Allows analysis of kinome microarray data. PIIKA permits users to answer complex biological questions about kinome array data. The software covers cluster analysis, statistical analysis, and data visualization. It can assist in the identification of cellular signaling pathways that are upregulated or downregulated in response to a particular treatment. PIIKA can also facilitate the identification of peptides that have inconsistent responses among the technical replicates on a single array or among different biological replicates.
Automates identification of linear B cell epitopes. ArrayPitope allows to analyse peptide microarray data. It identifies the contribution of each amino acid residue of the target protein for recognition of the corresponding antibodies. The tool incorporates binding signals from overlapping peptides into one statistical analysis to map the selectivity of each residue of the target protein involved in the recognition. It can be useful in complete substitution analysis of pre-identified binding peptides from multiple proteins. ArrayPitope is easy to use for non-expert users.
Manages microarray experiments. ArrayNinja offers a planning application providing features for array designing and source plate population to streamline custom microarray builds. This application can be adapted to simulate any arrayer and is independent of the printed substrate and slide surface chemistry. It aims to assist users in automating the mapping of biomaterial from microarray slide to source plate.
Allows analysis of high-throughput experimental data. MADNet is a data mining and visualization web server that integrates experimental results with the existing biological data in the context of metabolic and signaling pathways, transcription factors and drug targets. The software provides a systems biology approach to complex research problems in a user-friendly interface. It is not only confined to microarray experiments, but can also be used to analyze expression information from different experimental techniques.
Allows automated analysis of high-throughput peptide array data. rapmad is a multi-step approach that combines several computational statistics procedures. Its steps include the preprocessing of the data by removing systematic effects, the exclusion of unreliable measurements and secondary antibody binding peptides and a probabilistic signal call for reactive peptides. It can contribute to establish and broaden the usage of peptide microarrays as a standard tool for a wide-range of peptidomics applications.
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