DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads
The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutiliz...
Saved in:
| Published in: | Nature communications Vol. 16; no. 1; pp. 6202 - 26 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
04.07.2025
Nature Publishing Group Nature Portfolio |
| Subjects: | |
| ISSN: | 2041-1723, 2041-1723 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets.
Single-cell RNA-seq analysis is conventionally limited to gene-level quantification, missing transcript diversity. Here, authors present DOLPHIN, a deep learning method that enables exon- and junction-level analysis to improve cell representation and detect alternative splicing. |
|---|---|
| AbstractList | The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets. The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets.The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets. Abstract The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets. The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets.Single-cell RNA-seq analysis is conventionally limited to gene-level quantification, missing transcript diversity. Here, authors present DOLPHIN, a deep learning method that enables exon- and junction-level analysis to improve cell representation and detect alternative splicing. The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets. Single-cell RNA-seq analysis is conventionally limited to gene-level quantification, missing transcript diversity. Here, authors present DOLPHIN, a deep learning method that enables exon- and junction-level analysis to improve cell representation and detect alternative splicing. |
| ArticleNumber | 6202 |
| Author | Song, Kailu Zheng, Yumin Zhao, Bowen Tang, Jian Ding, Jun Eidelman, David H. |
| Author_xml | – sequence: 1 givenname: Kailu orcidid: 0009-0003-5326-7593 surname: Song fullname: Song, Kailu organization: Quantitative Life Sciences, McGill University, Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre – sequence: 2 givenname: Yumin orcidid: 0009-0008-4580-5247 surname: Zheng fullname: Zheng, Yumin organization: Quantitative Life Sciences, McGill University, Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre – sequence: 3 givenname: Bowen surname: Zhao fullname: Zhao, Bowen organization: Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre, Division of Experimental Medicine, Department of Medicine, McGill University – sequence: 4 givenname: David H. surname: Eidelman fullname: Eidelman, David H. organization: Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre, Division of Experimental Medicine, Department of Medicine, McGill University – sequence: 5 givenname: Jian surname: Tang fullname: Tang, Jian organization: HEC Montréal, Mila - Quebec AI Institute – sequence: 6 givenname: Jun orcidid: 0000-0001-5183-6885 surname: Ding fullname: Ding, Jun email: jun.ding@mcgill.ca organization: Quantitative Life Sciences, McGill University, Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre, Division of Experimental Medicine, Department of Medicine, McGill University, Mila - Quebec AI Institute, School of Computer Science, McGill University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40615408$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kk1v1DAQhiNUREvpH-CAInHhEvBXHPuEUFvoSivKAc6WP8Yhq6y92MmW_fd1d0tpOeCLx_bzvh6P52V1FGKAqnqN0XuMqPiQGWa8axBpG45bgZqbZ9UJQQw3uCP06FF8XJ3lvEJlUIkFYy-qY4aKhiFxUrmL6-W3q8XXWrutDhZynYfQj9BYGMd6Sjpkm4bNFNeDzbWBXQyu7iFAPcIWxtrs9kHSfZHV8DuGWhdiNQc7DWWRQLv8qnru9Zjh7H4-rX58vvx-ftUsr78szj8tG8skmxrAGhA3zAtOPFAQDANpPfGOG9l5jaWmrQPrGPMgpJeMgueayxa11GBNT6vFwddFvVKbNKx12qmoB7XfiKlXOk2DHUFRw6HD0gIHwkBS6YUG543gxlDrUfH6ePDazGYNzkIoxRifmD49CcNP1cetwoSQjnNZHN7dO6T4a4Y8qfWQ78qqA8Q5K1o4isojcUHf_oOu4pxCqdWewh3qmCjUm8cpPeTy5zcLQA6ATTHnBP4BwUjddY06dI0qXaP2XaNuiogeRLnAoYf09-7_qG4BXtPGbg |
