The present disclosure provides compositions and methods for the generation of an antibody or immunogenic composition, such as a vaccine, through epitope focusing by variable effective antigen surface concentration. Generally, the composition and methods of the disclosure comprise three steps: a “design process” comprising one or more in silico bioinformatics steps to select and generate a library of potential antigens for use in the immunogenic composition; a “formulation process”, comprising in vitro testing of potential antigens, using various biochemical assays, and further combining two or more antigens to generate one or more immunogenic compositions; and an “administering” step, whereby the immunogenic composition is administered to a host animal, immune cell, subject or patient. Further steps may also be included, such as the isolation and production of antibodies raised by host immune response to the immunogenic composition.

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 invention relates to a method for discovering specific functional antibodies, in particular to a method for discovering specific functional antibodies based on sequence composition and frequency analysis of variable regions of immune host antibodies. Compared with conventional high-throughput antibody sequencing method, this method can quickly and accurately obtain the full-length sequence of the variable region of antibody containing candidate CDR3, the success rate of antibody gene pairing is high and it is suitable for the detection of few samples, and improves the efficiency of obtaining specific functional antibodies.

Disclosed is a system for gaining mechanistic insights into action of a drug using in-silico techniques. The system is communicably coupled to a phenotype ontological databank; wherein the system comprises a processor communicably coupled to a memory. The processor is configured to receive a first input of the drug, receive a second input relating to at least one phenotype associated with the drug, identify targets of the drug to obtain a drug target list, determine phenotypic targets of the drug, compare the drug target list with the phenotypic targets of the drug to identify a plurality of overlapping targets therebetween, generate a Drug-Target-Phenotype (DTP) network using the plurality of overlapping targets, compute relevant pathways by performing Signaling Pathway Impact Analysis (SPIA) for the plurality of overlapping targets, generate a Pathway-Target-Phenotype (PTP) network, and compute mechanistic insights into the action of the drug from the analysis of PTP network.

Disclosed is a system for gaining mechanistic insights into action of a drug using in-silico techniques. The system is communicably coupled to a phenotype ontological databank; wherein the system comprises a processor communicably coupled to a memory. The processor is configured to receive a first input of the drug, receive a second input relating to at least one phenotype associated with the drug, identify targets of the drug to obtain a drug target list, determine phenotypic targets of the drug, compare the drug target list with the phenotypic targets of the drug to identify a plurality of overlapping targets therebetween, generate a Drug-Target-Phenotype (DTP) network using the plurality of overlapping targets, compute relevant pathways by performing Signaling Pathway Impact Analysis (SPIA) for the plurality of overlapping targets, generate a Pathway-Target-Phenotype (PTP) network, and compute mechanistic insights into the action of the drug from the analysis of PTP network.

The present invention provides methods for diagnosing melanoma and/or solar lentigo in a subject by analyzing nucleic acid molecules obtained from the subject. The present invention also provides methods for distinguishing melanoma from solar lentigo and/or dysplastic nevi and/or normal pigmented skin. The methods include analyzing expression or mutations in epidermal samples, of one or more skin markers. The methods can include the use of a microarray to analyze gene or protein profiles from a sample.

The present invention provides methods for diagnosing melanoma and/or solar lentigo in a subject by analyzing nucleic acid molecules obtained from the subject. The present invention also provides methods for distinguishing melanoma from solar lentigo and/or dysplastic nevi and/or normal pigmented skin. The methods include analyzing expression or mutations in epidermal samples, of one or more skin markers. The methods can include the use of a microarray to analyze gene or protein profiles from a sample.

The present invention is directed to methods and devices for real-time diagnosis of disease states in subjects, for example, infections caused by one or more microorganisms or cancer.

Provided are in silico methods for utilizing an algorithm and machine learning model to compute a protein nutritional quality score for an organism from the organism's genome and to select an organism as a source of protein based on a computed protein nutritional quality score.

Provided are in silico methods for utilizing an algorithm and machine learning model to compute a protein nutritional quality score for an organism from the organism's genome and to select an organism as a source of protein based on a computed protein nutritional quality score.