Embodiments of a method and/or system can include generating a set of quality control template (QCT) molecules; determining a set of QCT sequence read clusters based on the set of QCT molecules, such as based on variation regions of the set of QCT molecules; and based on the set of QCT sequence read clusters, determining a sequencing-related parameter, such as a contamination parameter and/or molecule count parameter, associated with the at least one of sequencing library preparation and sequencing.

Embodiments of a method and/or system can include generating a set of quality control template (QCT) molecules; determining a set of QCT sequence read clusters based on the set of QCT molecules, such as based on variation regions of the set of QCT molecules; and based on the set of QCT sequence read clusters, determining a sequencing-related parameter, such as a contamination parameter and/or molecule count parameter, associated with the at least one of sequencing library preparation and sequencing.

Disclosed herein include systems, devices, and methods for determining a variable number tandem repeat (VNTR) status. Haplotypes of a VNTR can be determined using long sequence reads of reference samples aligned to the VNTR in a reference. Short reads of a test sample of a test subject can be aligned to the haplotypes determined using the long sequence reads to determine a VNTR status (e.g., one or more haplotypes or a genotype of the test subject) of the test subject based on the probability indications of the haplotypes.

The present disclosure provides systems and methods for machine learning classification and assessment of COVID-19 disease based on gene expression data. In an aspect, a method for determining a COVID-19 disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample at each of a plurality of COVID-19 disease-associated genomic loci; (b) computer processing the data set to determine the COVID-19 disease state of the subject; and (c) electronically outputting a report indicative of the COVID-19 disease state of the subject.

The present disclosure provides systems and methods for machine learning classification and assessment of COVID-19 disease based on gene expression data. In an aspect, a method for determining a COVID-19 disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample at each of a plurality of COVID-19 disease-associated genomic loci; (b) computer processing the data set to determine the COVID-19 disease state of the subject; and (c) electronically outputting a report indicative of the COVID-19 disease state of the subject.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

The present disclosure provides a system comprising a communication interface and computer for assigning a label to the biomolecule fingerprint, wherein the label corresponds to a biological state. The present disclosure also provides a sensor arrays for detecting biomolecules and methods of use. In some embodiments, the sensor arrays are capable of determining a disease state in a subject.

A biomarker diagnostic system includes a sensor to collect a sweat sample from a biological subject; a processor operatively connected to the sensor, wherein the processor is configured to perform metabolic and proteomic profiling of biomarkers in the sweat sample. The metabolic and proteomic profile is compared to a predetermined profile of the biomarkers and to determine a physiological status of the biomarkers. The system further includes a feedback unit operatively coupled to the sensor and the processor and configured to output physiological performance data based on the physiological status.