This disclosure describes methods, non-transitory computer readable media, and systems that can generate signal-to-noise-ratio metrics for clusters of oligonucleotides to which tagged nucleotide bases are added and utilize the signal-to-noise-ratio metrics to generate nucleotide-base calls and determine base-call quality. For example, the disclosed systems can generate the signal-to-noise-ratio metrics using scaling factors and noise levels associated with light signals detected from the clusters of oligonucleotides. The disclosed systems can utilize the signal-to-noise-ratio metrics to generate intensity-value boundaries for generating nucleotide-base-calls for the signals in accordance with one or more base-call-distribution models. Additionally, the disclosed systems can utilize a threshold to filter out signals detected from the clusters of oligonucleotides that have low signal-to-noise-ratio metrics. The disclosed systems can further utilize the signal-to-noise-ratio metrics to generate quality metrics for generated nucleotide-base calls.

A method for assessing drug-resistant microorganism includes the following steps. A model establishing step is performed so as to obtain an antibiotic resistance assessing classifier. A test sample is provided. A sample pre-processing step is performed so as to obtain a processed sample. An analysis step is performed so as to obtain a target mass spectrum data. A spectrum pre-processing step is performed so as to obtain a normalized target mass spectrum data. A feature extraction step is performed so as to obtain a spectrum feature. An assessing step is performed, wherein the spectrum feature is analyzed by the antibiotic resistance assessing classifier so as to output an assessed result of drug-resistant microorganism, and the assessed result of drug-resistant microorganism is for assessing whether the test microorganism is a drug-resistant microorganism or not.

A method for assessing drug-resistant microorganism includes the following steps. A model establishing step is performed so as to obtain an antibiotic resistance assessing classifier. A test sample is provided. A sample pre-processing step is performed so as to obtain a processed sample. An analysis step is performed so as to obtain a target mass spectrum data. A spectrum pre-processing step is performed so as to obtain a normalized target mass spectrum data. A feature extraction step is performed so as to obtain a spectrum feature. An assessing step is performed, wherein the spectrum feature is analyzed by the antibiotic resistance assessing classifier so as to output an assessed result of drug-resistant microorganism, and the assessed result of drug-resistant microorganism is for assessing whether the test microorganism is a drug-resistant microorganism or not.

A method of progressively training a base caller is disclosed. The method includes iteratively initially training a base caller with analyte comprising a single-oligo base sequence, and generating labelled training data using the initially trained base caller. At operations (i), the base caller is further trained with analyte comprising multi-oligo base sequences, and labelled training data is generated using the further trained base caller. Operations (i) are iteratively repeated to further train the base caller. In an example, during at least one iteration, a complexity of neural network configuration loaded within the base caller is increased. In an example, labelled training data generated during an iteration is used to train the base caller during an immediate subsequent iteration.

The present disclosure provides, inter alia, methods and compositions (e.g., conjugates) for imaging, at high spatial resolution, targets of interest.

The present disclosure provides, inter alia, methods and compositions (e.g., conjugates) for imaging, at high spatial resolution, targets of interest.

The present invention concerns the analysis of cell-free nucleic acids to determine the contribution of cell-free nucleic acids from specific tissues. In addition, the invention provides methods for diagnosing diseases based on cell-free nucleic acid analysis. The methods of the invention are also useful to detect quality defects in samples containing cell-free nucleic acids.

The present invention concerns the analysis of cell-free nucleic acids to determine the contribution of cell-free nucleic acids from specific tissues. In addition, the invention provides methods for diagnosing diseases based on cell-free nucleic acid analysis. The methods of the invention are also useful to detect quality defects in samples containing cell-free nucleic acids.

A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.

A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.