Inscripta, Inc.

Paul Hardenbol

Miles Gander

Eileen Spindler

Krishna Kannan

Matthew David Estes

Krishna Yerramsetty

Isaac David Wagner

Thomas Hraha

Chad Douglas Sanada

Andrea Halweg-Edwards

Shea Lambert

Disclosed systems and methods relate to predicting the relative representation of genomic variants in an edited cell population, based on the editing cassette design representation in an editing cassette design library used to generate the edited cell population. A library of editing cassette designs is generated, and a feature vector, or sequence embedding, is developed for each design using natural language processing techniques. The feature vector may be based upon sequence attributes and editing kinetics of each cassette design as well as attributes that describe the library context. Features may include sequence embeddings generated from a neural network, linguistic-type distances, and statistical distance summaries thereof. The feature vectors are classified using one or more machine learning models, and the classified feature vectors are used to predict the representation of each design an edited cell population.

Methods and systems for modeling of design representation in a library of editing cassettes

The present disclosure provides methods to increase the percentage of edited yeast cells in a cell population using nucleic-acid guided editing, and automated multi-module instruments for performing these methods.

Increased nucleic-acid guided cell editing in yeast

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Boulder, CO, United States of America

Paul Hardenbol