NmRF: identification of multispecies RNA 2’-O-methylation modification sites from RNA sequences

Abstract 2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Briefings in bioinformatics Jg. 23; H. 1
Hauptverfasser:	Ao, Chunyan, Zou, Quan, Yu, Liang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Oxford University Press 17.01.2022 Oxford Publishing Limited (England)
Schlagworte:	Algorithms Base Sequence Biological activity Computational Biology - methods Gene sequencing Humans Hydroxyl groups Learning algorithms Machine Learning Methylation Methyltransferase miRNA Post-transcription Prediction models Ribonucleic acid RNA RNA - genetics RNA modification tRNA light gradient boosting random forest RNA feature extraction 2’-O-methylation sites
ISSN:	1467-5463, 1477-4054, 1477-4054
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Abstract 2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.
AbstractList	2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF. Abstract 2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF. 2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.
Author	Zou, Quan Yu, Liang Ao, Chunyan
Author_xml	– sequence: 1 givenname: Chunyan orcidid: 0000-0002-3008-6357 surname: Ao fullname: Ao, Chunyan email: acy196707@163.com – sequence: 2 givenname: Quan surname: Zou fullname: Zou, Quan email: zouquan@nclab.net – sequence: 3 givenname: Liang orcidid: 0000-0002-8351-3332 surname: Yu fullname: Yu, Liang email: lyu@xidian.edu.cn
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/34850821$$D View this record in MEDLINE/PubMed
BookMark	eNp90c1KAzEQB_AgFW2rJ--yIIggq_nqJvUmYlUoLYieQ5JNMGV3UzfZQ2--hq_nkxjbiiAiOSSH30yG_wxAr_GNAeAIwQsEx-RSOXWplFSUwx3QR5SxnMIR7X29C5aPaEH2wSCEBYQYMo72wD6hfAQ5Rn0gZ_Xj5CpzpWmis07L6HyTeZvVXRVdWBrtTMgeZ9cZ_nh7z-d5beLLqtqw2pc_NcHFJG3r6zUP5rUzjTbhAOxaWQVzuL2H4Hly-3Rzn0_ndw8319NcY8ZjzpG1tsCGWsw54UQjYpWiiJE0f1nocYkkUTghy8YaGUO4Le24lIxiBRMbgrNN32Xr09chitoFbapKNsZ3QeACjjCBmNFET37Rhe_aJk2XVDopKIKSOt6qTtWmFMvW1bJdie_wEkAboFsfQmus0C6uw4itdJVAUHwtSKQFie2CUs35r5rvtn_r04323fJf-Am6fZ_7
CitedBy_id	crossref_primary_10_1016_j_ijbiomac_2025_145088 crossref_primary_10_1093_bib_bbaf332 crossref_primary_10_1016_j_jmb_2025_168978 crossref_primary_10_1016_j_omtn_2022_10_004 crossref_primary_10_3389_fgene_2023_1254827 crossref_primary_10_1016_j_jbc_2024_107140 crossref_primary_10_1093_bib_bbad476 crossref_primary_10_1016_j_csbj_2022_08_053 crossref_primary_10_3390_foods12071498 crossref_primary_10_1093_nar_gkad818 crossref_primary_10_3389_fgene_2023_1133775 crossref_primary_10_3390_ijms232113493 crossref_primary_10_3389_fgene_2022_1012828 crossref_primary_10_3389_fgene_2025_1608490 crossref_primary_10_1007_s11704_022_2559_6 crossref_primary_10_3389_fgene_2023_1165765 crossref_primary_10_59717_j_xinn_life_2024_100112 crossref_primary_10_1016_j_compbiomed_2023_107065 crossref_primary_10_3390_diagnostics13142465 crossref_primary_10_1016_j_ymeth_2022_10_008 crossref_primary_10_3389_fmicb_2023_1170785 crossref_primary_10_1016_j_csbj_2025_03_024 crossref_primary_10_1093_bioinformatics_btaf417 crossref_primary_10_3389_fgene_2022_1069558 crossref_primary_10_1016_j_ymeth_2023_08_012 crossref_primary_10_1021_acsomega_5c01924 crossref_primary_10_3390_ijms241310854 crossref_primary_10_1016_j_ymeth_2022_02_009 crossref_primary_10_2174_1574893618666230516144641 crossref_primary_10_3390_genes15080996 crossref_primary_10_1016_j_compbiomed_2022_105918 crossref_primary_10_1093_nar_gkac830 crossref_primary_10_1016_j_ymeth_2023_01_007 crossref_primary_10_1093_bib_bbac573 crossref_primary_10_1093_bib_bbac571 crossref_primary_10_3389_fmed_2023_1052923 crossref_primary_10_1016_j_compbiomed_2022_105577 crossref_primary_10_1016_j_compbiomed_2022_106523 crossref_primary_10_1016_j_ijbiomac_2024_130180 crossref_primary_10_1109_TCBB_2023_3237769 crossref_primary_10_1016_j_ymeth_2022_03_007 crossref_primary_10_3389_fgene_2023_1157021 crossref_primary_10_1016_j_ymeth_2024_11_004 crossref_primary_10_1093_bib_bbae601 crossref_primary_10_1016_j_ijbiomac_2023_124247 crossref_primary_10_1371_journal_pcbi_1010404
