PATENT CLAIM ANALYSIS

Application Number: 16411045
Application Type: Utility
Filing Date: 2019-05
Publication Date: 2019-09
Patent Classification: ["435", "006120"]

Abstract:
Next Generation DNA sequencing promises to revolutionize clinical medicine and basic research. However, while this technology has the capacity to generate hundreds of billions of nucleotides of DNA sequence in a single experiment, the error rate of approximately 1% results in hundreds of millions of sequencing mistakes. These scattered errors can be tolerated in some applications but become extremely problematic when “deep sequencing” genetically heterogeneous mixtures, such as tumors or mixed microbial populations. To overcome limitations in sequencing accuracy, a method Duplex Consensus Sequencing (DCS) is provided. This approach greatly reduces errors by independently tagging and sequencing each of the two strands of a DNA duplex. As the two strands are complementary, true mutations are found at the same position in both strands. In contrast, PCR or sequencing errors will result in errors in only one strand. This method uniquely capitalizes on the redundant information stored in double-stranded DNA, thus overcoming technical limitations of prior methods utilizing data from only one of the two strands.

Claim (Index 37):
A method for quantifying single nucleotide variant cancer biomarkers in circulating nucleic acid from a subject, comprising:\n (a) providing a plurality of circulating nucleic acid molecules obtained from a bodily sample of the subject; (b) attaching tags comprising barcodes selected from a plurality of distinct barcode sequences to said circulating nucleic acid molecules obtained from said bodily sample of the subject, to generate non-uniquely tagged parent polynucleotides, wherein each non-uniquely tagged parent polynucleotide is substantially unique with respect to other non-uniquely tagged parent polynucleotides in the bodily sample; (c) amplifying the non-uniquely tagged parent polynucleotides to produce amplified non-uniquely tagged progeny polynucleotides; (d) sequencing the amplified non-uniquely tagged progeny polynucleotides to produce a plurality of sequence reads from each non-uniquely tagged parent polynucleotide, wherein each sequence read comprises a barcode sequence and a sequence derived from a circulating nucleic acid molecule; (e) grouping the plurality of sequence reads produced from each non-uniquely tagged parent polynucleotide into families based on i) the barcode sequence and ii) sequence information derived from the circulating nucleic acid molecule, whereby each family comprises sequence reads of non-uniquely tagged progeny polynucleotides amplified from a unique polynucleotide among the non-uniquely tagged parent polynucleotides; (f) comparing the sequence reads grouped within each family to each other to determine consensus sequences for each family, wherein each of the consensus sequences corresponds to a unique polynucleotide among the non-uniquely tagged parent polynucleotides; (g) providing a reference sequence, said reference sequence comprising one or more loci; (h) identifying consensus sequences that map to a given locus of said one or more loci; and (i) calculating a number of consensus sequences that map to the given locus that include a cancer-associated single nucleotide variant thereby quantifying single nucleotide variant cancer biomarkers in said circulating nucleic acid from said subject.

Metadata:
- Claim Count in Document: 89.0
- Percentile: 100.0
- Lexical Diversity: 1.51639
- Patent Class: 435.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16411066', '16120019', '16120091', '15660785', '16120072']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.7216957417522036
- 35 USC 102 Novelty (BERT): 0.6825745671411583
- Combined Prediction Score: 0.7177836242910992
- Mean Citation Score: 711.6731560000003
- Max Citation Score: 766.6147
- Similarity Product: 668.3620245178042

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test