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/

Global: Zendy Tools annual

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

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Because of the decreasing cost and high digital resolution, next-generationsequencing (NGS) is expected to replace the traditional hybridization-basedmicroarray technology. For genetics study, the first-step analysis of NGS datais often to identify genomic variants among sequenced samples. Severalstatistical models and tests have been developed for variant calling in NGSstudy. The existing approaches, however, are based on either conventionalBayesian or frequentist methods, which are unable to address the multiplicityand testing efficiency issues simultaneously. In this paper, we derive anoptimal empirical Bayes testing procedure to detect variants for NGS study. Weutilize the empirical Bayes technique to exploit the across-site informationamong many testing sites in NGS data. We prove that our testing procedure isvalid and optimal in the sense of rejecting the maximum number of nonnullswhile the Bayesian false discovery rate is controlled at a given nominal level.We show by both simulation studies and real data analysis that our testingefficiency can be greatly enhanced over the existing frequentist approachesthat fail to pool and utilize information across the multiple testing sites.

An empirical Bayes testing procedure for detecting variants in analysis of next generation sequencing data