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AirLab
A laboratory software solution for research in the digital age. AirLab facilitates organization of data on large collections of antibody stocks and streamlines the processes of purchasing, storage, antibody panel creation, results logging, and antibody validation data sharing and distribution. Designed with antibody-based experiments in mind, the software can be easily adapted to tracking of any type of reagent and any type of high- or low-throughput experiment. The cloud-based platform in conjunction with mobile gadgets enables ready access to relevant information, reducing the logistical overhead of research that has become increasingly convoluted due to application of multiparametric experiments.
Glep / overlapping graph clustering-based B-cell epitope predictor
Detects antigen epitopes. Glep is an antibody-agnostic epitope prediction algorithm that detects single, multi-separated, as well as overlapping epitopes from antigens, assuming the data of the corresponding antibodies are not given. The software is composed of three steps: (1) construction of a residue-level graph of an antigen, (2) partition of the graph into subgraphs, and (3) classification of each expanded subgraph as an epitope or a non-epitope by support vector machine (SVM).
CombiNAber
Predicts and analyzes combination antibody neutralization scores using IC50 and/or IC80 for individual antibodies. CombiNaber is a web-tool that predict bnAb combination neutralization results from single bnAb neutralization data using either “Bliss-Hill” (BH) or additive models and perform systematic analysis. It provides the user with the best candidate combinations for their panel. The predicted scores are systematically compared for all single antibodies and 2, 3 and 4 antibody combinations analyzed.
SePaCS / Seroreactivity Profile Classification Service
Obsolete
Allows users to rank seroreactivity profiles using an assortement of supervised statistical learning approaches. SePaCS is a web application that can be performed from several human diseases such as autoimmune diseases and tumor entities. It contains approaches including support vector machines, linear and diagonal discriminant analysis. The application can be run by using both a training and a test set as antibody profile sets or only with a test set.
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