Project description:Genomic analyses have redefined the molecular subgrouping of pediatric acute lymphoblastic leukemia (ALL). Molecular subgroups guide risk-stratification and targeted therapies, but outcomes of recently identified subtypes are often unclear, owing to limited cases with comprehensive profiling and cross-protocol studies. We developed a machine learning tool (ALLIUM) for the molecular subclassification of ALL in retrospective cohorts as well as for up-front diagnostics. ALLIUM uses DNA methylation and gene expression data from 1131 Nordic ALL patients to predict 17 ALL subtypes with high accuracy. ALLIUM was used to revise and verify the molecular subtype of 281 B-cell precursor ALL (BCP-ALL) cases with previously undefined molecular phenotype, resulting in a single revised subtype for 81.5% of these cases. Our study shows the power of combining DNA methylation and gene expression data for resolving ALL subtypes and provides a comprehensive population-based retrospective cohort study of molecular subtype frequencies in the Nordic countries.
Project description:Most proteogenomic approaches for mapping single amino acid polymorphisms (SAPs) require construction of a sample-specific database containing protein variants predicted from the next-generation sequencing (NGS) data. We present a new strategy for direct SAP detection without relying on NGS data. Among the 348 putative SAP peptides identified in an industrial yeast strain, 85.6% of SAP sites were validated by genomic sequencing.
Project description:Allium sativum (garlic) is considered the source of the diverse beneficial effects on thrombosis, heart disease, diabetes, nerve disorders and cancer, as well as being circulatory strengthening. We describe the global changes in transcriptomic activity from a short period of using a moderate amount of fresh garlic and its potential link to some of the beneficial effects.