504 may also be another form of computer-readable medium, such as a magnetic or optical disk.

[0039] The storage device 506 can provide mass storage for the computing device 500. In one embodiment, the storage device 506 can be or include a computer-readable medium such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid-state memory device, or an array of devices including devices in a storage area network or other configuration. A computer program product can be tangibly embodied on an information carrier. The computer program product can also include instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as memory 504, the storage device 506, or memory on the processor 502.

[0040] The high-speed controller 508 manages bandwidth-intensive operations of the computing device 500, while the low-speed controller 512 manages less bandwidth-intensive operations. This division of functions is merely exemplary. In one embodiment, the high-speed controller 508 is coupled to the memory 504, the display 516, e.g., a graphics processor or accelerator, and to a high-speed expansion port 510 that can accept various expansion cards (not shown). In this embodiment, the low-speed controller 512 is coupled to the storage device 506 and the low-speed expansion port 514. The low-speed expansion port may include various communication ports, such as USB, Bluetooth, Ethernet, wireless Ethernet, etc., but may also be coupled to one or more input / output devices, such as a keyboard, a pointing device, a microphone / speaker pair, a scanner, or a networking device, such as a switch or router, via a network adapter. The computing device 500, as shown, may be implemented in several different forms. For example, it may be implemented as a standard server 520 or multiple times in a group of such servers. It may also be implemented as part of a rack server system 524. Furthermore, it may be implemented in a personal computer such as a laptop computer 522. Alternatively, components from computing device 500 may be combined with other components in a mobile device (not shown), such as device 550. Each such device may include one or more computing devices 500, 550, and the overall system may consist of multiple computing devices 500, 550 in communication with each other.

[0041] Computing device 500, as shown, may be implemented in several different forms. For example, it may be implemented as a standard server 520 or multiple times in a group of such servers. It may also be implemented as part of a rack server system 524. Furthermore, it may be implemented in a personal computer, such as a laptop computer 522. Alternatively, components from computing device 500 may be combined with other components in a mobile device (not shown), such as device 550. Each such device may include one or more computing devices 500, 550, and the entire system may be made up of multiple computing devices 500, 550 communicating with each other.

[0042] Computing device 550 includes, among other components, a processor 552, memory 564, input / output devices such as a display 554, a communications interface 566, and a transceiver 568. Device 550 may be provided with a storage device such as a microdrive or other device to provide additional storage. Each of the components 550, 552, 564, 554, 566, 568 are interconnected using various buses, and some of the components may be implemented on a common motherboard or in any other suitable manner.

[0043] Processor 552 can execute instructions within computing device 550, including instructions stored in memory 564. The processor can be implemented as a chipset of chips including separate analog and digital processors. Additionally, the processor can be implemented using any of a number of architectures. For example, processor 510 can be a CISC (Complex Instruction Set Computer) processor, a RISC (Reduced Instruction Set Computer) processor, or a MISC (Minimum Instruction Set Computer) processor. The processor can coordinate other components of device 550, such as controlling a user interface, applications run by device 550, and wireless communication by device 550.

[0044] Processor 552 can communicate with a user via control interface 558 and display interface 556 coupled to display 554. Display 554 can be, for example, a TFT (thin film transistor liquid crystal display) display, an OLED (organic light emitting diode) display, or other suitable display technology. Display interface 556 can include appropriate circuitry for driving display 554 to present graphics and other information to the user. Control interface 558 can receive commands from the user and convert them for submission to processor 552. Additionally, an external interface 562 can be in communication with processor 552 to enable short-range communication between device 550 and other devices. External interface 562 can provide, for example, for wired communication in some implementations or wireless communication in other implementations; multiple interfaces can also be used.

[0045] Memory 564 stores information internal to computing device 550. Memory 564 may be implemented as one or more of a computer-readable medium, a volatile memory unit, or a non-volatile memory unit. Expansion memory 574 may also be provided and connected to device 550 via expansion interface 572, which may include, for example, a SIMM (single in-line memory module) card interface. Such expansion memory 574 may provide additional storage space for device 550 or may store applications or other information for device 550. Specifically, expansion memory 574 may include instructions for implementing or supplementing the processes described above, and may also include secure information. Thus, for example, expansion memory 574 may be provided as a security module for device 550 and programmed with instructions that enable secure use of device 550. Additionally, secure applications may be provided via SIMM cards, along with additional information, such as placing identifying information on the SIMM card in an unhackable manner.

[0046] The memory may include, for example, flash memory and / or NVRAM memory, as described below. In one embodiment, a computer program product is tangibly embodied on an information carrier. The computer program product includes instructions that, when executed, perform one or more methods, such as those described above. The information carrier is, for example, a computer-readable or machine-readable medium, such as memory 564, expansion memory 574, or memory on processor 552, receivable via transceiver 568 or external interface 562.

