Querying in a versatile way variation patterns in the large diversity of molecular building blocks Proposal for breaking down this requirement in Functional Feature Modules across Functional Domains V1 on 6-jan-2014 by Emmanuel Van der Stuyft 1

LTD-FDT? Functions overview Defining a limited number of models to capture at a summary level ◦ The identity of a specific molecular building block ◦ Core features of interest about that molecular building block In such a way that  A broad range of data types can be flexibly uploaded into a fitting model  Versatility and performance on querying is adequately facilitated for Providing a querying framework In analogy with cohort select query fcty at phenotypical level ◦ To reach exactly into the molecular building blocks of interest  from the overall picture via a concept tree-like paradigm  Iteratively extending reach into new data models are these are being added by DM-FDT ◦ To apply the appropriate filters on features of these building blocks ◦ To combine the above components with adequate boolean logic ◦ To pass on relevant query results to the cohort selection list ◦ To save query definitions for later reuse 2 DM-FDT BQ-FDT DCL-FDT DM-FDT?

Data models to fit common themes ◦ When annotated name refers to 1 unique building block  Then : Building block name (eg transcript), (chromos. Pos ref?) Feature measure type (eg abundance), value, unit; … ◦ When annotation name refers to a variation of building blocks  Then : Building block name (eg SNP X), (chromos. Pos ref?) Variant identity (possibly multiple), feature measure type (eg abundance, quality,.. ), value, unit;... ◦ When non-annotated variation (eg tumor vs germline var analysis)  Then: Chromosome position/range reference, Variant string descriptor (possibly multiple), feature measure type (eg abundance, quality,.. ), value, unit;... ◦ When trying to capture themes in variation with similar effects over a broader chromosome position range, then... ◦ When coming across other types of variation that cannot be captured in the above models  Then: Create an adapted one... 3

To reach from the overall picture exactly into the molecular building blocks of interest Selecting what molecular profiling data to look at ◦ Via Facetted search? down through hierarchy layers:  Link into subject selection already made as part of cohort definition  Possibly select a related subject type from which to investigate multidim. data  Select sample source and tissue type  Select visit and timepoint  Select data type (eg mRNA)  Select technology (eg RNAseq)  Select platform (eg Illumina) Need to upgrade molecular profiling query capabilities to match phenotypical querying capabilities 4