Cites_doi	10.1155/2017/7049406 10.1261/rna.069112.118 10.1093/bib/bby127 10.1093/nar/gkz779 10.1093/nar/gkx1076 10.3390/e21090897 10.1016/j.neucom.2020.12.068 10.1016/j.cub.2005.07.029 10.1186/s12864-019-5654-9 10.1021/acschemneuro.7b00490 10.2174/1574893614666190902154332 10.1093/nar/gks698 10.1093/nar/gkw810 10.1093/bioinformatics/btab463 10.1093/bib/bbz120 10.1007/s11704-019-8208-z 10.1093/nar/gkw104 10.2174/1574893615999200503030350 10.2174/1574893615666200120103948 10.1093/bioinformatics/bty824 10.1038/ni.1979 10.1016/j.knosys.2020.106254 10.1186/1471-2164-9-S2-S22 10.1093/nar/gkab124 10.1021/acs.jcim.9b01012 10.2174/1574893615999200711165743 10.1007/s00521-019-04569-z 10.1093/bioinformatics/btaa914 10.1371/journal.pcbi.1008696 10.1038/s41467-020-15493-5 10.2217/epi-2019-0321 10.2174/1574893614666190919103752 10.1038/nbt.3437 10.1089/cmb.2018.0004 10.1093/bib/bbv033 10.1093/bioinformatics/bts565 10.3389/fgene.2021.639461 10.1093/nar/gkx449 10.1016/j.ygeno.2020.08.016 10.1093/nar/gkab122 10.1093/bib/bbz049 10.1504/IJDMB.2013.056078 10.1093/bioinformatics/btz694 10.1093/nar/gkx934 10.1093/bib/bbz112 10.3389/fbioe.2020.00134 10.1093/bib/bbz081 10.1016/j.ymthe.2021.04.004 10.1093/bib/bbz041 10.1074/mcp.RA118.001169 10.1093/bib/bbab244 10.1016/S0968-0004(02)02109-6 10.1093/nar/gkaa755 10.1186/s12864-018-4928-y 10.1016/S0300-9084(02)01402-5 10.1093/bib/bbz125 10.1016/j.ygeno.2016.05.003 10.1093/bfgp/elaa023 10.1093/nar/gkaa258 10.1016/S0092-8674(02)00718-3 10.3389/fbioe.2020.584807 10.1093/bib/bbaa367 10.1093/nar/gkaa790 10.1093/bioinformatics/bty112 10.1016/j.jtbi.2018.12.034 10.1155/2015/861402 10.1016/j.inffus.2021.02.015 10.1016/0167-4781(94)90250-X 10.2174/1574893614666191114123453 10.1016/j.jtbi.2018.11.012 10.1186/s12859-019-3265-8 10.1093/bioinformatics/btab169 10.1002/ctm2.432 10.1186/s12864-019-6019-0
ContentType	Journal Article
Copyright	The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2021 The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
Copyright_xml	– notice: The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2021 – notice: The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. – notice: The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QO 7SC 8FD FR3 JQ2 K9. L7M L~C L~D P64 RC3 7X8
DOI	10.1093/bib/bbab480
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Biotechnology Research Abstracts Computer and Information Systems Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts Genetics Abstracts MEDLINE - Academic
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Genetics Abstracts Biotechnology Research Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Engineering Research Database Advanced Technologies Database with Aerospace Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional MEDLINE - Academic
DatabaseTitleList	MEDLINE Genetics Abstracts CrossRef MEDLINE - Academic
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Biology
EISSN	1477-4054
ExternalDocumentID	34850821 10_1093_bib_bbab480 10.1093/bib/bbab480
Genre	Research Support, Non-U.S. Gov't Journal Article
GroupedDBID	--- -E4 .2P .I3 0R~ 1TH 23N 2WC 36B 4.4 48X 53G 5GY 5VS 6J9 70D 8VB AAGQS AAHBH AAIJN AAIMJ AAJKP AAJQQ AAMDB AAMVS AAOGV AAPQZ AAPXW AARHZ AAUQX AAVAP AAVLN ABDBF ABEJV ABEUO ABGNP ABIXL ABNKS ABPQP ABPTD ABQLI ABWST ABXVV ABXZS ABZBJ ACGFO ACGFS ACGOD ACIWK ACPRK ACUFI ACUHS ACUXJ ACYTK ADBBV ADEYI ADFTL ADGKP ADGZP ADHKW ADHZD ADOCK ADPDF ADQBN ADRDM ADRTK ADVEK ADYVW ADZTZ ADZXQ AECKG AEGPL AEGXH AEJOX AEKKA AEKSI AELWJ AEMDU AEMOZ AENEX AENZO AEPUE AETBJ AEWNT AFFZL AFGWE AFIYH AFOFC AFRAH AGINJ AGKEF AGQXC AGSYK AHGBF AHMBA AHQJS AHXPO AIAGR AIJHB AJEEA AJEUX AKHUL AKVCP AKWXX ALMA_UNASSIGNED_HOLDINGS ALTZX ALUQC ALXQX AMNDL ANAKG APIBT APWMN ARIXL AXUDD AYOIW AZVOD BAWUL BAYMD BEYMZ BHONS BQDIO BQUQU BSWAC BTQHN C1A C45 CAG CDBKE COF CS3 CZ4 DAKXR DIK DILTD DU5 D~K E3Z EAD EAP EAS EBA EBC EBD EBR EBS EBU EE~ EJD EMB EMK EMOBN EST ESX F5P F9B FHSFR FLIZI FLUFQ FOEOM FQBLK GAUVT GJXCC GROUPED_DOAJ GX1 H13 H5~ HAR HW0 HZ~ IOX J21 JXSIZ K1G KBUDW KOP KSI KSN M-Z M49 MK~ ML0 N9A NGC NLBLG NMDNZ NOMLY NU- O0~ O9- OAWHX ODMLO OJQWA OK1 OVD OVEED P2P PAFKI PEELM PQQKQ Q1. Q5Y QWB RD5 RPM RUSNO RW1 RXO SV3 TEORI TH9 TJP TLC TOX TR2 TUS W8F WOQ X7H YAYTL YKOAZ YXANX ZKX ZL0 ~91 77I AAYXX CITATION ROX CGR CUY CVF ECM EIF NPM 7QO 7SC 8FD FR3 JQ2 K9. L7M L~C L~D P64 RC3 7X8
ID	FETCH-LOGICAL-c278t-81fff62e4f288383c13fbb4173463d6c9d1a3b2ffff79c1ee38fdf9da742b0173
IEDL.DBID	TOX
ISICitedReferencesCount	49
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000763000800177&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1467-5463 1477-4054
IngestDate	Fri Sep 05 13:17:45 EDT 2025 Mon Oct 06 16:59:24 EDT 2025 Mon Jul 21 05:59:08 EDT 2025 Sat Nov 29 05:43:27 EST 2025 Tue Nov 18 22:08:04 EST 2025 Fri May 23 09:42:25 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	light gradient boosting random forest RNA feature extraction 2’-O-methylation sites
