iFlow – new features available from early 2022

1) New workflows added We added new workflows covering more areas of genomic medicine:

• Somatic cancer workflows created with GATK best practices and ACMG recommendations workflows grouped in 27 areas: Acute Myeloid Leukemia | Acute Promyelocytic Leukemia | Breast Cancer | Cancer Chronic Lymphocytic Leukemia | Chronic Myeloid Leukemia | Chronic Myelomonocytic | Leukemia | Colorectal Cancer | Essential Thrombocythemia | Female Reproductive Organ Cancer | Gastric Adenocarcinoma |Glioblastoma | Hematologic Cancer | Her2-receptor Positive Breast Cancer | Lung Non-small Cell | Carcinoma | Melanoma | Myelofibrosis | Myeloid And Lymphoid Neoplasms With Eosinophilia And Abnormalities Of PDGFRA, PDGFRB, And FGFR1s | Myeloproliferative Neoplasm | Myxoid Liposarcoma | Neuroblastoma | Oropharynx Cancer | Ovarian Sex-cord Stromal Tumor | Skin Melanoma | Synovial Sarcoma | Transitional Cell Carcinoma
• Structural variants (CNV) research analysis workflow for whole genome sequencing data (WGS): Structural variants calling; identifies, annotates, and evaluates pathogenicity (ACMG recommendations) of germline structural and copy number variants.Read more about the available analytical areas here.

2) A phenotype validation and gene prioritization module prior to the analysis run We have added new features that accelerate the process of selecting genes for the analysis in the germline hereditary disorders workflows. Now, when the user enters a patient phenotype as an input, our engine will automatically validate it and create a list of genes related to this phenotype. By expanding the patient's phenotype description, the algorithm will match an increasingly relevant list of genes associated with the phenotype. Each type of input will get the appropriate score, based on which our system will prioritize the genes selected for analysis. Phenotype match (score) is a percent of the maximum possible score that is attributed to a gene. For each HPO term and/or phenotype description provided by a physician, the algorithm traverses the phenotype ontology tree searching for a match with each gene and assigning a score based on the distance between the original term and the term that matches the gene. For specific genes provided by a physician, the phenotype match is assigned to be 75%. For specific diseases provided by a physician, the phenotype match is assigned to be 50%. For genes from the gene panel, phenotype match is assigned to be 30%. The highest score is given to the genes most associated with the patient's phenotype and the lowest to those associated least. A use-case example: Based on the following phenotypic description entered as an input for a hereditary disorders analysis: Short stature, Failure to thrive, Narrow mouth, Narrow forehead, Anteverted nares, Low-set ears, Optic atrophy, Developmental cataract iFlow created a panel of genes with assigned scores indicating the match with the phenotype: Once the user accepts the panel, only genes associated with the phenotype will be analyzed - in this case, 959 genes.