[0047] Device 550 can communicate wirelessly via communication interface 566, which may include digital signal processing circuitry as needed. Communication interface 566 can provide for communication in various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS. Such communication can occur, for example, via radio frequency transceiver 568. Additionally, short-range communication can occur, such as using Bluetooth, Wi-Fi, or other such transceivers (not shown). Additionally, GPS (Global Positioning System) receiver module 570 can provide additional navigation- and location-related wireless data to device 550, which can be used as appropriate by applications executing on device 550.

[0048] Device 550 can also communicate voice using audio codec 560, which can receive voice information from a user and convert it into usable digital information. Audio codec 560 can also generate audible sounds for the user, for example, through a speaker in the handset of device 550. Such sounds can include sounds from a voice call, recorded sounds such as voice messages, music files, and the like, and can also include sounds generated by applications running on device 550.

[0049] The computing device 550, as shown, can be implemented in many different forms, for example, as a mobile phone 580. It can also be implemented as part of a smartphone 582, personal digital assistant, or other similar mobile device.

[0050] Various implementations of the systems and methods described herein may be realized in digital electronic circuitry, integrated circuits, specially designed ASICs (application-specific integrated circuits), computer hardware, firmware, software, and / or combinations of such implementations. These various implementations may include implementation in one or more computer programs executable and / or interpretable by a programmable system including at least one programmable processor, which may be special purpose or general purpose, coupled to receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0051] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and may be implemented in a high-level procedural and / or object-oriented programming language and / or assembly / machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal, such as a magnetic disk, optical disk, memory, or programmable logic device (PLD). The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0052] To provide for user interaction, the systems and techniques described herein can be implemented in a computer having a display device, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to a user, and a keyboard and pointing device, such as a mouse or trackball, through which the user can provide input to the computer. Other types of devices can be used to provide for user interaction as well; for example, feedback provided to the user can be any form of sensory feedback, such as visual, auditory, or tactile feedback, and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0053] The systems and techniques described herein may be implemented in a computing system that includes back-end components, such as data servers, middleware components, e.g., application servers, or front-end components, such as client computers having a graphical user interface or web browser that allows users to interact with implementations of the systems and techniques described herein, or any combination of such back-end, middleware, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication, e.g., a communications network. Examples of communications networks include a local area network ("LAN"), a wide area network ("WAN"), and the Internet.

[0054] A computing system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0055] A number of embodiments have been described. Nevertheless, it will be understood that various modifications are possible without departing from the spirit and scope of the present invention. Additionally, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. Additionally, other steps may be provided from the described flows or steps may be deleted from the described flows, and other components may be added to or removed from the described systems. Accordingly, other embodiments are within the scope of the following claims.

[0056] All of the embodiments and functional operations of the present disclosure described herein may be implemented in digital electronic circuitry, or computer software, firmware, or hardware, including the structures disclosed herein and their structural equivalents, or in one or more combinations thereof. Embodiments of the methods and compositions may be implemented as one or more computer program products, e.g., one or more modules of computer program instructions encoded on a computer-readable medium for execution by or to control the operation of a data processing apparatus. The computer-readable medium may be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter providing a machine-readable propagated signal, or one or more combinations thereof. The term "data processing apparatus" encompasses all apparatuses, devices, and machines for processing data, including, by way of example, a programmable processor, a computer, or multiple processors or computers. In addition to hardware, an apparatus may include code that creates an execution environment for the computer program in question, such as code constituting processor firmware, a protocol stack, a database management system, an operating system, or one or more combinations thereof. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal generated to encode information for transmission to an appropriate receiver apparatus.

[0057] A computer program (also known as a program, software, software application, script, or code) may be written in any type of programming language, including compiled or interpreted languages, and may be deployed in any form, such as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program may be stored as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple associated files (e.g., files storing one or more modules, subprograms, or portions of code). A computer program may be deployed to be executed on one computer or to be executed on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communications network.

[0058] The processes and logic flows described herein may be performed by one or more programmable processors that execute one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows may also be performed by, and apparatus may be implemented as, special purpose logic circuitry, such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0059] Processors suitable for executing a computer program include, by way of example, both general-purpose and special-purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor receives instructions and data from a read-only memory or a random-access memory, or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will include one or more mass storage devices, such as magnetic, magneto-optical, or optical disks, for storing data, or will be operatively coupled to receive data from or transfer data to them. However, a computer need not have such devices. Furthermore, a computer can be incorporated into another device, such as a tablet computer, a mobile phone, a personal digital assistant (PDA), a portable audio player, or a global positioning system (GPS) receiver, to name a few. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, by way of example, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0060] To provide for user interaction, embodiments of the present disclosure can be implemented on a computer having a display device, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user, and a keyboard and pointing device, such as a mouse or trackball, by which the user can provide input to the computer. Other types of devices can be used to provide for user interaction as well; for example, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0061] Embodiments of the present disclosure may be implemented in a computing system including back-end components, e.g., a data server or middleware components, a computing system including an application server, or a front-end component, e.g., a computing system including a client computer having a graphical user interface or web browser through which a user can interact with method embodiments, or any combination of one or more such back-end, middleware, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication, e.g., a communications network. Examples of communications networks include a local area network ("LAN"), a wide area network ("WAN") such as the Internet, or the like.