Language	English
License	This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c278t-81fff62e4f288383c13fbb4173463d6c9d1a3b2ffff79c1ee38fdf9da742b0173
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-3008-6357 0000-0002-8351-3332
PMID	34850821
PQID	2626200231
PQPubID	26846
ParticipantIDs	proquest_miscellaneous_2605230274 proquest_journals_2626200231 pubmed_primary_34850821 crossref_citationtrail_10_1093_bib_bbab480 crossref_primary_10_1093_bib_bbab480 oup_primary_10_1093_bib_bbab480
PublicationCentury	2000
PublicationDate	2022-01-17
PublicationDateYYYYMMDD	2022-01-17
PublicationDate_xml	– month: 01 year: 2022 text: 2022-01-17 day: 17
PublicationDecade	2020
PublicationPlace	England
PublicationPlace_xml	– name: England – name: Oxford
PublicationTitle	Briefings in bioinformatics
PublicationTitleAlternate	Brief Bioinform
PublicationYear	2022
Publisher	Oxford University Press Oxford Publishing Limited (England)
Publisher_xml	– name: Oxford University Press – name: Oxford Publishing Limited (England)
References	Wang (2022012000302551600_ref72) 2020; 60 Long (2022012000302551600_ref63) 2020; 15 Incarnato (2022012000302551600_ref8) 2017; 45 Wang (2022012000302551600_ref11) 2008; 9 Zhang (2022012000302551600_ref69) 2020; 18 Li (2022012000302551600_ref65) 2020; 22 Cai (2022012000302551600_ref36) 2021; 22 Lv (2022012000302551600_ref66) 2021 Yu (2022012000302551600_ref25) 1997; 3 Tahir (2022012000302551600_ref30) 2019; 465 Ao (2022012000302551600_ref35) 2020; 112 Zou (2022012000302551600_ref33) 2020; 21 Dong (2022012000302551600_ref67) 2020; 14 Yu (2022012000302551600_ref71) 2020; 15 Ding (2022012000302551600_ref79) 2020; 23 Shang (2022012000302551600_ref53) 2021; 434 Zhao (2022012000302551600_ref9) 2015; 2015 Zeng (2022012000302551600_ref2) 2016; 17 Zuo (2022012000302551600_ref15) 2020; 15 Shen (2022012000302551600_ref70) 2019; 462 Chen (2022012000302551600_ref87) 2021; 37 Yin (2022012000302551600_ref19) 2020; 48 Zhou (2022012000302551600_ref44) 2016; 44 Luo (2022012000302551600_ref4) 2020; 21 Li (2022012000302551600_ref3) 2018; 46 Ajuh (2022012000302551600_ref26) 1994; 1219 Zou (2022012000302551600_ref34) 2019; 25 Wang (2022012000302551600_ref76) 2021; 22 Bachellerie (2022012000302551600_ref7) 2002; 84 Zhou (2022012000302551600_ref31) 2019; 20 Ke (2022012000302551600_ref54) 2017 Hong (2022012000302551600_ref64) 2020; 21 Luo (2022012000302551600_ref5) 2021; 12 Xue (2022012000302551600_ref22) 2018; 9 Long (2022012000302551600_ref51) 2021; 11 Tang (2022012000302551600_ref52) 2021; 29 Tang (2022012000302551600_ref56) 2020; 21 Jiang (2022012000302551600_ref81) 2013; 8 Guo (2022012000302551600_ref37) 2020; 8 Wei (2022012000302551600_ref46) 2018; 35 Zhang (2022012000302551600_ref84) 2021; 2021 Mostavi (2022012000302551600_ref29) 2018; 2018 Lv (2022012000302551600_ref59) 2020; 8 He (2022012000302551600_ref49) 2020; 15 He (2022012000302551600_ref68) 2020; 14 Yang (2022012000302551600_ref28) 2018; 25 Jiang (2022012000302551600_ref18) 2019; 20 Yang (2022012000302551600_ref57) 2020; 21 Zuest (2022012000302551600_ref23) 2011; 12 Chen (2022012000302551600_ref42) 2021; 49 Decatur (2022012000302551600_ref20) 2002; 27 Yang (2022012000302551600_ref50) 2021; 75 Cai (2022012000302551600_ref43) 2021; 37 Ju (2022012000302551600_ref55) 2020; 15 Ao (2022012000302551600_ref40) 2021; 20 Ding (2022012000302551600_ref78) 2020; 204 Li (2022012000302551600_ref74) 2017; 45 Chen (2022012000302551600_ref27) 2016; 107 Chen (2022012000302551600_ref41) 2020; 21 Huang (2022012000302551600_ref82) 2020; 12 Xuan (2022012000302551600_ref1) 2018; 46 Wu (2022012000302551600_ref38) 2021 Zhao (2022012000302551600_ref48) 2018; 19 Doench (2022012000302551600_ref47) 2016; 34 Tang (2022012000302551600_ref58) 2019; 18 Lin (2022012000302551600_ref86) 2020 Kiss (2022012000302551600_ref6) 2002; 109 Wang (2022012000302551600_ref12) 2010; 5 Wang (2022012000302551600_ref16) 2020; 48 Zeng (2022012000302551600_ref17) 2018; 34 Dong (2022012000302551600_ref21) 2012; 40 Chen (2022012000302551600_ref24) 2021; 49 Zhang (2022012000302551600_ref61) 2020; 15 Lin (2022012000302551600_ref85) 2020; 21 Zhao (2022012000302551600_ref10) 2017; 2017 Chen (2022012000302551600_ref80) 2020; 11 Chen (2022012000302551600_ref39) 2020; 21 Yang (2022012000302551600_ref77) 2020; 48 Hong (2022012000302551600_ref45) 2020; 36 Yin (2022012000302551600_ref14) 2021; 49 Fu (2022012000302551600_ref32) 2012; 28 Xiong (2022012000302551600_ref75) 2021; 49 Pedregosa (2022012000302551600_ref73) 2011; 12 Li (2022012000302551600_ref13) 2005; 15 Yu (2022012000302551600_ref83) 2021; 17 Li (2022012000302551600_ref60) 2019; 21 Hong (2022012000302551600_ref62) 2020; 21
