The invention relates to a reference-based method for the compression of genome sequence data produced by a sequencing machine. The sequences of nucleotides or bases, that have been previously aligned to a reference sequence, are determined to be perfectly mapped, imperfectly mapped or unmapped with the reference sequence; and then coded according to said determination. The determining step comprises comparing, for each imperfectly mapped sequence, the number of mismatches between said sequence and the reference sequence with a reference threshold value, and encoding the imperfectly mapped sequences according to distinct encoding processes, depending on the result of said comparison method for the compression of genome sequence data produced by a sequencing machine.

This disclosure describes methods, non-transitory computer readable media, and systems that can train a genome-location-classification model to classify or score genomic coordinates or regions by the degree to which nucleobases can be accurately identified at such genomic coordinates or regions. For instance, the disclosed systems can determine sequencing metrics for sample nucleic-acid sequences or contextual nucleic-acid subsequences surrounding particular nucleobase calls. By leveraging ground-truth classifications for genomic coordinates, the disclosed systems can train a genome-location-classification model to relate data from one or both of the sequencing metrics and contextual nucleic-acid subsequences to confidence classifications for such genomic coordinates or regions. After training, the disclosed systems can also apply the genome-location-classification model to sequencing metrics or contextual nucleic-acid subsequences to determine individual confidence classifications for individual genomic coordinates or regions and then generate at least one digital file comprising such confidence classifications for display on a computing device.

A computer-implemented method and system uses a map which maps from gene expression data for a plurality of training tumors in a tumor atlas to gene expression data representing single cells derived from mammal samples in developmental stages in a single-cell atlas. The method and system: (A) use the map to extract, from the plurality of training tumors, a plurality of biological components, thereby generating, for each training tumor-biological component pair, a corresponding biological component score; and (B) construct, based on the two atlases and the map, a machine learning perceptron classifier that outputs a tumor type of an input tumor based on its gene expression data. The method and system may generate the map before using it. The method and system may apply the machine learning perceptron classifier to the input tumor's gene expression data to generate the tumor type of the input tumor.

A computer-implemented method and system uses a map which maps from gene expression data for a plurality of training tumors in a tumor atlas to gene expression data representing single cells derived from mammal samples in developmental stages in a single-cell atlas. The method and system: (A) use the map to extract, from the plurality of training tumors, a plurality of biological components, thereby generating, for each training tumor-biological component pair, a corresponding biological component score; and (B) construct, based on the two atlases and the map, a machine learning perceptron classifier that outputs a tumor type of an input tumor based on its gene expression data. The method and system may generate the map before using it. The method and system may apply the machine learning perceptron classifier to the input tumor's gene expression data to generate the tumor type of the input tumor.

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.

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.

Exemplary embodiments described herein provide improved techniques for identifying and accounting for molecule variants when modeling a fragmentation of the molecule. The variants may be identified by comparing possible modifications of molecule fragments against experimental data to rank or score the possible modifications. Possible modifications may be shown in a variant interface where a modification may be selected as a candidate for comparison to experimental data.

Exemplary embodiments described herein provide improved techniques for matching an experimental mass spectrometry fragmentation against a known or predicted fragmentation from a library. Among other improvements, exemplary embodiments provide more accessible interfaces that are easier to interpret, thus allowing for more accurate and faster matches. They also may automatically accumulate multiple experimental results to determine whether several runs of a given sample cumulatively represent a library fragmentation pattern. Furthermore, exemplary embodiments provide simplified techniques for identifying and accounting for molecule variants.