Abstract: A method, apparatus, and computer-readable medium for efficiently optimizing a phenotype with a specialized prediction model, including receiving constraints, encoding genotype information in experimental data points corresponding to the constraints experiential genotype vectors, the experimental data points comprising the genotype information and phenotype information corresponding to the genotype information, training a phenotype prediction model based on the experiential genotype vectors, the corresponding phenotype information, and the one or more constraints, applying the phenotype prediction model to available genotypes corresponding to the constrains to generate scores, determining result genotypes based on a ranking of the available genotypes according to the scores, and generating, a result based on the result genotypes, the result indicating one or more genetic constructs for testing.

Abstract: A method, apparatus, and computer-readable medium for efficiently optimizing a phenotype with a combination of a generative and a predictive model, training a phenotype prediction model based on experiential genotype vectors, training a genotype generation model based on sample genotype vectors, generating new genotype vectors, applying the phenotype prediction model to the new genotype vectors to generate scores, determining result genotypes based on a ranking of the available genotypes according to the scores, and generating a result based on the result genotypes, the result indicating one or more genetic constructs for testing.

Abstract: A method, apparatus, and computer-readable medium for efficiently optimizing a phenotype with a specialized prediction model, including receiving constraints, encoding genotype information in experimental data points corresponding to the constraints experiential genotype vectors, the experimental data points comprising the genotype information and phenotype information corresponding to the genotype information, training a phenotype prediction model based on the experiential genotype vectors, the corresponding phenotype information, and the one or more constraints, applying the phenotype prediction model to available genotypes corresponding to the constrains to generate scores, determining result genotypes based on a ranking of the available genotypes according to the scores, and generating, a result based on the result genotypes, the result indicating one or more genetic constructs for testing.

Abstract: A method, apparatus, and computer-readable medium for efficiently optimizing a phenotype with a combination of a generative and a predictive model, training a phenotype prediction model based on experiential genotype vectors, training a genotype generation model based on sample genotype vectors, generating new genotype vectors, applying the phenotype prediction model to the new genotype vectors to generate scores, determining result genotypes based on a ranking of the available genotypes according to the scores, and generating a result based on the result genotypes, the result indicating one or more genetic constructs for testing.

Abstract: A method, apparatus, and computer-readable medium for optimal pooling of nucleic acid samples for next generation sequencing, including receiving sample records corresponding to samples, each sample record comprising a sample identifier and a nucleic acid reference sequence of the sample, determining unique nucleic acid reference sequences in the sample records, computing, a nucleic acid overlaps between the unique nucleic acid reference sequences, and determining an optimal grouping of the plurality of samples into a plurality of sample pools based at least in part on the nucleic acid reference sequence of each sample record, the nucleic acid overlaps between the unique nucleic acid reference sequences, and one or more constraints, the one or more constraints including a maximum overlap constraint.

Abstract: A method, apparatus, and computer-readable medium for efficiently optimizing a phenotype with a combination of a generative and a predictive model, training a phenotype prediction model based on experiential genotype vectors, training a genotype generation model based on sample genotype vectors, generating new genotype vectors, applying the phenotype prediction model to the new genotype vectors to generate scores, determining result genotypes based on a ranking of the available genotypes according to the scores, and generating a result based on the result genotypes, the result indicating one or more genetic constructs for testing.

Abstract: A method, apparatus, and computer-readable medium for efficiently optimizing a phenotype with a specialized prediction model, including receiving constraints, encoding genotype information in experimental data points corresponding to the constraints experiential genotype vectors, the experimental data points comprising the genotype information and phenotype information corresponding to the genotype information, training a phenotype prediction model based on the experiential genotype vectors, the corresponding phenotype information, and the one or more constraints, applying the phenotype prediction model to available genotypes corresponding to the constrains to generate scores, determining result genotypes based on a ranking of the available genotypes according to the scores, and generating, a result based on the result genotypes, the result indicating one or more genetic constructs for testing.

Abstract: Compositions and methods are provided for the rapid and highly accurate identification of the entire essential genome of any organism under a given selection condition at a resolution of a few base pairs. An engineered transposon bearing an adapter sequence for ultra high throughput adaptor-based sequencing is employed for hyper-saturated transposon mutagenesis. Transposon junctions are subsequently isolated and collectively amplified through a shared parallel PCR strategy such that a second adaptor sequence is further incorporated into template DNA so that the first adaptor sequence and the second adaptor sequence flank the 5? and 3? regions of the sample DNA, respectively. Sample DNA is then sequenced in an ultra high-throughput adaptor-based DNA sequencer using adaptor primers. Transposon insertion sites are mapped onto the organism's genome, allowing for the algorithmic identification of essential genetic elements based on genomic transposition frequency.