References_xml	– volume: 2017 start-page: 7049406 year: 2017 ident: 2022012000302551600_ref10 article-title: Methods of MicroRNA promoter prediction and transcription factor mediated regulatory network publication-title: Biomed Res Int doi: 10.1155/2017/7049406 – volume: 25 start-page: 205 year: 2019 ident: 2022012000302551600_ref34 article-title: Gene2vec: gene subsequence embedding for prediction of mammalian N6-Methyladenosine sites from mRNA publication-title: RNA doi: 10.1261/rna.069112.118 – volume: 21 start-page: 621 year: 2020 ident: 2022012000302551600_ref56 article-title: ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies publication-title: Brief Bioinform doi: 10.1093/bib/bby127 – volume: 48 start-page: D1042 year: 2020 ident: 2022012000302551600_ref19 article-title: VARIDT 1.0: variability of drug transporter database publication-title: Nucleic Acids Res doi: 10.1093/nar/gkz779 – volume: 46 start-page: D1121 year: 2018 ident: 2022012000302551600_ref3 article-title: Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics publication-title: Nucleic Acids Res doi: 10.1093/nar/gkx1076 – volume: 21 start-page: 14 year: 2019 ident: 2022012000302551600_ref60 article-title: Evidential decision tree based on belief entropy publication-title: Entropy doi: 10.3390/e21090897 – volume: 22 start-page: 1 year: 2020 ident: 2022012000302551600_ref65 article-title: DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences publication-title: Brief Bioinform – volume: 434 start-page: 80 year: 2021 ident: 2022012000302551600_ref53 article-title: Prediction of drug-target interactions based on multi-layer network representation learning publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.12.068 – volume: 15 start-page: 1501 year: 2005 ident: 2022012000302551600_ref13 article-title: Methylation protects miRNAs and siRNAs from a 3 '-end uridylation activity in Arabildopsis publication-title: Curr Biol doi: 10.1016/j.cub.2005.07.029 – volume: 20 year: 2019 ident: 2022012000302551600_ref18 article-title: LightCpG: a multi-view CpG sites detection on single-cell whole genome sequence data (vol 20, 306, 2019) publication-title: BMC Genomics doi: 10.1186/s12864-019-5654-9 – volume: 9 start-page: 1128 year: 2018 ident: 2022012000302551600_ref22 article-title: What contributes to serotonin-norepinephrine reuptake inhibitors' dual-targeting mechanism? The key role of transmembrane domain 6 in human serotonin and norepinephrine transporters revealed by molecular dynamics simulation publication-title: ACS Chem Nerosci doi: 10.1021/acschemneuro.7b00490 – volume: 15 start-page: 300 year: 2020 ident: 2022012000302551600_ref63 article-title: Predicting protein phosphorylation sites based on deep learning publication-title: Current Bioinformatics doi: 10.2174/1574893614666190902154332 – volume: 40 year: 2012 ident: 2022012000302551600_ref21 article-title: RTL-P: a sensitive approach for detecting sites of 2 '-O-methylation in RNA molecules publication-title: Nucleic Acids Res doi: 10.1093/nar/gks698 – volume: 45 start-page: 1433 year: 2017 ident: 2022012000302551600_ref8 article-title: High-throughput single-base resolution mapping of RNA 2 '-O-methylated residues publication-title: Nucleic Acids Res doi: 10.1093/nar/gkw810 – year: 2021 ident: 2022012000302551600_ref38 article-title: EPSOL: sequence-based protein solubility prediction using multidimensional embedding publication-title: Bioinformatics (Oxford, England) doi: 10.1093/bioinformatics/btab463 – volume: 21 start-page: 1825 year: 2020 ident: 2022012000302551600_ref62 article-title: Convolutional neural network-based annotation of bacterial type IV secretion system effectors with enhanced accuracy and reduced false discovery publication-title: Brief Bioinform doi: 10.1093/bib/bbz120 – volume: 14 start-page: 241 year: 2020 ident: 2022012000302551600_ref67 article-title: A survey on ensemble learning publication-title: Front Comp Sci doi: 10.1007/s11704-019-8208-z – volume: 44 start-page: e91 year: 2016 ident: 2022012000302551600_ref44 article-title: SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features