Rsubread: mapping and quantification of RNA-seq data Wei Shi Olivia Newton-John Cancer Research Institute, Melbourne, Australia Abstract Rsubread is a popular Bioconductor package that was developed for mapping RNA-seq reads and then counting the reads to genomic features including genes and exons. It also includes a recently developed new function, called CellCounts, for quantifying scRNA-seq data generated by the 10X Chromium platform. In this talk, I will describe the algorithms implemented in the mapping and counting functions in Rsubread and present the results of comparing to competing tools, including the CellRanger program developed by 10X for the quantification of their single-cell data.
Short talk: RnaSeqSampleSize: Real data based sample size estimation for RNA-Seq with complex design
RnaSeqSampleSize: Real data based sample size estimation for RNA-Seq with complex design Shilin Zhao,Yu Shyr Vanderbilt University Medical Center Abstract One of the most important components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. We developed a sample size and power estimation package named RnaSeqSampleSize, which utilized the genes expression patterns in real RNA-seq data with similar conditions to provide a more accurate and reliable estimation. The package was published two years ago and widely used by investigators in different areas.
Short talk: GenomicSuperSignature: interpretation of RNA-seq experiments through robust, efficient comparison to public databases
GenomicSuperSignature: interpretation of RNA-seq experiments through robust, efficient comparison to public databases Sehyun Oh,Ludwig Geistlinger,Marcel Ramos,Vincent James Carey,Casey Greene,Levi Waldron,Sean Davis The City University of New York Abstract PURPOSE: Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. Existing methods for leveraging these public resources have focused on the reanalysis of existing data or analysis of new datasets independently. We present a novel approach to interpreting new transcriptomic datasets by near-instantaneous comparison to public archives without high-performance computing requirements.
Bambu - Context-Aware Quantification of Transcript Expression with Long Read RNA-Seq Andre Sim,Ying Chen,Jonathan Goeke Genome Institute of Singapore, A*STAR Abstract Here we present bambu, a computational method for context aware quantification of transcript expression from long read RNA-Seq data. Bambu utilizes two modules: (1) generates a set of curated annotations across all samples of interest, (2) then quantifies isoform expression with an expectation maximisation algorithm that estimates full-length and partial-length read support per transcript.