ARTIFICIAL INTELLIGENCE-BASED GENERATION OF SEQUENCING METADATA

The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the...

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Bibliographic Details
Main Authors KASHEAGHIGHI, Dorna, KIA, Amirali, DUTTA, Anindita
Format Patent
LanguageEnglish
French
German
Published 26.01.2022
Subjects
BEER
BIOCHEMISTRY
CALCULATING
CHEMISTRY
COMPOSITIONS OR TEST PAPERS THEREFOR
COMPUTING
CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL ORENZYMOLOGICAL PROCESSES
COUNTING
ENZYMOLOGY
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES
MEASURING
MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEICACIDS OR MICROORGANISMS
METALLURGY
MICROBIOLOGY
MUTATION OR GENETIC ENGINEERING
PHYSICS
PROCESSES OF PREPARING SUCH COMPOSITIONS
SPIRITS
TESTING
VINEGAR
WINE
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Summary:The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the sequencing images as one of four bases (A, C, T, and G), generating a cluster map that identifies clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence, determining cluster metadata based on the disjointed regions in the cluster map, and using the cluster metadata to generate the ground truth training data for training the neural network-based template generator for the cluster metadata determination task.
Bibliography:Application Number: EP20200719052