publication-title: Nucleic Acids Res doi: 10.1093/nar/gkw104 – volume: 15 start-page: 1213 year: 2020 ident: 2022012000302551600_ref49 article-title: MRMD2.0: a python tool for machine learning with feature ranking and reduction publication-title: Current Bioinformatics doi: 10.2174/1574893615999200503030350 – volume: 15 start-page: 1036 year: 2020 ident: 2022012000302551600_ref71 article-title: Exploiting XG boost for predicting enhancer-promoter interactions publication-title: Current Bioinformatics doi: 10.2174/1574893615666200120103948 – volume: 22 start-page: 1 year: 2021 ident: 2022012000302551600_ref76 article-title: Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment publication-title: Brief Bioinform – volume: 35 start-page: 1326 year: 2018 ident: 2022012000302551600_ref46 article-title: Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty824 – volume: 12 start-page: 137 year: 2011 ident: 2022012000302551600_ref23 article-title: Ribose 2 '-O-methylation provides a molecular signature for the distinction of self and non-self mRNA dependent on the RNA sensor Mda5 publication-title: Nat Immunol doi: 10.1038/ni.1979 – volume: 204 year: 2020 ident: 2022012000302551600_ref78 article-title: Identification of drug-target interactions via dual Laplacian regularized least squares with multiple kernel fusion publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2020.106254 – volume: 9 start-page: S22 issue: Suppl 2 year: 2008 ident: 2022012000302551600_ref11 article-title: Transcription factor and microRNA regulation in androgen-dependent and -independent prostate cancer cells publication-title: BMC Genomics doi: 10.1186/1471-2164-9-S2-S22 – volume: 49 start-page: 3719 year: 2021 ident: 2022012000302551600_ref75 article-title: Modeling multi-species RNA modification through multi-task curriculum learning publication-title: Nucleic Acids Res doi: 10.1093/nar/gkab124 – volume: 60 start-page: 1876 year: 2020 ident: 2022012000302551600_ref72 article-title: Identification of highest-affinity binding sites of yeast transcription factor families publication-title: J Chem Inf Model doi: 10.1021/acs.jcim.9b01012 – volume: 15 start-page: 898 year: 2020 ident: 2022012000302551600_ref61 article-title: Review of the applications of deep learning in bioinformatics publication-title: Current Bioinformatics doi: 10.2174/1574893615999200711165743 – volume: 23 start-page: 10303 year: 2020 ident: 2022012000302551600_ref79 article-title: Identification of drug-target interactions via fuzzy bipartite local model publication-title: Neural Comput Applic doi: 10.1007/s00521-019-04569-z – volume: 2021 start-page: 6664362 year: 2021 ident: 2022012000302551600_ref84 article-title: iBLP: an XGBoost-based predictor for identifying bioluminescent proteins publication-title: Comput Math Methods Med – volume: 37 year: 2021 ident: 2022012000302551600_ref43 article-title: iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor publication-title: Bioinformatics doi: 10.1093/bioinformatics/btaa914 – volume: 17 year: 2021 ident: 2022012000302551600_ref83 article-title: Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways publication-title: PLoS Comput Biol doi: 10.1371/journal.pcbi.1008696 – volume: 11 start-page: 1675 year: 2020 ident: 2022012000302551600_ref80 article-title: The Litsea genome and the evolution of the laurel family publication-title: Nat Commun doi: 10.1038/s41467-020-15493-5 – volume: 12 start-page: 1443 year: 2020 ident: 2022012000302551600_ref82 article-title: Prediction of transcription factors binding events based on epigenetic modifications in different human cells publication-title: Epigenomics doi: 10.2217/epi-2019-0321 – volume: 15 start-page: 589 year: 2020 ident: 2022012000302551600_ref15 article-title: Analysis of the epigenetic signature of cell reprogramming by computational DNA methylation profiles publication-title: Current Bioinformatics doi: 10.2174/1574893614666190919103752 – volume: 34 start-page: 184 year: 2016 ident: 2022012000302551600_ref47 article-title: Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9 publication-title: Nat Biotechnol doi: 10.1038/nbt.3437 – volume: 25 start-page: 1266 year: 2018 ident: 2022012000302551600_ref28 article-title: iRNA-2OM: a sequence-based predictor for identifying 2 '-O-methylation sites in Homo sapiens publication-title: J Comput Biol doi: 10.1089/cmb.2018.0004 – volume: 17 start-page: 193 year: 2016 ident: 