國立虎尾科技大學 |

Development of Deep Learning Models for RNA Modification Detection Using Nanopore Sequencing.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Development of Deep Learning Models for RNA Modification Detection Using Nanopore Sequencing./
作者:	Mateos, Pablo Acera.
面頁冊數:	1 online resource (125 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Deep learning. -
電子資源:	click for full text (PQDT)
ISBN:	9798381021233

Development of Deep Learning Models for RNA Modification Detection Using Nanopore Sequencing.
Mateos, Pablo Acera.

Development of Deep Learning Models for RNA Modification Detection Using Nanopore Sequencing. - 1 online resource (125 pages)

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

Thesis (Ph.D.)--The Australian National University (Australia), 2023.

Includes bibliographical references

The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted and has the potential to answer some of the fundamental questions in RNA biology. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than one RNA modification type at a time, identifying RNA modifications in individual molecules, and estimate modification stoichiometry accurately. The development of new computational techniques are a key driving force to address some of the limitations in the field and although multiple algorithmic applications for detecting and discovering RNA modifications have been developed, several challenges still exist to leverage the full potential of current experimentally produced datasets. Nanopore direct RNA sequencing (DRS) is a technology able to sequence single native RNA molecules and capture information about covalently bonded RNA modifications. This technology can be used to address some of the current limitations in the epitranscriptomic landscape. To address the current limitations, we developed CHEUI (CH3 (methylation) Estimation Using Ionic current), a two-stage deep learning method that detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. With CHEUI we developed the first deep learning algorithm able to detect several RNA modifications in individual reads, improving quantitative estimation of RNA modifications compared to previous methods, a major milestone in the field of DRS RNA modification detection. Moreover, we describe the protocol we used to train and test CHEUI, which can potentially be used to develop deep learning models to detect other RNA modifications. Finally, using CHEUI's unique capability to identify two modification types in the same sample we showed a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. We think CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.

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

Mode of access: World Wide Web

ISBN: 9798381021233Subjects--Topical Terms:

1127428
Deep learning.
Index Terms--Genre/Form:

554714
Electronic books.

Development of Deep Learning Models for RNA Modification Detection Using Nanopore Sequencing.
LDR:03478ntm a22003617 4500 001 1143888
005 20240517105019.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798381021233
035 $a (MiAaPQ)AAI30727024
035 $a (MiAaPQ)AustNatlU1885295474
035 $a AAI30727024
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Mateos, Pablo Acera. $3 1468697
245 1 0 $a Development of Deep Learning Models for RNA Modification Detection Using Nanopore Sequencing.
264 0 $c 2023
300 $a 1 online resource (125 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Jimenez, Eduardo Eyras.
502 $a Thesis (Ph.D.)--The Australian National University (Australia), 2023.
504 $a Includes bibliographical references
520 $a The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted and has the potential to answer some of the fundamental questions in RNA biology. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than one RNA modification type at a time, identifying RNA modifications in individual molecules, and estimate modification stoichiometry accurately. The development of new computational techniques are a key driving force to address some of the limitations in the field and although multiple algorithmic applications for detecting and discovering RNA modifications have been developed, several challenges still exist to leverage the full potential of current experimentally produced datasets. Nanopore direct RNA sequencing (DRS) is a technology able to sequence single native RNA molecules and capture information about covalently bonded RNA modifications. This technology can be used to address some of the current limitations in the epitranscriptomic landscape. To address the current limitations, we developed CHEUI (CH3 (methylation) Estimation Using Ionic current), a two-stage deep learning method that detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. With CHEUI we developed the first deep learning algorithm able to detect several RNA modifications in individual reads, improving quantitative estimation of RNA modifications compared to previous methods, a major milestone in the field of DRS RNA modification detection. Moreover, we describe the protocol we used to train and test CHEUI, which can potentially be used to develop deep learning models to detect other RNA modifications. Finally, using CHEUI's unique capability to identify two modification types in the same sample we showed a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. We think CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Deep learning. $3 1127428
650 4 $a Neural networks. $3 1011215
650 4 $a Signal processing. $3 561459
650 4 $a Labeling. $3 1466860
650 4 $a Chromosomes. $3 582176
650 4 $a Feature selection. $3 1413636
650 4 $a Probability. $3 667790
650 4 $a Genomes. $3 600075
650 4 $a Metabolism. $3 581368
650 4 $a Annotations. $3 1465627
650 4 $a Enzymes. $3 557408
650 4 $a Electrical engineering. $3 596380
650 4 $a Genetics. $3 578972
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0800
690 $a 0544
690 $a 0369
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The Australian National University (Australia). $3 1186624
773 0 $t Dissertations Abstracts International $g 85-06B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30727024 $z click for full text (PQDT)