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MetaSRA: normalized human sample-specific metadata for the Sequence Read Archive

Falcon: Scaling up hands-off crowdsourced entity matching to build cloud services

Chromatin module inference on cellular trajectories identifies key transition points and poised epigenetic states in diverse developmental processes

SCnorm: robust normalization of single-cell RNA-seq data


scDD is an R package to identify genes with distributional changes across conditions in a single-cell RNA-seq experiment

Screening breast MRI outcomes in routine clinical practice: comparison to BI-RADS benchmarks

Towards interactive debugging of rule-based entity matching


scPattern is an R package to identify and classify gene expression changes in ordered single-cell RNA-seq experiments


Regulatory interaction prediction for promoters and long-range enhancers

MetaSRA: normalized metadata for the Sequence Read Archive

MetaSRA is an annotation/re-coding of sample-specific metadata in the Sequence Read Archive using biomedical ontologies. Currently, MetaSRA maps biological samples to biologically relevent terms in the Disease Ontology, Experimental Factor Ontology, Cell Ontology, Uberon, and Cellosaurus.

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