Abstract: Disclosed herein include systems, methods, compositions, and kits for sample analysis. Nucleic acid fragments comprising a capture sequence (or a complement thereof) can be generated from double-stranded genomic deoxyribonucleic acid (gDNA), barcoded to generate single-stranded DNA (ssDNA) fragments, and sequenced. Information relating to the gDNA (e.g., genome, chromatin accessibility, methylome) can be determined based on the sequences of the ssDNA fragments in the sequencing data obtained.

Abstract: Disclosed herein include systems, methods, compositions, and kits for sample analysis. Nucleic acid fragments comprising a capture sequence (or a complement thereof) can be generated from double-stranded genomic deoxyribonucleic acid (gDNA), barcoded to generate single-stranded DNA (ssDNA) fragments, and sequenced. Information relating to the gDNA (e.g., genome, chromatin accessibility, methylome) can be determined based on the sequences of the ssDNA fragments in the sequencing data obtained.

Abstract: Disclosed herein include systems, methods, compositions, and kits for sample analysis. Nucleic acid fragments comprising a capture sequence (or a complement thereof) can be generated from double-stranded genomic deoxyribonucleic acid (gDNA), barcoded to generate single-stranded DNA (ssDNA) fragments, and sequenced. Information relating to the gDNA (e.g., genome, chromatin accessibility, methylome) can be determined based on the sequences of the ssDNA fragments in the sequencing data obtained.

Abstract: Disclosed herein are methods and systems for classifying cell labels, for example identifying a signal cell label. In some embodiments, the method comprises: obtaining sequencing data of barcoded targets created using targets in cells barcoded using barcodes, wherein a barcode comprises a cell label and a molecular label. After ranking the cell labels, a minimum of a second derivative plot of a cumulative sum plot can be determined. Using the methods, a cell label can be classified as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with the cell label and a cell label threshold.

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: Disclosed herein are methods and systems for classifying cell labels, for example identifying a signal cell label. In some embodiments, the method comprises: obtaining sequencing data of barcoded targets created using targets in cells barcoded using barcodes, wherein a barcode comprises a cell label and a molecular label. After ranking the cell labels, a minimum of a second derivative plot of a cumulative sum plot can be determined. Using the methods, a cell label can be classified as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with the cell label and a cell label threshold.

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: Disclosed herein include systems, methods, compositions, and kits for the labeling of nucleic acids targets. In some embodiments, the methods comprise the use of target-specific primers and target-non-specific primers during library preparation (e.g., hybrid library preparation). In some embodiments, the methods increase the abundance of select low abundance targets during cDNA library preparation and/or sequencing library preparation.

Abstract: Disclosed herein include methods, compositions, and kits suitable for use in performing single nuclei capture and barcoding in a single step. In some embodiments, the method comprises isolating nuclei using a nuclei-isolation composition. The nuclei-isolation composition can comprise a nuclei-binding reagent capable of specifically binding to one or more components of a nucleus. The method can include barcoding targets in the nuclei using barcodes to generate barcoded targets for sequencing analysis.

Abstract: Disclosed herein include systems, methods, compositions, and kits for sample analysis. Nucleic acid fragments comprising a capture sequence (or a complement thereof) can be generated from double-stranded genomic deoxyribonucleic acid (gDNA), barcoded to generate single-stranded DNA (ssDNA) fragments, and sequenced. Information relating to the gDNA (e.g., genome, chromatin accessibility, methylome) can be determined based on the sequences of the ssDNA fragments in the sequencing data obtained.

Abstract: Disclosed herein are methods and systems for classifying cell labels, for example identifying a signal cell label. In some embodiments, the method comprises: obtaining sequencing data of barcoded targets created using targets in cells barcoded using barcodes, wherein a barcode comprises a cell label and a molecular label. After ranking the cell labels, a minimum of a second derivative plot of a cumulative sum plot can be determined. Using the methods, a cell label can be classified as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with the cell label and a cell label threshold.

Abstract: Disclosed herein are devices and systems comprising a) a substrate comprising at least 100 microwells and a plurality of beads, wherein a plurality of the at least 100 microwells each contain a single bead, and wherein the ratio of the average diameter of the microwells to the diameter of the beads ranges from about 1.2 to about 1.8; b) a flow cell in fluid communication with the substrate; and c) at least one inlet port and at least one outlet port, wherein the at least one inlet port and at least one outlet port are capable of directing a flow of a fluid through the flow cell, thereby contacting the microwells with the fluid.