Revealing the genetic background of chronic non-communicable diseases
Revealing the genetic background of chronic non-communicable diseases is a considerable challenge. The functional interpretation of NGS reads is complicated by the large number of variations and their combined effects contributing to a multifactorial disease. The suite is based on modules – created to answer specific questions. The modules can be customized according to the individual characteristics of the project.

Variant Detection and Annotation Module

The result of raw data analysis is an assembled and aligned nucleotide sequence. Further analysis starts with annotation: gene location and function need to be identified. Revealed variants are first filtered then annotated, resulting in dbSNP rs IDs, overlapping-gene accession numbers, SNP function (e.g. missense coding), etc. Module components:

  •  SNP Calling
  • CNV Detection
  • In/Del Discovery
  • Structural Variant Calling (inversion, translocation)
  • Variant Annotation
  • Variant Frequency Analysis
  • Statistics

Phenotype Prediction Module

Mapped sequences and variants need to be functionally annotated and assigned. The module performs pathway analysis, function prediction and protein-protein interaction prediction – providing genotype-phenotype associations. Module components:

  •  Variant Filtering (e.g. missense coding)
  • Filtering against public SNP, mutation databases and HapMap
  • Function Prediction
  • Protein-Protein Interaction
  • Gene Ontology
  • Variant Frequency Analysis
  • Pathway Analysis

Genome-Wide Association Analysis Module

Genetic characterization of multifactorial diseases is a complex matter. The output of genome-wide association mapping is a whole genome/exome scanning analysis for statistically strong associations between a set of SNVs/structural variants and a particular trait or disease. Module components:

  • Quality Control (statistical metrics of raw data, e.g Ti/Tv ratio, dbSNP concordance)
  • Phenotype Classification
  • Variant Frequency Analysis
  • Genotype-Phenotype Association Testing
  • Variant Classification

Data Integration Module

Data originating from different platforms or procedures (microarray, DNA/mRNA/ChIP/miRNA sequencing, proteomic methods, etc.) need to be integrated into a single holistic data warehouse for simultanous interpretation. The omics module presents data modelling in a system wide context. Module components:

  • Integrated Data Warehouse (ADD ON proteomics, metabolomics, transcriptomics etc. databases)
  • Data Mining (pattern recognition, classification, clustering)
  • Biostatistics
  • Customized Visualization
  • Variant Annotation
  • Report Generation