The Center for Pediatric Genomic Medicine has developed novel software for genome sequence analysis. The software is free for academic, research use. To download the software, visit the genome software portal. For all other uses, please email email@example.com for license information.
Rapid Understanding of Nucleotide variant Effect Software (RUNES)
RUNES is a multi-stage analysis pipeline for annotating and classifying human nucleotide variation detected through short read alignment. RUNES assesses the functional impact of variants through in silico prediction tools and comparison to external databases. RUNES is a high-throughput application that can fully characterize a whole human genome sequence in 30 minutes.
RUNES characterization uses a variety of software and data from both internal and external sources including novel splice impact and transcript context evaluation, ENSEMBL Variant Effect Predictor (VEP), NCBI’s dbSNP, ClinVar and Biosystems databases, Online Mendelian Inheritance in Man (OMIM), Human Gene Mutation Database (HGMD/GenomeTrax), University of Washington’s Exome Variant Server (EVS), the Broad Institute’s Exome Aggregation Consortium (ExAC) database and the Catalog of Somatic Mutations in Cancer (COSMIC).
The CPGM Variant Warehouse database stores the genome sequencing results for over 3800 patients, including 170 million nucleotide variants that have been characterized by the RUNES pipeline. In addition to presenting detailed information on each variant, the warehouse allows researchers to search and export variants by gene, position and by variant category.
The Variant Warehouse also stores a minor allele frequency for all variants in the warehouse. This frequency value records how often a variant has been seen in the patient population including how many patients were heterozygous or homozygous for the variant. The minor allele frequency is invaluable in downstream analysis since it allows researchers to easily identify rare and common variants. The allele frequency for every variant in the warehouse is recalculated four times a day using a distributed Hadoop Map/Reduce job that sifts through over 3 billion variant calls in 30 minutes.
Symptom and Sign Assisted Genome Analysis
SSAGA is a novel clinico-pathologic correlation tool that matches clinical features to diseases and disease genes. This allows physicians to limit their genome analysis to genes that are relevant to patient symptoms in accord with published guidelines for genetic testing in children.
Version 1 – TaGSCAN
SSAGA version 1.0 was developed to support the TaGSCAN clinical diagnostic test. It has a menu of 227 SNOMED-CT terms, arranged in 9 categories, to map to 591 well-established recessive diseases with known causal genes. Phenotype-to-disease-to-gene mapping was informed by Gene Reviews, Online Mendelian Inheritance in Man (OMIM) Clinical Synopsis, Mitocarta and expert physician reviewers.
Version 2 – STAT-Seq, whole genome and whole exome sequencing
SSAGA version 2.0 has been updated to meet the needs of whole genome and whole exome sequencing by expanding to map symptoms to over 4200 diseases and genes. SSAGA v2 is built using the Human Phenotype Ontology (HPO) and catalogs symptoms for both research and clinical patients.
VIKING (Variant Integration and Knowledge Interpretation in Genomes)
VIKING is software used by clinical and research analysts to interpret patient sequencing results. VIKING integrates patient results with RUNES classifications and SSAGA symptoms and candidate genes to provide a powerful interpretation and reporting tool. VIKING allows users to dynamically filter variants by individual clinical features, disease, genes, assigned ACMG-type pathogenicity category,variant warehouse minor allele frequency, genotype and inheritance pattern. VIKING enables a provisional molecular diagnosis to be determined in as little as a few seconds and allows data mark-up, the saving of user sessions and export of data in formats suitable for inclusion in diagnostic reports.
Astrolabe – Precision Medicine with Pharmacogenomics
Patient genotypes are increasingly used to guide drug choice and dosing, however they are often limited by assay speed and scale. To address this problem, in collaboration with CMH's Clinical Pharmacology Department, we developed Constellation. Using current whole genome technology we are able to identify the most likely set of haplotypes from unphased VCF files for CYP2D6. We continue to work to expand the number of gene loci as well as improve the gene nomenclatures.