Cross-sectional technology for Omics data processing
Experiments and studies involving Omics technologies usually provide very large amounts of data. These data contain either millions of short nucleic acid sequences (DNA or RNA) which are read from a biological sample by Next Generation Sequencing (NGS). Or extremely complex, high-dimensional data sets are generated that reflect the expression of hundreds to thousands of gene transcripts, proteins or metabolites. These data networks are usually obtained from a rather small number of biological samples.
The task of bioinformatics is to manage these high-throughput data and to evaluate them using suitable algorithms and software on powerful servers in such a way that they can expand their biological significance. External data (e.g. known information on molecular networks or complementary gene annotations) as well as sample-specific data are usually included in the evaluation. Bioinformatic evaluations can make a fundamental contribution to answering a wide range of biological and medical questions, and also have the potential to support diagnoses and prognoses in everyday clinical life.
Analysis of DNA, RNA or protein sequences is an essential part of many bioinformatic analyses. Sequence analyses can be used, for example, to determine the relationship between different species, detect mutations or predict the position of genes on the DNA. NGS technology (DNA-seq or RNA-seq) is used to determine such sequences in biological samples.
The interplay and dependencies between genes and other molecules can be analysed by bioinformatic mathematical modelling of networks.
The analysis of high-throughput gene expression data is used to identify molecular patterns/signatures (i.e. genes expressed up or down by a disease). Such signatures can be used for diagnosis or prognosis of the further progression of the disease. Gene expression data are nowadays mostly measured with microarrays or with NGS (RNA-seq).