Abstract: 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.

Abstract: The present disclosure is drawn to creating cassette designs for nucleic acid-guided nuclease editing. In designing editing cassettes, a set of edit specifications must first be obtained. These edit specifications are taken together with a set of configuration parameters to start a computational pipeline that generates a collection of cassette designs. The process of designing editing cassettes involves the following exemplary steps: 1) creation of a set of candidate cassette designs for each unique edit specification, 2) enumeration of features describing biophysical characteristics of each candidate design, 3) providing each candidate design with a score, and 4) returning a number of scored and rank-ordered candidate cassette designs for each edit specification.

Abstract: 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.

Abstract: The present disclosure relates to compositions, methods, modules and automated integrated instrumentation to make auxiliary or ancillary edits—in addition to intended edits and immunizing edits—in cellular genomic nucleic acids in order to assess and improve nucleic acid-guided nuclease or nickase fusion editing.

Abstract: The present disclosure is drawn to creating cassette designs for nucleic acid-guided nuclease editing. In designing editing cassettes, a set of edit specifications must first be obtained. These edit specifications are taken together with a set of configuration parameters to start a computational pipeline that generates a collection of cassette designs. The process of designing editing cassettes involves the following exemplary steps: 1) creation of a set of candidate cassette designs for each unique edit specification, 2) enumeration of features describing biophysical characteristics of each candidate design, 3) providing each candidate design with a score, and 4) returning a number of scored and rank-ordered candidate cassette designs for each edit specification.

Abstract: The present disclosure is drawn to creating cassette designs for nucleic acid-guided nuclease editing. In designing editing cassettes, a set of edit specifications must first be obtained. These edit specifications are taken together with a set of configuration parameters to start a computational pipeline that generates a collection of cassette designs. The process of designing editing cassettes involves the following exemplary steps: 1) creation of a set of candidate cassette designs for each unique edit specification, 2) enumeration of features describing biophysical characteristics of each candidate design, 3) providing each candidate design with a score, and 4) returning a number of scored and rank-ordered candidate cassette designs for each edit specification.