English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

High-throughput RNA sequencing (RNA-seq) has emerged as a revolutionary andpowerful technology for expression profiling. Most proposed methods fordetecting differentially expressed (DE) genes from RNA-seq are based onstatistics that compare normalized read counts between conditions. However,there are few methods considering the expression measurement uncertainty intoDE detection. Moreover, most methods are only capable of detecting DE genes,and few methods are available for detecting DE isoforms. In this paper, aBayesian framework (BDSeq) is proposed to detect DE genes and isoforms withconsideration of expression measurement uncertainty. This expressionmeasurement uncertainty provides useful information which can help to improvethe performance of DE detection. Three real RAN-seq data sets are used toevaluate the performance of BDSeq and results show that the inclusion ofexpression measurement uncertainty improves accuracy in detection of DE genesand isoforms. Finally, we develop a GamSeq-BDSeq RNA-seq analysis pipeline tofacilitate users, which is freely available at the websitehttp://parnec.nuaa.edu.cn/liux/GSBD/GamSeq-BDSeq.html.

Detecting Differential Expression from RNA-seq Data with Expression Measurement Uncertainty