DAFS: Data-Adaptive Flag Method for RNA-Sequencing Data
*Nysia George, NCTR Keywords: RNA sequencing, low expression, data-adaptive Next-generation sequencing (NGS) has advanced the application of high-throughput sequencing technologies in genetic and genomic variation analysis. Whole transcriptome sequencing (RNA-seq) utilizes NGS to measure RNA levels of transcripts in a sample, and is expected to replace the microarray technology. Several statistical methods have been developed to accommodate the unique features of RNA-seq data. However, meaningful interpretation of the statistical analysis of low expression is difficult, and there is no consensus on the definition of a low expressed region. A number of factors affect the distribution of read counts for a given study and vary from study to study. Thus, an arbitrary cutoff to identify high/low expression region might be misleading. In this study, a data-adaptive approach is developed to estimate the lower bound of high expression in a given RNA-seq sample. Several RNA-seq datasets are used to demonstrate the robustness of our methodology.
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Key Dates
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April 30 - May 22, 2013
Invited Abstract Submission Open -
June 4, 2013
Online Registration Opens -
August 9 - August 23, 2013
Invited Abstract Editing -
August 23, 2013
Short Course materials due from Instructors -
August 26, 2013
Housing Deadline -
September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT -
September 16 - September 18, 2013
Marriott Wardman Park, Washington, DC