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A High-Throughput Method for Accurate Fast QC of Plasmids

IP.com Disclosure Number: IPCOM000245347D
Publication Date: 2016-Mar-02
Document File: 4 page(s) / 392K

Publishing Venue

The IP.com Prior Art Database

Abstract

The construction process of generating vectors and plasmids can introduce mutations, rearrangements, insertions and deletions. Propagation of the precise, designed information contained in a vector is critical to ensure the provenance of genetic information and to allow downstream use of vectors meeting certain quality requirements, e.g. integrity and identity criteria. Vectors are designed to express RNA(s) or protein(s) for research and/or commercial purposes. Single nucleotide and, in some cases, structural variations can go undetected using standard plasmid quality control methods such as restriction digestions or Sanger sequencing of only a partial region of the vector. These variations can significantly negatively impact the expression or function of the expressed RNA or protein leading to failed research experiments or commercial production of the intended vector product(s). These failures often incur significant loss of time and money. Vector quality control eliminates these risks. The steps of a Vector Quality Control (Vector QC or VQC) process includes generating DNA sequence data from a next-generation sequencing library and identifying variations between a vector reference map and the sequenced vector. Identified variations are assigned a score based on the nature and severity of the variation. The vector's sequence map is updated to show the variations and the plasmid has been verified. An overview of a vector quality control process is shown in Figure 1. As mentioned herein, VQC uses next-generation DNA sequencing technology to sequence plasmids from a next-generation sequencing library constructed using routine techniques, such as those available from Illumina, Ion Torrent™, Pacific Biosciences® and Oxford Nanopore-based technologies. DNA sequence data is aligned to a vector's reference sequence – map or blueprint - to identify potential deviations or rearrangements from the intended design prior to plasmid use, including but not limited to use of vectors in transformation or expression studies. Identified variations are scored based on position (coding or regulatory vs. noncoding or vector backbone sequences) within the plasmid and sequence variant types, such as single-base, di- or polynucleotide substitutions, (e.g., premature stop, missense mutations) insertions or deletions. Parameters involved in detecting and scoring variants include DNA sequence read coverage, purity scores and location of variants within the plasmid. Any suitable algorithm that aligns next generation DNA sequence reads may be used to filter or identify vector sequences. Examples of suitable algorithms include but are not limited to Bowtie, GSNAP and BWA. Vector sequences include but are not limited to transformation vectors including but not limited to viral vectors, such as adenovirus, adeno-associated virus (AAV), and lentivirus vectors, plant transformation vectors such as binary plant transformation vectors, Agrobacterium-mediated transformation vectors and plasmids used in microparticle bombardment or electroporation transformation methods and expression cassettes used for the production of RNA or protein in eukaryotic and prokaryotic systems. Any algorithm that can align DNA sequences reads to the plasmid reference map may be used in the VQC approach described herein. Exemplary algorithms include but are not limited to GSNAP, Bowtie, and BWA. Variant calling requires a minimum coverage of sequence reads at any given position. The DNA sequence read coverage, the location(s) of potential variations across the plasmid and the identity of these variations can be viewed on a web-interface display, such as that produced using TITAN®. The output can provide details for each variant identified (type, position, score) for each sample. Plasmid sequence maps can be updated to reflect variants. In addition or as an alternative, new plasmid isolates can be re-submitted according to the type and location of the variant, for example, showing if the variant occurs in a regulatory region, coding region, or non-coding region within the vector sequence elements. The VQC method can be used with any next-generation DNA sequencing technologies, for example, with Illumina, Ion Torrent™, Pacific Biosciences® and Oxford Nanopore-based technologies in turn making the method high throughput. While the laboratory steps can be performed manually, Vector QC laboratory operations, including DNA preparation and library construction, are intended to be performed using laboratory automation. Vector QC results including coverage graphs, variant-type, -location, and -score can be made available through a web-based display (Figure 2.).   Figure 1 – Vector Quality Control Workflow  

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A High-Throughput Method for Accurate Fast QC of Plasmids

The construction process of generating vectors and plasmids can introduce mutations, rearrangements, insertions and deletions. Propagation of the precise, designed information contained in a vector is critical to ensure the provenance of genetic information and to allow downstream use of vectors meeting certain quality requirements, e.g. integrity and identity criteria. Vectors are designed to express RNA(s) or protein(s) for research and/or commercial purposes. Single nucleotide and, in some cases, structural variations can go undetected using standard plasmid quality control methods such as restriction digestions or Sanger sequencing of only a partial region of the vector. These variations can significantly negatively impact the expression or function of the expressed RNA or protein leading to failed research experiments or commercial production of the intended vector product(s). These failures often incur significant loss of time and money. Vector quality control eliminates these risks.

The steps of a Vector Quality Control (Vector QC or VQC) process includes generating DNA sequence data from a next-generation sequencing library and identifying variations between a vector reference map and the sequenced vector. Identified variations are assigned a score based on the nature and severity of the variation. The vector's sequence map is updated to show the variations and the plasmid has been verified. An overview of a vector quality control process is shown in Figure 1.

As mentioned herein, VQC uses next-generation DNA sequencing technology to sequence plasmids from a next-generation sequencing library constructed using routine techniques, such as those available from Illumina, Ion Torrent™, Pacific Biosciences® and Oxford Nanopore-based technologies. DNA sequence data is aligned to a vector's reference sequence - map or blueprint - to identify potential deviations or rearrangements from the intended design prior to plasmid use, including but not limited to use of vectors in transformation or expression studies. Identified variations are scored based on position (coding or regulatory vs. noncoding or vector backbone sequences) within the plasmid and sequence variant types, such as single-base, di- or polynucleotide substitutions, (e.g., premature stop, missense mutations) insertions or deletions. Parameters involved in detecting and scoring variants include DNA sequence read coverage, purity scores and location of variants within the plasmid.

Any suitable algorithm t...