18results about "Recognition of DNA microarray pattern" patented technology

Image data analysis, and particularly identifying cluster or polony locations for performing base-calling in a digital image of a flow cell during DNA sequencing is described. A method may include generating a first plurality of flow cell images of a cellular sample immobilized on a support by conducting one or more cycles of sequencing reactions. The cellular sample may include a plurality of concatemer molecules therewithin. For the first plurality of flow cell image, pixel intensities, and a respective color purity of each of the pixel intensities may be determined. A base calling template may include base calling locations based on the pixel intensities and the respective color purity of the pixel intensities. The base calling template may be for registering a second plurality of flow cell images of the support in one or more subsequent cycles of the one or more cycles.

The application relates to the field of protein deconvolution analysis, in particular to a mass spectrum analysis method and device and an intelligent terminal. The mass spectrum analysis method comprises the following steps: obtaining a target spectrum, wherein the target spectrum is used for reflecting mass spectrum data of a protein peptide segment; determining a spectrum feature of the target spectrum based on a mass-to-charge ratio of the target spectrum; inputting the spectrum feature of the target spectrum into a preset deconvolution model to obtain an analysis category of the target spectrum, wherein the analysis category is used for reflecting a peptide sequence corresponding to the target spectrum. The mass-to-charge ratio of the target spectrum is used to obtain the spectrum feature, and the spectrum feature is input into the deconvolution model, and the analysis category is obtained through the deconvolution model. The application has the characteristics that the peptide sequence of the protein corresponding to the target spectrum can be effectively, accurately and quickly analyzed.

The present disclosure describes systems, non-transitory computer-readable media, and methods for determining adjusted locations of clusters of oligonucleotides based on a phase difference between band-edge spectral regions of a nucleotide-sample-slide region image. For example, the disclosed systems (i) identify a pair of band-edge spectral regions from a spatial frequency band in an oligonucleotide-cluster image and (ii) determine a phase difference between the pair of band-edge spectral regions. Based on the phase difference, the disclosed systems generate a location error in at least one direction for predicted locations of the oligonucleotide clusters. Based on the location error, the disclosed systems determine adjusted locations of the oligonucleotide-clusters within the nucleotide-sample-slide region.

The disclosed technology attenuates spatial crosstalk from sequencing images used for base calling. Specifically, the disclosed technology accesses an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional neighboring clusters. These pixels include a center pixel that contains a center of the target cluster. Each of these pixels can be divided into a plurality of subpixels. From a particular subpixel, among the plurality of subpixels of the center pixel that contains the center of the target cluster, the disclosed technology selects a subpixel lookup table corresponding to the particular subpixel from a library of subpixel lookup tables. The selected subpixel lookup table contains pixel coefficients configured to maximize a signal-to-noise ratio. The disclosed technology multiplies these pixel coefficients element-wise with the pixels and determines a weighted sum.

The application discloses a kind of stomach cancer HER2 state prediction model training, prediction method and system, wherein, training method includes: obtaining sample image set, the sample image set includes first dyeing image and second dyeing image;Second dyeing image in the sample image set is marked with state, and image label is obtained;The sample image set is pathologically annotated, and training sample set is obtained;The training sample set and the image label are input into untrained stomach cancer HER2 state prediction model and are trained, and the stomach cancer HER2 state prediction model of training completion is obtained.The embodiment of the application can improve the accuracy of stomach cancer HER2 state prediction model, and can be widely applied in artificial intelligence technical field.

A computer-implemented method comprises: determining coefficients corresponding to a section of a flow cell; accessing an image depicting the section of the flow cell and intensity emissions from a target cluster of concatemers; and generating a base call for the target cluster of concatemers by applying the coefficients to the intensity emissions for the target cluster of concatemers.

The technology disclosed accesses an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters. The technology disclosed selects, from a bank of lookup tables, a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio. The technology disclosed applies the pixel coefficients to the pixels having intensity values to produce an output, and base calls the target cluster based on the output.

Methods and systems for image analysis are provided, and in particular for identifying a set of base-calling locations in a flow cell for DNA sequencing. These include capturing flow cell images after each sequencing step performed on the flow cell, and identifying candidate cluster centers in at least one of the flow cell images. Intensities are determined for each candidate cluster center in a set of flow cell images. Purities are determined for each candidate cluster center based on the intensities. Each candidate cluster center with a purity greater than the purity of the surrounding candidate cluster centers within a distance threshold is added to a template set of base-calling locations.