2022012000302551600_ref2 article-title: Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks publication-title: Brief Bioinform doi: 10.1093/bib/bbv033 – start-page: 2739 volume-title: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20 year: 2020 ident: 2022012000302551600_ref86 – volume: 28 start-page: 3150 year: 2012 ident: 2022012000302551600_ref32 article-title: CD-HIT: accelerated for clustering the next-generation sequencing data publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts565 – volume: 12 start-page: 639461 year: 2021 ident: 2022012000302551600_ref5 article-title: Effects of DNA methylation on TFs in human embryonic stem cells publication-title: Front Genet doi: 10.3389/fgene.2021.639461 – volume: 48 start-page: D1031 year: 2020 ident: 2022012000302551600_ref16 article-title: Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics publication-title: Nucleic Acids Res – volume: 45 start-page: W162 year: 2017 ident: 2022012000302551600_ref74 article-title: NOREVA: normalization and evaluation of MS-based metabolomics data publication-title: Nucleic Acids Res doi: 10.1093/nar/gkx449 – volume: 112 start-page: 4666 year: 2020 ident: 2022012000302551600_ref35 article-title: Prediction of antioxidant proteins using hybrid feature representation method and random forest publication-title: Genomics doi: 10.1016/j.ygeno.2020.08.016 – volume: 49 year: 2021 ident: 2022012000302551600_ref42 article-title: iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization publication-title: Nucleic Acids Res doi: 10.1093/nar/gkab122 – volume: 21 start-page: 1058 year: 2020 ident: 2022012000302551600_ref57 article-title: Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data publication-title: Brief Bioinform doi: 10.1093/bib/bbz049 – volume: 8 start-page: 282 year: 2013 ident: 2022012000302551600_ref81 article-title: Predicting human microRNA-disease associations based on support vector machine publication-title: Int J Data Min Bioinform doi: 10.1504/IJDMB.2013.056078 – volume: 36 start-page: 1037 year: 2020 ident: 2022012000302551600_ref45 article-title: Identifying enhancer–promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz694 – volume: 12 start-page: 2825 year: 2011 ident: 2022012000302551600_ref73 article-title: Scikit-learn: machine learning in Python publication-title: J Mach Learn Res – volume: 46 start-page: D327 year: 2018 ident: 2022012000302551600_ref1 article-title: RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data publication-title: Nucleic Acids Res doi: 10.1093/nar/gkx934 – volume: 21 start-page: 1676 year: 2020 ident: 2022012000302551600_ref39 article-title: Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences publication-title: Brief Bioinform doi: 10.1093/bib/bbz112 – volume: 8 start-page: 10 year: 2020 ident: 2022012000302551600_ref59 article-title: RF-PseU: a random forest predictor for RNA pseudouridine sites publication-title: Front Bioeng Biotechnol doi: 10.3389/fbioe.2020.00134 – volume: 21 start-page: 1437 year: 2020 ident: 2022012000302551600_ref64 article-title: Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning publication-title: Brief Bioinform doi: 10.1093/bib/bbz081 – volume: 29 year: 2021 ident: 2022012000302551600_ref52 article-title: mRNALocater: enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy publication-title: Mol Ther doi: 10.1016/j.ymthe.2021.04.004 – volume: 21 start-page: 1047 year: 2020 ident: 2022012000302551600_ref41 article-title: iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data publication-title: Brief Bioinform doi: 10.1093/bib/bbz041 – volume: 21 start-page: 1 year: 2020 ident: 2022012000302551600_ref33 article-title: Sequence clustering in bioinformatics: an empirical study publication-title: Brief Bioinform – volume: 18 start-page: 1683 year: 2019 ident: 2022012000302551600_ref58 article-title: Simultaneous improvement in the precision, accuracy, and robustness of label-free proteome quantification by optimizing data manipulation chains publication-title: Mol Cell Proteomics doi: 10.1074/mcp.RA118.001169 – volume: 14 start-page: 14 year: 2020 ident: 2022012000302551600_ref68 article-title: Hybritus: a password strength checker by ensemble learning from the query feedbacks of websites publication-title: Front Comp Sci – year: 2021 ident: 2022012000302551600_ref66 article-title: DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach publication-title: Brief Bioinform