• Gizem Özbek
  • August 10th 2021

NGS Cloud is a clinical decision support system that automatically presents a list of candidate variants in the light of submitted phenotypic data, with minimal or no need for filtering to reach a clinical diagnosis. The most fundamental component of this system is the GENIUS algorithm, which performs gene-genotype-phenotype matching and variant prioritisation.


GENIUS (GENe Interactions Under Scope)

Approximately 70,000 variants in whole-exome sequencing (WES) and 5,000,000 variants are called in whole-genome sequencing (WGS) (1). These variants are then filtered and excluded according to several different parameters, such as

  • the observed allele frequency in population-based studies, 
  • the prevalence of the disorder, 
  • the phenotypic relationship(s) of the gene, 
  • the overlapping degree of the phenotype with the samples’ phenotype, 
  • the known inheritance pattern and the compatibility of the genotype, 
  • the functional evidence in the literature, 
  • the familial segregation status, 
  • whether it has been detected in similarly affected individuals, 
  • computational & predictive effects on the protein.


Here, the GENIUS algorithm has been developed to automatically apply all known relevant criteria and enables you to reach the disease-causing variant faster and easier.

Inputs of the GENIUS algorithm:


  1. Human Phenotype Ontology (HPO) terms describing human diseases with a standard vocabulary
  2. HOPE (Harmonization of Ontologies by PairEnd) terms enriched with several different ontologies.
  3. Current age and age of onset
  4. Sex


In a recent validation study, GENIUS has presented six variants during a WES analysis on average (Figure 1) (2).

Figure 1. Automatically listed candidate variant numbers


The GENIUS algorithm listed clinically relevant variants in 94.4% of the cases. These variants were either the first or among the top three in 44.1% and 73.5% of the samples, respectively2. The GENIUS algorithm enables a straightforward analysis through tens of thousands, millions of genetic variants and identify genetic changes that cause disease, usually one or two. Most importantly, it becomes more accurate by reducing human-induced errors.



  1. Alfares, A., Alsubaie, L., Aloraini, T., Alaskar, A., Althagafi, A., Alahmad, A., Rashid, M., Alswaid, A., Alothaim, A., Eyaid, W., Ababneh, F., Albalwi, M., Alotaibi, R., Almutairi, M., Altharawi, N., Alsamer, A., Abdelhakim, M., Kafkas, S., Mineta, K., Cheung, N., … Alfadhel, M. (2020). What is the right sequencing approach? Solo VS extended family analysis in consanguineous populations. BMC medical genomics, 13(1), 103.
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