Abstract: Provided herein are methods for aligning raw genetic sequence data generated by a sequencing device. Also provided herein are methods and systems for quantifying the probability that possible alignments for one or more read pairs are correct, for calling known variants, and for detecting novel structural variants.

Abstract: A method for characterizing a sample comprising genetic information, comprising: (i) receiving a plurality of sequencing signals from a sequencing operation for a sample, each of the plurality of sequencing signals representing a genetic sequence; (ii) setting, based on a received sequencing signal, a bit within a bit array to a first value for the received sequencing signal, wherein a set of one or more bits is associated with a unique received sequencing signal; (iii) calculating a rate of change of bits within the bit array as new sequencing signals are received; (iv) comparing the rate of change to a predetermined threshold; and (v) identifying the sequencing operation as insufficient if the rate of change is at or above the predetermined threshold, or identifying the sequencing operation as sufficient if the rate of change is at or below the predetermined threshold.

Abstract: A method of predicting a gene target sequence for a gene-modifying therapeutic, such as a CRISPR-based therapeutic, that includes the steps of determining a genomic dataset for one or more infectious bacterial samples isolated from one or more hospitals, and inputting the genomic dataset for the one or more infectious bacterial samples into a machine learning model to determine the presence of antimicrobial resistance genes.

Abstract: A method (100) for generating a genome reference using a genome reference system (300), comprising: (i) receiving (110), by the system, sequencing data for a plurality of genomes, the sequencing data generated from a plurality of genomes obtained from a single species; (ii) selecting (120), by a processor (320), sequencing data from one of the plurality of genomes; (iii) aligning (130) the selected sequencing data from the selected genome, comprising a plurality of k-mers, with each of the plurality of genomes; (iv) determining (140), based on the alignment, a frequency of each of the plurality of k-mers within the plurality of genomes; (v) selecting (160), based on the frequency determination, one or more base positions within the plurality of k-mers that exceed a predetermined frequency threshold; (vi) assigning (170) the selected base positions to a genome reference; and (vii) storing (180) the genome reference in a data structure (326, 360).

Abstract: A method for predicting a drug resistance phenotype of a microbe, comprising: (i) receiving sequencing information for the microbe, comprising at least a portion of the microbe's accessory genome; (ii) determining an accessory genome similarity metric between the accessory genome of the microbe and the accessory genome of one or more microbes in a dataset of previously characterized microbes, wherein each microbe in the dataset of previously characterized microbes is associated with drug resistance information; (iii) predicting, based on the determined accessory genome similarity metrics, a drug resistance of the microbe; and (iv) reporting the predicted drug resistance of the microbe.

Abstract: A wherein the processor is further configured to method of determining the antibiotic resistance of a pathogen from in a sample from a patient or the environment in real-time, including: sequencing the genome of the pathogen from the patient sample in real-time; identifying the pathogen from the sequencing data as it becomes available; determining if the pathogen is resistant to a first antibiotic using a machine learning model, the identity of the pathogen, and the sequencing data for the pathogen; tabulating data regarding the antibiotic resistance of the pathogen; and producing a graphical user interface indicating the antibiotic resistance of the pathogen

Abstract: A method (100) for characterizing a genomic sample, comprising: (i) receiving (120) a first waveform from a sequencing operation for a sample, the first waveform representing a first genetic sequence; (ii) applying (130) a first function to the first waveform to generate a first waveform representation; (iii) setting (140), based on the first waveform representation, at least a first bit within a first bit array to a first value, wherein the first bit is associated with the generated first waveform representation; (iv) comparing (150) the first bit array with the first value to a second bit array, the second bit array comprising a plurality of bit values representing a set of genetic sequences; and (v) determining (160) whether the first genetic sequence is within the set of genetic sequences based on a match between the first bit array and the second bit array.

Abstract: A method of predicting a gene target sequence for a gene-modifying therapeutic, such as a CRISPR-based therapeutic, that includes the steps of determining a genomic dataset for one or more infectious bacterial samples isolated from one or more hospitals, and inputting the genomic dataset for the one or more infectious bacterial samples into a machine learning model to determine the presence of antimicrobial resistance genes.

Abstract: A method for characterizing a sample comprising genetic information, comprising: (i) receiving a plurality of sequencing signals from a sequencing operation for a sample, each of the plurality of sequencing signals representing a genetic sequence; (ii) setting, based on a received sequencing signal, a bit within a bit array to a first value for the received sequencing signal, wherein a set of one or more bits is associated with a unique received sequencing signal; (iii) calculating a rate of change of bits within the bit array as new sequencing signals are received; (iv) comparing the rate of change to a predetermined threshold; and (v) identifying the sequencing operation as insufficient if the rate of change is at or above the predetermined threshold, or identifying the sequencing operation as sufficient if the rate of change is at or below the predetermined threshold.

Abstract: Provided herein are methods for aligning raw genetic sequence data generated by a sequencing device. Also provided herein are methods and systems for quantifying the probability that possible alignments for one or more read pairs are correct, for calling known variants, and for detecting novel structural variants.