Tag Archives: microarray

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Strategies to Identify Natural Antisense Transcripts

Strategies to Identify Natural Antisense Transcripts

Natural antisense transcripts, originally considered as transcriptional noises arising from so-called “junk DNA”, are recently recognized as important modulators for gene regulation. They are prevalent in nearly all realms of life and have been found to modulate gene expression positively ...

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University of Cambridge researchers describe new method for revealing short-term changes in gene expression, alterations in RNA decay rates, and the kinetics of RNA processing

University of Cambridge researchers describe new method for revealing short-term changes in gene expression, alterations in RNA decay rates, and the kinetics of RNA processing

Cellular RNA levels are orchestrated by highly regulated processes involving RNA synthesis (transcription), processing (e.g., splicing, polyadenylation, transport), and degradation. Profiling these changes provides valuable information on the regulation of gene expression. Total cellular RNA is a poor template for ...

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Mapping eQTLs With RNA-Seq reveals a more comprehensive set of eQTLs and illuminates underlying molecular consequence missed by microarrays

Mapping eQTLs With RNA-Seq reveals a more comprehensive set of eQTLs and illuminates underlying molecular consequence missed by microarrays

Studies attempting to functionally interpret complex-disease susceptibility loci by GWAS and eQTL integration have predominantly employed microarrays to quantify gene-expression. RNA-Seq has the potential to discover a more comprehensive set of eQTLs and illuminate the underlying molecular consequence. Researchers from ...

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Pseudotime Estimation – Deconfounding Single Cell Time Series

Pseudotime Estimation – Deconfounding Single Cell Time Series

Repeated cross-sectional time series single cell data confound several sources of variation, with contributions from measurement noise, stochastic cell-to-cell variation and cell progression at different rates. Time series from single cell assays are particularly susceptible to confounding as the measurements ...

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GOexpress – an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data

GOexpress – an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data

Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine ...

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Upcoming Webinar – Integrating Probes and Reads

Upcoming Webinar – Integrating Probes and Reads

  Microarray and Next-Generation Sequencing don’t have to live in separate worlds. In this complimentary webinar, you will learn how to integrate your microarray and RNA-Seq expression data to unveil gene expression patterns that are common or unique to each ...

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