ITHANET

ITHANET provides integrated databases and computational resources for curation, annotation, and analysis of genetic variation and epidemiology related to haemoglobinopathies.

Key Features:

• IthaGenes Database: Curated information on genes and variants affecting haemoglobin disorders including phenotype descriptions, relevant publications, external links, and integration with NCBI Sequence Viewer for graphical representation of variations.
• IthaCNVs: Annotation of 291 known copy number variants associated with haemoglobinopathies with filtering by breakpoint information, chromosomal positions, MLPA probes, and affected genes for molecular DNA analysis support.
• IthaPhen (Genotype-Phenotype Annotation): Computational annotation of genotype–phenotype relationships with evaluation of in silico predictors such as CADD, Eigen-PC, REVEL, and SpliceAI to inform pathogenicity and splicing effects in the context of ACMG/AMP guidelines.
• IthaScore: A gene-ranking metric that uses curated IthaGenes data with protein–protein interaction network analysis and functional enrichment analysis to identify candidate modifier genes of haemoglobinopathy phenotypes.
• IthaMaps: Epidemiological distribution maps that provide region-specific prevalence data for haemoglobinopathies.

Scientific Applications:

• Diagnostic Support: Molecular annotation of variants and CNVs to assist confirmation of diagnoses in complex haemoglobinopathy cases.
• Phenotypic Interpretation: Integration of genotype–phenotype data and in silico predictor evaluations to support variant interpretation under ACMG/AMP frameworks.
• Epidemiological Insights: Geographical distribution mapping of haemoglobinopathies to inform prevalence analyses and public health studies.
• Modifier Gene Identification: Network- and enrichment-based ranking to discover genes that modify disease severity and phenotype.

Methodology:

Combines manual curation and annotation with computational analyses including integration with NCBI Sequence Viewer, annotation and filtering of CNVs by breakpoint information, chromosomal positions, MLPA probes and affected genes, evaluation of in silico predictors (CADD, Eigen-PC, REVEL, SpliceAI) for pathogenicity and splicing, protein–protein interaction network analysis, and functional enrichment analysis.