Spotfire provides unique capabilities to analyze the wealth of “-omics” data; enabling Spotfire customers to more effectively identify new drug targets and biomarkers. Spotfire facilitates the routine analysis of genomic and proteomic data, and the real power emerges when data is combined from multiple sources to enable new insights and discoveries.
Quickly identify the most promising genes, validate these targets by supporting methods such as rtPCR, further understand their biological relevance by incorporating additional information, and apply this information to target disease states.
Identify single nucleotide polymorphisms (SNPs) of interest based on experimental data associated with known information from a variety of sources, easily create new methods for handling the latest genotyping technology and implement new statistical routines and visualizations. Discover proteins with multi-fold changes in expression patterns and cluster the expression profiles to find proteins with similar patterns, correlate interesting findings with corresponding gene expression data.
"I selected TIBCO Spotfire S+ because the company has a history of developing and delivering powerful analytical tools. I believe that this product has a significant advantage over other competitors due to its powerful analytics and visualization tools."
- Todd Wood, Director of Bioinformatics, Genomics Institute
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Target Identification: Quickly identify the most promising genes, validate these targets by supporting methods such as rtPCR, further understand their biological relevance by incorporating additional information, and apply this information to target disease states.
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Genotyping: Identify single nucleotide polymorphisms (SNPs) of interest based on experimental data associated with known information from a variety of sources, easily create new methods for handling the latest genotyping technology and implement new statistical routines and visualizations.
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Protein Expression: Discover proteins with multi-fold changes in expression patterns and cluster the expression profiles to find proteins with similar patterns, correlate interesting findings with corresponding gene expression data.