Methods and systems for determining a plurality of sequences of nucleic acid (e.g., DNA) molecules in a sequencing-by-synthesis process are provided. In one embodiment, the method comprises obtaining images of fluorescent signals obtained in a plurality of synthesis cycles. The images of fluorescent signals are associated with a plurality of different fluorescence channels. The method further comprises preprocessing the images of fluorescent signals to obtain processed images. Based on a set of the processed images, the method further comprises detecting center positions of clusters of the fluorescent signals using a trained convolutional neural network (CNN) and extracting, based on the center positions of the clusters of fluorescent signals, features from the set of the processed images to generate feature embedding vectors. The method further comprises determining, in parallel, the plurality of sequences of DNA molecules using the extracted features based on a trained attention-based neural network.

The technology disclosed attenuates spatial crosstalk from sequencing images for base calling. In particular, the technology disclosed accesses an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters. The pixels include a center pixel that contains a center of the target cluster. Each pixel in the pixels is divisible into a plurality of subpixels. Depending upon a particular subpixel, in a plurality of subpixels of the center pixel, which contains the center of the target cluster, the technology disclosed selects, from a bank of subpixel lookup tables, a subpixel lookup table that corresponds to the particular subpixel. The selected subpixel lookup table contains pixel coefficients that are configured to maximizes a signal-to-noise ratio. The technology disclosed element-wise multiplies the pixel coefficients with the pixels and determines a weighted sum.

System and methods in this disclosure identify leakage from one partition to surrounding partitions. Detecting leakage may include input data describing the location of the partition, the fluorescence intensity, and whether the partition is positive for an analyte. A machine learning model may be trained on an extensive input data set to identify partitions that have leakage or a high probability of leakage. In some embodiments, features used in the machine learning model include values that describe a neighborhood around the partition and not merely the partition itself.

The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.

This disclosure relates to sensing the movement of DNA rolling motors comprising microparticles or rods on transduction material in the presence of viruses, microbes, or other analytes for diagnostic testing. In certain embodiments, the presence of viral particles, other target microbial biomolecules, or analytes stall the motor by specifically binding to aptamers crosslinking the analytes to the particles, rods, or surface of the transduction material. It is contemplated that microparticles or other rolling motors move along a surface whereby an aptamer targets an analyte, e.g., viral SARS-CoV-2, and acts to inhibit, reduce, or restrict the speed, acceleration, or area of movement on the surface indicating the presence of the analyte in the sample.

A base calling method and system, a gene sequencer and a storage medium. The base calling method comprises the following steps: acquiring a first image of a biochip in a red light channel and a second image of the biochip in a green light channel (S1); performing base grouping according to the first image and the second image, and preliminarily identifying the base category of each group (S2); when the number of the base categories of all the groups is at least two, adjusting the brightness value of the first image and the brightness value of the second image according to the base categories of all the groups (S3); respectively performing normalization processing on the first image and the second image (S4); and performing base grouping according to the normalized first image and the normalized second image, and identifying the base category of each group again (S5). The base calling method can accurately identify base categories for data to be sequenced in which some base categories are missing, so that the accuracy of gene sequencing can be improved.

The application discloses a center positioning method and device of a fluorescent cluster, a terminal device and a computer readable storage medium. A preset clustering algorithm is used to obtain a brightness segmentation threshold of a sub-image of a fluorescent image. The sub-image is subjected to threshold segmentation according to the brightness segmentation threshold to obtain a fluorescent cluster segmentation image corresponding to the fluorescent image. A fluorescent cluster template is determined according to the area size of each fluorescent cluster in the fluorescent cluster segmentation image. The center of the fluorescent cluster is positioned according to the brightness distribution of the fluorescent cluster in the fluorescent cluster template and the maximum brightness point in the fluorescent cluster to obtain a cluster center. The application solves the technical problem that the conventional fluorescent cluster center positioning method is prone to failure in the case of fluorescent cluster adhesion, thereby failing to accurately position the center of the fluorescent cluster.

A computer-implemented method comprises: determining coefficients corresponding to a section of a flow cell; accessing an image depicting the section of the flow cell and intensity emissions from a target cluster of concatemers; and generating a base call for the target cluster of concatemers by applying the coefficients to the intensity emissions for the target cluster of concatemers.

Direct targeted sequencing (DTS) methods and a hidden Markov model (HMV) can be used to call the copy number of a segment of interest within a region of interest. Described herein are methods for calling a copy number variant or a copy number variant abnormality using an HMM, and methods for determining a copy number based on a copy number likelihood model, in a test sequencing library that has be sequenced using DTS methods. Also described herein are methods for determining a copy number of a segment, including accounting for spurious capture probes that may arise from the DTS methods.

The technology disclosed accesses an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters. The technology disclosed selects, from a bank of lookup tables, a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio. The technology disclosed applies the pixel coefficients to the pixels having intensity values to produce an output, and base calls the target cluster based on the output.