Precise detection of circular and linear RNA with RNA-Seq - Szabo, Linda; Salzman, Julia; Montgomery, Stephen, 1979-; Plevritis, Sylvia; Stanford University, Program in Biomedical Informatics (2016)
The advent of RNA-Seq initially suggested that a complete and precise reconstruction of transcriptomes, with low false positive splice site identification, would be feasible and straightforward. Although numerous RNA-Seq analysis algorithms have been developed and much progress has been made on this task, recent benchmarks by multiple groups demonstrate that significant conceptual and computational improvements are needed to improve accuracy. Highlighting the insufficiency of existing algorithms, Dr. Salzman recently discovered a new class of RNA, circular RNA, which was overlooked in spite of intensive searches for novel alternative splicing using a variety of computational approaches to analyze RNA-Seq data. Simple statistical models have improved quantification of the known transcriptome and enabled the identification of circular RNA formed from annotated exons. In this work, we developed computational and statistical algorithms for accurate detection of novel splicing events from RNA-Seq. We have developed an algorithm for the identification of annotated and novel splicing events which enabled the discovery of new features of circular RNA biology. We also developed a novel split-read approach to annotation-independent junction discovery which identifies a broad range of genomic events detectable in RNA-Seq and includes the reporting of additional features for candidate junctions that are essential for downstream statistical analysis.
Submitted to the Program in Biomedical Informatics.
Thesis (Ph.D.)--Stanford University, 2016.
Rights: (c) Copyright 2016 by Linda Anne Szabo
Rights: This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Keywords: theses