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Novel Synthetic Long-Read Methods for Structural Variant Discovery and Transcriptomic Assembly.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Novel Synthetic Long-Read Methods for Structural Variant Discovery and Transcriptomic Assembly./
作者:	Meleshko, Dmitry.
面頁冊數:	1 online resource (116 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Bioinformatics. -
電子資源:	click for full text (PQDT)
ISBN:	9798381187298

Novel Synthetic Long-Read Methods for Structural Variant Discovery and Transcriptomic Assembly.
Meleshko, Dmitry.

Novel Synthetic Long-Read Methods for Structural Variant Discovery and Transcriptomic Assembly. - 1 online resource (116 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--Weill Medical College of Cornell University, 2024.

Includes bibliographical references

Synthetic long reads (SLRs) data is a type of sequencing data that adds long-range information to conventional short reads called barcodes. Reads with the same barcodes tend to cluster together when aligned to the reference genome. Usually, downstream applications benefit from long-range information encoded in reads, but in the case of SLRs, this long-range information is hard to utilize due to its unique nature. For a long time, applications of SLRs in computational biology were limited by structural variation (SV) detection of the events with the size greater than 50 kbp and to some extent whole-genome assembly of diploid genomes. Potentially, SLRs data is a powerful tool that can replace short reads or even long reads in multiple applications, but the lack of computational methods for this type of sequencing data limits its spread. To address this problem, we aim to develop several novel computational methods for processing SLR data, that solve core computational biology problems.This thesis presents three methods designed specifically for SLR data: (1) Novel-X - a tool for novel sequence insertions detection using SLR data, (2) Blackbird - a local assembly-based method for general SV calling using SLR data, and (3) rnacloudSPAdes - a method for transcriptomic assembly and isoform quantification of SLR data. These methods implement novel approaches for handling long-range information of SLR data. It allows to overcome limitations of conventional short reads, and significantly improve results compared to the best short read tools. Additionally, Blackbird is able to work with a combination of SLRs and low-coverage long reads. Given that SLRs are cheaper than long reads, it allows to decrease the cost of whole genome SV screening. Ideas behind these approaches have the potential to increase the popularity of SLRs for a wide range of applications such as SV discovery or transcriptomic assembly and inspire developments for other computational biology problems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381187298Subjects--Topical Terms:

583857
Bioinformatics.
Subjects--Index Terms:

AlgorithmsIndex Terms--Genre/Form:

554714
Electronic books.

Novel Synthetic Long-Read Methods for Structural Variant Discovery and Transcriptomic Assembly.
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