doi: 10.1093/bib/bbab244 – volume: 27 start-page: 344 year: 2002 ident: 2022012000302551600_ref20 article-title: rRNA modifications and ribosome function publication-title: Trends Biochem Sci doi: 10.1016/S0968-0004(02)02109-6 – volume: 49 start-page: D1233 year: 2021 ident: 2022012000302551600_ref14 article-title: INTEDE: interactome of drug-metabolizing enzymes publication-title: Nucleic Acids Res doi: 10.1093/nar/gkaa755 – volume: 19 year: 2018 ident: 2022012000302551600_ref48 article-title: Imbalance learning for the prediction of N-6-methylation sites in mRNAs publication-title: BMC Genomics doi: 10.1186/s12864-018-4928-y – volume: 84 start-page: 775 year: 2002 ident: 2022012000302551600_ref7 article-title: The expanding snoRNA world publication-title: Biochimie doi: 10.1016/S0300-9084(02)01402-5 – volume: 21 start-page: 2099 year: 2020 ident: 2022012000302551600_ref85 article-title: A novel molecular representation with BiGRU neural networks for learning atom publication-title: Brief Bioinform doi: 10.1093/bib/bbz125 – volume: 107 start-page: 255 year: 2016 ident: 2022012000302551600_ref27 article-title: Identifying 2 '-O-methylationation sites by integrating nucleotide chemical properties and nucleotide compositions publication-title: Genomics doi: 10.1016/j.ygeno.2016.05.003 – volume: 20 start-page: 1 year: 2021 ident: 2022012000302551600_ref40 article-title: Prediction of bio-sequence modifications and the associations with diseases publication-title: Brief Funct Genomics doi: 10.1093/bfgp/elaa023 – volume: 48 start-page: W436 year: 2020 ident: 2022012000302551600_ref77 article-title: NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data publication-title: Nucleic Acids Res doi: 10.1093/nar/gkaa258 – volume: 109 start-page: 145 year: 2002 ident: 2022012000302551600_ref6 article-title: Small nucleolar RNAs: an abundant group of noncoding RNAs with diverse cellular functions publication-title: Cell doi: 10.1016/S0092-8674(02)00718-3 – volume: 2018 start-page: 2394 year: 2018 ident: 2022012000302551600_ref29 article-title: Deep-2'-O-me: predicting 2'-O-methylation sites by convolutional neural networks publication-title: Annu Int Conf IEEE Eng Med Biol Soc – volume: 8 start-page: 584807 year: 2020 ident: 2022012000302551600_ref37 article-title: Discrimination of thermophilic proteins and non-thermophilic proteins using feature dimension reduction publication-title: Front Bioeng Biotechnol doi: 10.3389/fbioe.2020.584807 – volume: 22 year: 2021 ident: 2022012000302551600_ref36 article-title: ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation publication-title: Brief Bioinform doi: 10.1093/bib/bbaa367 – volume: 49 start-page: D1396 year: 2021 ident: 2022012000302551600_ref24 article-title: RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis publication-title: Nucleic Acids Res doi: 10.1093/nar/gkaa790 – volume: 5 year: 2010 ident: 2022012000302551600_ref12 article-title: Signal transducers and activators of transcription-1 (STAT1) regulates microRNA transcription in interferon gamma-stimulated HeLa cells publication-title: PLoS One – volume: 34 start-page: 2425 year: 2018 ident: 2022012000302551600_ref17 article-title: Prediction of potential disease-associated microRNAs using structural perturbation method publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty112 – volume: 18 year: 2020 ident: 2022012000302551600_ref69 article-title: AIEpred: an ensemble predictive model of classifier chain to identify anti-inflammatory peptides publication-title: IEEE/ACM Trans Comput Biol Bioinform – volume: 3 start-page: 324 year: 1997 ident: 2022012000302551600_ref25 article-title: A new method for detecting sites of 2′-O-methylation in RNA molecules publication-title: RNA – volume: 465 start-page: 1 year: 2019 ident: 2022012000302551600_ref30 article-title: iRNA-PseKNC(2methyl): identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components publication-title: J Theor Biol doi: 10.1016/j.jtbi.2018.12.034 – volume: 2015 start-page: 861402 year: 2015 ident: 2022012000302551600_ref9 article-title: MicroRNA promoter identification in Arabidopsis using multiple histone markers publication-title: Biomed Res Int doi: 10.1155/2015/861402 – volume: 75 start-page: 140 year: 2021 ident: 2022012000302551600_ref50 article-title: Risk prediction of diabetes: big data mining with fusion of multifarious physical examination indicators publication-title: Information