Deep Basecaller for Sanger Sequencing

    公开(公告)号:US20220013193A1

    公开(公告)日:2022-01-13

    申请号:US17312168

    申请日:2019-12-10

    Abstract: A deep basecaller system for Sanger sequencing and associated methods are provided. The methods use deep machine learning. A Deep Learning Model is used to determine scan labelling probabilities based on an analyzed trace. A Neural Network is trained to learn the optimal mapping function to minimize a Connectionist Temporal Classification (CTC) Loss function. The CTC function is used to calculate loss by matching a target sequence and predicted scan labelling probabilities. A Decoder generates a sequence with the maximum probability. A Basecall position finder using prefix beam search is used to walk through CTC labelling probabilities to find a scan range and then the scan a position of peak labelling probability within the scan range for each called base. Quality Value (QV) is determined using a feature vector calculated from CTC labelling probabilities as an index into a QV look-up table to find a quality score.

    METHODS AND SYSTEMS FOR ANALYZING BIOLOGICAL REACTION SYSTEMS
    2.
    发明申请
    METHODS AND SYSTEMS FOR ANALYZING BIOLOGICAL REACTION SYSTEMS 有权
    分析生物反应系统的方法和系统

    公开(公告)号:US20160275687A1

    公开(公告)日:2016-09-22

    申请号:US14778415

    申请日:2014-03-18

    Abstract: A method for analyzing biological reaction systems is provided. The method includes receiving an image of a substrate including a plurality of reaction sites after a biological reaction has taken place. Next, the method includes removing a noise background from the first image. The method includes determining an initial position of each reaction site based on an intensity threshold to generate a initial position set, then refining the initial position set of each reaction site based on an expected pattern of locations of the plurality of reaction sites to generate a first refined position set. The method further includes determining a presence or absence of a fluorescent emission from each reaction site based on the first refined position set and the first image.

    Abstract translation: 提供了分析生物反应体系的方法。 该方法包括在发生生物反应之后接收包括多个反应位点的底物的图像。 接下来,该方法包括从第一图像中去除噪声背景。 该方法包括基于强度阈值确定每个反应位点的初始位置以产生初始位置集合,然后基于多个反应位点的预期位置模式来精化每个反应位点的初始位置集合,以产生第一 精致的位置集。 该方法还包括基于第一精制位置集和第一图像来确定来自每个反应位点的荧光发射的存在或不存在。

    IMAGE DRIVEN QUALITY CONTROL FOR ARRAY-BASED PCR

    公开(公告)号:US20200074303A1

    公开(公告)日:2020-03-05

    申请号:US16555803

    申请日:2019-08-29

    Abstract: A system and methods are provided for image driven quality control for array based PCR. The system comprises a PCR unit, a reaction array plate, a convolutional neural network (CNN) configured to receive a sequence of images of the reaction array plate in the PCR system, and an output of the CNN coupled to a control for the reaction array plate. The method comprises applying a sequence of images from a plurality of subarrays of the reaction array plate to a plurality of CNNs during operation of the PCR system on the reaction plate array, operating the CNNs to generate failure mode predictions for the reaction plate based on the sequence of images, and coupling an output of the CNNs to one or more of a setting for manufacture of the reaction array plate or to control the PCR system.

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