[0062] A computing system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0063] While this specification contains many details, these should not be construed as limitations on the scope of the invention or what may be claimed, but rather as descriptions of features specific to particular embodiments of the invention. Features described herein in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable subcombination. Furthermore, while features may be described above as acting in a particular combination, and even initially claimed as such, one or more features from a claimed combination may, in some cases, be carved out of that combination, and the claimed combination may be directed to a subcombination or a variation of the subcombination.

[0064] Similarly, while operations are shown in the figures in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown, or in any sequential order, or that all of the illustrated operations be performed, to achieve desirable results. In certain situations, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems may generally be integrated into a single software product or packaged into multiple software products.

[0065] In each instance where an HTML file is mentioned, other file types or formats can be substituted. For example, the HTML file can be replaced with an XML, JSON, plain text, or other type of file. Additionally, where a table or hash table is mentioned, other data structures (such as a spreadsheet, relational database, or structured file) can be used. [Example]

[0066] The present invention is further described in the following examples, which do not limit the scope of the invention described in the claims.

[0067] Example 1: Mining metagenomic data for novel AAV capsids identifies 44 novel AAV capsids and 3 putative undiscovered AAV clades To demonstrate the effectiveness of the computational pipeline disclosed herein, which includes a sequence alignment-based module, a gene-based data processing module, and further post-hoc characterization of putative AAV cap ORFs to confirm novelty, the computational pipeline was run on the NCBI Sequence Read Archive (SRA) dataset. First, the sequence alignment-based module was used to quantify SRA reads that mapped to known virus genomes. This step utilized the Serratus cloud computing infrastructure. This step effectively detected the presence / absence of known viruses in the SRA dataset based on the number of mapped reads. In this way, 5.8 million SRA samples were screened to extract approximately 9,000 SRA samples containing at least some reads that mapped to Parvoviridae genomes.

[0068] Next, a gene-based data processing module was used to assemble contigs from the 9000 SRA samples. The Cenote-Taker2 program was used to exclude viral contigs and filter viral contig identities based on the gene mapping of the hallmarks described above.

[0069] Finally, to confirm the novelty of contigs containing putative AAV cap ORFs, further post-hoc characterization of the putative AAV cap ORFs was performed. In this step, the cap ORFs were translated, contigs were filtered for complete cap ORFs, BLAST alignment sequence similarity searches were performed against any known patented AAV capsid sequences using the NCBI database, and phylogenetic analysis was performed to place novel putative AAV cap ORFs among AAV isolates in a phylogenetic tree.

[0070] A phylogenetic tree of known AAV clades is shown in Figure 3. A computational pipeline run on millions of SRA reads identified 44 novel AAV capsids, a subset of which form three previously undiscovered putative AAV branches.

[0071] More specifically, Figure 4 shows a subset of the phylogenetic tree of 163 natural isolates of AAV. As shown in Figure 4, nine contigs (clade B) close to AAV2 were identified as complete and novel by the computational pipeline. Multiple sequence alignment (MSA) of these novel AAV cap ORFs, which share some similarity with AAV2, indicates that the majority of amino acid differences between the novel AAV cap ORFs and AAV2 cap reside in variable regions and are therefore unlikely to be sequencing artifacts and may have functional effects on the AAV capsid. Variable regions within the capsid protein sequence define AAV serotypes and tropism by altering the binding interactions between capsids and receptors that facilitate their cellular uptake. As shown in Figure 6, gray bars highlight the amino acid residues in the nine novel AAV cap ORFs that differ compared to the AAV2 VP1 reference sequence. These results demonstrate that the computational pipeline disclosed herein can successfully identify polynucleotide sequences encoding novel AAV capsids in petabytes of previously unexplored metagenomic data.

[0072] As demonstrated by the novel putative AAV cap ORFs identified by the computational methods and systems disclosed herein, petabytes of publicly available metagenomic datasets can be mined to identify previously undiscovered putative AAV capsids from various environmental samples. Using the computational pipeline disclosed herein, 44 novel putative AAV capsids were identified in a short period of time. Previously, compiling a list of 160 naturally occurring AAV isolates required over 60 years of research. The novel AAV capsids identified by the computational pipeline disclosed herein expand the current repertoire of AAV isolates available for clinically useful human gene therapy by approximately 30%.

