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Statistical Analysis Supports Pervasive RNA Subcellular Localization and Alternative UTR Regulation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Analysis Supports Pervasive RNA Subcellular Localization and Alternative UTR Regulation./
作者:	Bierman, Robert Forrest.
面頁冊數:	1 online resource (101 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
標題:	Embryos. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30462705click for full text (PQDT)
ISBN:	9798379654320

Statistical Analysis Supports Pervasive RNA Subcellular Localization and Alternative UTR Regulation.
Bierman, Robert Forrest.

Statistical Analysis Supports Pervasive RNA Subcellular Localization and Alternative UTR Regulation. - 1 online resource (101 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

Understanding the subcellular localization of RNA molecules across different cell-types and tissues provides a window into previously unknown biology and disease. Low-plex RNA imaging technologies, where only a handful of RNA species could be observed in a single experiment, have been transformed by novel spatially resolved imaging and sequencing techniques which can simultaneously investigate thousands of genes. Named "Method of the Year" in 2019 by Nature Methods, the field of spatially resolved transcriptomics continues to accelerate in resolution, sensitivity, and ease of use. Large and exciting datasets have been produced from these efforts, but have been underutilized to discover subcellular RNA localization.We introduce a novel statistical framework to identify RNA subcellular localization patterns in publicly available datasets. We detect that a majority of investigated genes have non-random RNA distribution, and often differential distribution between cell-types.We've combined our analyses of spatial datasets with standard, spatially-naive, single-cell RNA sequencing to further identify genes which have subcellular localization patterning correlated with RNA isoform usage to generate testable hypotheses which we've collaborated with others to successfully validate.Spatial Transcriptomics is rapidly evolving, and we expect that our contribution of a flexible and statistically-sound algorithm will be applicable for the impending influx of spatial datasets.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379654320Subjects--Topical Terms:

2152917
Embryos.
Index Terms--Genre/Form:

542853
Electronic books.

Statistical Analysis Supports Pervasive RNA Subcellular Localization and Alternative UTR Regulation.
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