Fusion doi: 10.1016/j.inffus.2021.02.015 – volume-title: 31st Annual Conference on Neural Information Processing Systems (NIPS) year: 2017 ident: 2022012000302551600_ref54 – volume: 1219 start-page: 89 year: 1994 ident: 2022012000302551600_ref26 article-title: Chemical secondary structure probing of two highly methylated regions in Xenopus laevis 28S ribosomal RNA publication-title: Biochim Biophys Acta doi: 10.1016/0167-4781(94)90250-X – volume: 15 start-page: 725 year: 2020 ident: 2022012000302551600_ref55 article-title: Prediction of Neddylation sites using the composition of k-spaced amino acid pairs and fuzzy SVM publication-title: Current Bioinformatics doi: 10.2174/1574893614666191114123453 – volume: 462 start-page: 230 year: 2019 ident: 2022012000302551600_ref70 article-title: Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC publication-title: J Theor Biol doi: 10.1016/j.jtbi.2018.11.012 – volume: 20 start-page: 690 year: 2019 ident: 2022012000302551600_ref31 article-title: NmSEER V2.0: a prediction tool for 2'-O-methylation sites based on random forest and multi-encoding combination publication-title: BMC Bioinformatics doi: 10.1186/s12859-019-3265-8 – volume: 37 year: 2021 ident: 2022012000302551600_ref87 article-title: MUFFIN: multi-scale feature fusion for drug–drug interaction prediction publication-title: Bioinformatics doi: 10.1093/bioinformatics/btab169 – volume: 11 year: 2021 ident: 2022012000302551600_ref51 article-title: Integrated biomarker profiling of the metabolome associated with impaired fasting glucose and type 2 diabetes mellitus in large-scale Chinese patients publication-title: Clin Transl Med doi: 10.1002/ctm2.432 – volume: 21 start-page: 672 year: 2020 ident: 2022012000302551600_ref4 article-title: Identification of methylation states of DNA regions for Illumina methylation BeadChip publication-title: BMC Genomics doi: 10.1186/s12864-019-6019-0
SSID	ssj0020781
Score	2.5199049
Snippet	Abstract 2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the... 2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2′-hydroxyl... 2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl...
SourceID	proquest pubmed crossref oup
SourceType	Aggregation Database Index Database Enrichment Source Publisher
SubjectTerms	Algorithms Base Sequence Biological activity Computational Biology - methods Gene sequencing Humans Hydroxyl groups Learning algorithms Machine Learning Methylation Methyltransferase miRNA Post-transcription Prediction models Ribonucleic acid RNA RNA - genetics RNA modification tRNA
Title	NmRF: identification of multispecies RNA 2’-O-methylation modification sites from RNA sequences
URI	https://www.ncbi.nlm.nih.gov/pubmed/34850821 https://www.proquest.com/docview/2626200231 https://www.proquest.com/docview/2605230274
Volume	23
WOSCitedRecordID	wos000763000800177&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVASL databaseName: Oxford University Press Journals Open Access Collection customDbUrl: eissn: 1477-4054 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020781 issn: 1467-5463 databaseCode: TOX dateStart: 20000101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8QwEB50UfDi-1GfETwJwU3S3aTeFnHxIFUWld5K0iQgaCuuCnvzb_j3_CUmbbeyKuq5M6TMdMhMZ75vAA5MKDvW5dnYEsVx6BJiHHHVwV2mrW0zymVJu3hzzuNYJEl0WQ_IDn9o4UfsSN2qI6WkCoUvzUlH-EUFVxdJU1d5vpoKRMSxZ3evYXhfdCcungkw27ecsrxb-gv_fatFmK-zR9Sr3L0EUyZfhtlqn-RoBWR8P-gfo1tdjwCVVkeFReXYoAdVuroYDeIeou-vb_gC-_3Ro2oaDt0X-lPH95SHyGNPSvFm4HoVrvunVydnuN6hgDPKxRMWxFrbpSa0fq2wYBlhVqmQcOYMpbtZpIlkijohy6OMGMOE1TbS0pXMykUrW4NWXuRmA5DMlJaueuSRzZyHtepwzmjbmLZ0uooFcDg2cJrVBON-z8VdWjW6WepsltY2C-CgEX6oeDV-FttznvpdYnvsxbQOv2FKu55n31PbBbDfPHaB47shMjfFs5cp_4i7qjyA9cr7zTksFC5xpWTzz-O3YI56QESbYMK3ofX0-Gx2YCZ7cV593IVpnojd8mv9ANl-5mE
linkProvider	Oxford University Press
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=NmRF%3A+identification+of+multispecies+RNA+2%E2%80%99-O-methylation+modification+sites+from+RNA+sequences&rft.jtitle=Briefings+in+bioinformatics&rft.au=Ao%2C+Chunyan&rft.au=Zou%2C+Quan&rft.au=Yu%2C+Liang&rft.date=2022-01-17&rft.pub=Oxford+University+Press&rft.issn=1467-5463&rft.eissn=1477-4054&rft.volume=23&rft.issue=1&rft_id=info:doi/10.1093%2Fbib%2Fbbab480&rft.externalDocID=10.1093%2Fbib%2Fbbab480
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1467-5463&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1467-5463&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1467-5463&client=summon