[0073] Other embodiments While the present invention has been described with reference to its detailed description, it will be understood that the foregoing description is for illustrative purposes only and is not intended to limit the scope of the invention as defined by the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

1. 1. A method for identifying novel viral polypeptide sequences by screening polynucleotide sequencing data, comprising: providing polynucleotide sequencing data comprising a plurality of sequencing reads; aligning each of the plurality of sequencing reads to one or more reference sequences; assembling a plurality of sequence contigs from the plurality of sequencing reads aligned to the one or more reference sequences; selecting one or more contigs containing viral open reading frame (ORF) sequences based on the one or more reference sequences; and identifying one or more novel viral polypeptide sequences based on the translated polypeptide sequences of said viral ORFs; A method comprising:

2. 2. The method of claim 1, wherein the one or more reference sequences are viral reference sequences.

3. 3. The method of claim 2, wherein the one or more viral reference sequences are Parvoviridae reference sequences.

4. The method of any one of claims 2 to 3, wherein the one or more viral reference sequences are adeno-associated virus (AAV) reference sequences.

5. 5. The method of claim 4, wherein the AAV reference sequence is a sequence encoding an AAV cap protein.

6. The method of any one of claims 1 to 5, wherein the polynucleotide sequencing data is metagenomic data.

7. 7. The method of claim 1, wherein the plurality of sequencing reads are generated by next generation sequencing (NGS).

8. The method according to any one of claims 1 to 7, wherein the selection step further comprises defining a minimum circular contig length.

9. 9. The method of claim 8, wherein the minimum circular contig length is 4000 bp.

10. The method according to any one of claims 1 to 9, wherein the selection step further comprises defining a minimum linear contig length.

11. 11. The method of claim 10, wherein the minimum linear contig length is 1500 bp.

12. 12. The method of any one of claims 8 to 11, wherein the minimum circular contig length is 4000 bp and the minimum linear contig length is 1500 bp.

13. The method of any one of claims 1 to 12, wherein the identifying step further comprises defining a minimum ORF length.

14. 14. The method of claim 13, wherein the minimum ORF length is 685 amino acids.

15. The method of any one of claims 1 to 14, wherein the identifying step further comprises a BLAST alignment analysis.

16. 16. The method of claim 15, wherein the BLAST alignment analysis is performed against previously annotated natural AAV isolates and / or previously discovered Parvoviridae amino acid sequences.

17. A computer program product tangibly embodied in a computer-readable medium, comprising instructions that, when executed by one or more processors, receiving polynucleotide sequencing data comprising a plurality of sequencing reads; aligning each of the plurality of sequencing reads to one or more reference sequences; assembling a plurality of sequence contigs from the plurality of sequencing reads aligned to the one or more reference sequences; selecting one or more contigs containing viral open reading frame (ORF) sequences based on the one or more reference sequences; identifying one or more novel viral polypeptide sequences based on the translated polypeptide sequences of said viral ORFs; A computer program product configured to:

18. 18. The computer program product of claim 17, wherein the one or more reference sequences are viral reference sequences.

19. 20. The computer program product of claim 18, wherein the one or more viral references are Parvoviridae sequences.

20. 20. The computer program product of any one of claims 18 to 19, wherein the viral reference sequence is an adeno-associated virus (AAV) reference sequence.

21. 21. The computer program product of claim 20, wherein the AAV reference sequence is a sequence encoding an AAV cap protein.

22. 22. The computer program product of any one of claims 17 to 21, wherein the polynucleotide sequencing data is metagenomic data.

23. 23. The computer program product of any one of claims 17 to 22, wherein the plurality of sequencing reads are generated by next generation sequencing (NGS).

24. 24. The computer program product of claim 17, wherein the selecting step further comprises defining a minimum circular contig length.

25. 25. The computer program product of claim 24, wherein the minimum circular contig length is 4000 bp.

26. 26. The computer program product of any one of claims 17 to 25, wherein the selecting step further comprises defining a minimum linear contig length.

27. 27. The computer program product of claim 26, wherein the minimum linear contig length is 1500 bp.

28. 28. The computer program product of any one of claims 24 to 27, wherein the length of the smallest circular contig is 4000 bp and the length of the smallest linear contig is 1500 bp.

29. 29. The computer program product of any one of claims 17 to 28, wherein the identifying step further comprises defining a minimum ORF length.

30. 30. The computer program product of claim 29, wherein the minimum ORF length is 685 amino acids.

31. The computer program product of any one of claims 17 to 30, wherein the identifying step further comprises a BLAST alignment analysis.

32. 32. The computer program product of claim 31 , wherein the BLAST alignment analysis is performed against previously annotated natural AAV isolates and / or previously discovered Parvoviridae amino acid sequences.

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