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Published byMarybeth Short Modified over 8 years ago
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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
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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?
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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
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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
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