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 present invention relates to a method for preparing a novel antibody library and a library prepared thereby. The antibody library prepared according to the present invention contains antibodies having excellent physical properties against a plurality of antigens, thereby having functional diversity and containing a plurality of unique sequences, and thus can be favorably used as an antibody library.

Provided are a system for constructing a genome-wide sgRNA library and a use thereof. The system includes an input module, an sgRNA design module and an sgRNA filtering module. By constructing three modules in the system, optimizing details and processes in the modules, and adopting multiple design criteria and screening principles, the genome-wide sgRNA library is finally constructed. The system and method are concise and efficient, and the obtained library has a high quality and good activity, and is convenient for applications in gene editing researches.

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 bionformatic 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 bionformatic tools and techniques.

The present disclosure provides methods for optimizing barcode design for multiplex DNA sequencing. Also disclosed are DNA barcodes optimized for use with particular sequencing technologies.

The present invention relates to an artificial-intelligence-based cancer diagnosis and cancer type prediction method, and, more particularly, to an artificial-intelligence-based cancer diagnosis and cancer type prediction method, which extracts nucleic acids from a biological sample to acquire sequence information, and thus generate vectorized data on the basis of aligned nucleic acid fragments, and then inputs same into a trained artificial intelligence model to analyze a calculated value. Compared with a conventional method, which uses a step of determining the number of chromosomes on the basis of a read count and utilizes each related value as a normalized value, the artificial-intelligence-based cancer diagnosis and cancer type prediction method according to the present invention generates vectorized data to perform an analysis using an AI algorithm, and thus is useful in that similar effects can be exhibited even when read coverage is low.

A system for selecting an immunogenic peptide composition comprising a processor and a memory storing processor-executable instructions that, when executed by the processor, cause the processor to create a first peptide set by selecting a plurality of base peptides, wherein at least one peptide of the plurality of base peptides is associated with a disease, create a second peptide set by adding to the first peptide set a modified peptide, wherein the modified peptide comprises a substitution of at least one residue of a base peptide selected from the plurality of base peptides, and create a third peptide set by selecting a subset of the second peptide set, wherein the selected subset of the second peptide set has a predicted vaccine performance, wherein the predicted vaccine performance has a population coverage above a predetermined threshold, and wherein the subset comprises at least one peptide of the second peptide set.

A system for selecting an immunogenic peptide composition comprising a processor and a memory storing processor-executable instructions that, when executed by the processor, cause the processor to create a first peptide set by selecting a plurality of base peptides, wherein at least one peptide of the plurality of base peptides is associated with a disease, create a second peptide set by adding to the first peptide set a modified peptide, wherein the modified peptide comprises a substitution of at least one residue of a base peptide selected from the plurality of base peptides, and create a third peptide set by selecting a subset of the second peptide set, wherein the selected subset of the second peptide set has a predicted vaccine performance, wherein the predicted vaccine performance has a population coverage above a predetermined threshold, and wherein the subset comprises at least one peptide of the second peptide set.