Scientists deal with massive datasets that must be interpreted in the context of other databases with biomedical knowledge. HGMD and NCBI GEO repositories accumulate vast amounts of data generated arrays and next‐generation sequencing (NGS) techniques. Although there is no universal way to analyse microarray data, best practices have been highlighted by which these data obtained from multiplex measurements using microarrays are explored.
Variant analysis, copy number aberrations and RNA and proteome expression analysis using Somalogic aptamers on microarrays involve the processing of large numeric data tables linked to literature/text, categorical sequence data and last but not least unstructured clinical data of patients. Its aim is to rank a list of variants, genes (RNA transcripts) and/or proteins that are expressed differentially (including coregulated and/or antiregulated molecules) and in a biologically relevant and statistically significant manner under many different experimental conditions or disease states. Visual data‐mining approaches are used besides advanced R statistical methods to identify unexpected relationships beyond the original hypotheses for clinical decision‐making in research or therapeutic settings. Software tools such as Instem/OmniViz Tibco/Spotfire, Partek, Biodiscovery Nexus, Qiagen/Ingenuity Pathway Analysis and/or Qiagen/Ingenuity Variant Analysis help scientists to put their scientific findings in the right context to stratify patients for targeted treatment.
Key Concepts
Bioinformatics uses computational tools for integration of the analysis of biological and medical data, which are being discussed in this article.
Bioinformatics covers software to handle, acquire, store, integrate, archive, analyse and visualise OMIC data. As the field matures, the focus shifts from single experiments towards cross‐OMIC data integration of DNA, RNA and protein array and sequencing data.
Detection of mutations, deletions and amplifications.
Patient stratification in Primary Immunodeficiencies.
Targeted therapy in AML.
DNA, RNA OMICS integrative analysis.
Web based resources for genome analysis.