Amino acid sequences of antibodies can be generated using a generative adversarial network that includes a first generating component that generates amino acid sequences of antibody light chains and a second generating component that generates amino acid sequences of antibody heavy chains. Amino acid sequences of antibodies call be produced by combining the respective amino acid sequences produced by the first generating component and the second generating component. The training of the first generating component and the second generating component can proceed at different rates. Additionally, the antibody amino acids produced by combining amino acid sequences front the first generating component and the second generating component may be evaluated according to complentarity-determining regions of the antibody amino acid sequences. Training datasets may be produced using amino acid sequences that correspond to antibodies have particular binding affinities with respect to molecules, such as binding affinity with major histocompatibility complex (MHC) molecules.

An effective sequencing method, the method comprising: (1) performing first sequencing on a sequencing template on a chip surface, so as to facilitate obtaining first sequencing data by means of first newly generated sequencing strands being formed, the sequencing template being connected onto the chip surface by means of a sequencing adapter; (2) performing first blocking treatment on 3′ ends of at least a portion of the first newly generated sequencing strands; and (3) performing second sequencing on the sequencing template, so as to facilitate obtaining second sequencing data by means of second newly generated sequencing strands being formed.

Provided herein are methods of identifying a group of complementarity determining region (CDR)3, 2 and/or 1 nanobody amino acid sequences (CDR3, CDR2 and/or CDR1 sequences) wherein a reduced number of the CDR3, CDR2 and/or CDR1 sequences are false positives as compared to a control, methods for determining antigen affinity of nanobody peptide sequences, and related methods for training a deep learning model.

Provided herein are methods of identifying a group of complementarity determining region (CDR)3, 2 and/or 1 nanobody amino acid sequences (CDR3, CDR2 and/or CDR1 sequences) wherein a reduced number of the CDR3, CDR2 and/or CDR1 sequences are false positives as compared to a control, methods for determining antigen affinity of nanobody peptide sequences, and related methods for training a deep learning model.

Provided herein are systems and methods for screening desirable biological variants using a high-throughput integrated system. The integrated system may be configured to input a plurality of parameters from functional studies of biological variants under applied conditions, in conjunction with integrated libraries of biological variants, and filter the inputs to produce desirable biological variants based on an input performance requirement. The system may output optimized strains, molecules, or novel molecules expected to have a desirable functional characteristic. Accordingly, the methods and systems disclosed herein enable multi-parametric studies of biological diversity and conditional diversity in systems biology.

The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bioinformatic tools and techniques.

The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bioinformatic tools and techniques.

This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.

This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.

The generation of a factory order to control production of nucleotide sequences by a gene manufacturing system includes receiving an expression indicating an operation on sequence operands, each representing at least one nucleotide sequence part, evaluating the expression to a sequence specification, wherein the sequence specification comprises a data structure including one or more first-level operations and one or more second-level operations, and generating the factory order based upon execution of the one or more first-level operations and the one or more second-level operations. In a recursive manner, the one or more first-level operations operate on at least one first-level sequence operand, the value of which is resolved by execution of one or more of the second-level operations. The factory order may then be provided to the gene manufacturing system to assemble the sequence parts into nucleotide sequences represented by the sequence specification.