| Cites_doi | 10.1016/j.biopha.2023.114631 10.5281/zenodo.15602232 10.1007/s12033-023-00777-0 10.1038/s41592-019-0619-0 10.21105/joss.00861 10.1038/s41592-024-02201-0 10.1186/gb-2004-5-10-r74 10.1038/s41586-023-06139-9 10.7554/eLife.73520 10.1093/nar/gks1026 10.1038/s41467-022-30489-z 10.1016/j.molcel.2017.06.003 10.1109/TSMC.1976.5408784 10.1093/bib/bbab105 10.2174/0115748936376211250429102627 10.1038/s41573-023-00688-4 10.1037/1082-989X.5.4.496 10.1371/journal.pcbi.1008195 10.1186/s13059-017-1382-0 10.3390/cancers15153765 10.1038/s41467-022-29744-0 10.1142/9789812704856_0007 10.5281/zenodo.15611935 10.1186/s13059-021-02461-5 10.1534/genetics.119.302523 10.1038/nmeth.1315 10.1038/ncomms14049 10.1038/s41598-017-12989-x 10.1038/s41592-018-0254-1 10.1016/j.ygeno.2020.01.014 10.1111/imr.12182 10.1002/eji.201141907 10.1016/j.ejca.2013.06.005 10.1101/2021.05.05.442755 10.1016/j.csbj.2020.12.009 10.1093/nar/gkac1260 10.1038/s41467-023-39813-7 10.18632/oncotarget.9063 10.1007/BF01908075 10.3389/fonc.2021.638099 10.1016/j.molmed.2011.05.011 10.1093/nar/gki033 10.1038/nbt.3192 10.1214/aoms/1177706717 10.1080/21541264.2023.2213514 10.1093/nar/gkp427 10.3390/jcm6010005 10.1080/14786440109462720 10.1038/ng.259 10.1016/j.compbiomed.2023.106733 10.1038/nature14966 10.1093/nar/28.21.4364 10.1038/s41467-024-50150-1 10.1006/smim.2000.0218 10.1016/j.patrec.2005.10.010 10.1093/bioinformatics/btv272 10.1371/journal.pcbi.1006360 10.3390/ijms24032943 10.1038/s41598-019-41695-z 10.3390/biom13010156 10.1186/s13059-015-0844-5 10.3748/wjg.v24.i43.4846 10.1093/nar/gkx1153 10.1093/bioinformatics/btt656 10.7150/thno.40860 10.1093/bioinformatics/btr174 10.1186/s13059-018-1496-z 10.7554/eLife.54603 10.1038/s41587-022-01312-3 10.1038/s41467-024-50194-3 10.1097/MPA.0000000000001461 10.1016/j.cellsig.2019.109470 10.1186/s13059-019-1850-9 10.1186/s13059-024-03323-6 10.3390/ijms21103534 10.1186/1471-2105-15-S8-S1 10.1016/j.cell.2021.04.048 10.1016/j.prp.2022.154220 10.1093/bioinformatics/btaa293 10.1038/nbt.3519 10.1093/bib/bbac585 10.1093/bioinformatics/btu170 10.1136/jclinpath-2020-206927 10.1093/bioinformatics/bts635 10.1038/nature24489 10.1186/1471-2164-11-5 10.1038/s42256-019-0037-0 10.1126/science.1160342 10.1038/s41592-018-0229-2 10.1038/s41422-019-0195-y 10.1101/gr.278822.123 10.1016/j.molcel.2018.08.018 10.1186/s13059-017-1248-5 10.1038/s41467-024-46480-9 10.1073/pnas.1419161111 10.1002/cyto.a.24507 10.1158/1535-7163.MCT-19-0208 10.1093/nar/gkab1049 10.1093/nar/gkaa1024 10.1038/s41569-022-00805-7 10.1038/s41587-021-01091-3 10.1038/s41590-020-0762-x 10.1148/rycan.2020190020 10.1016/0377-0427(87)90125-7 10.1038/s42256-022-00534-z 10.1093/nar/gkw943 10.15252/emmm.201910515 10.1038/ng.2764 10.1038/s41551-025-01423-7 10.1007/978-1-4419-6646-9_2 10.1038/s41598-021-89938-2 10.1093/bioinformatics/btab807 10.1038/s41577-021-00646-4 10.1084/jem.20191130 10.1038/s41587-020-0546-8 10.1016/j.biopha.2023.115077 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2025 2025. The Author(s). The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2025 2025 |
| Copyright_xml | – notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2025 2025 |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QL 7QP 7QR 7SN 7SS 7ST 7T5 7T7 7TM 7TO 7X7 7XB 88E 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU COVID DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. LK8 M0S M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS RC3 SOI 7X8 5PM DOA |
| DOI | 10.1038/s41467-025-61580-w |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Bacteriology Abstracts (Microbiology B) Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Ecology Abstracts Entomology Abstracts (Full archive) Environment Abstracts Immunology Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Coronavirus Research Database ProQuest Central Engineering Research Database ProQuest Health & Medical Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection ProQuest Health & Medical Collection Medical Database Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Genetics Abstracts Environment Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student Oncogenes and Growth Factors Abstracts ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection Chemoreception Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Entomology Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic Calcium & Calcified Tissue Abstracts ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) AIDS and Cancer Research Abstracts ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Immunology Abstracts Environment Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2041-1723 |
| EndPage | 26 |
| ExternalDocumentID | oai_doaj_org_article_3b6e719ce6e24e939f8aedfb86bb3cf0 PMC12227669 40615408 10_1038_s41467_025_61580_w |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada) grantid: RGPIN2022-04399 funderid: 501100000038 – fundername: Meakins-Christie Chair in Respiratory Research – fundername: Fonds de Recherche du Québec - Santé (Fonds de la recherche en sante du Quebec) grantid: 295299; 366764 funderid: 501100000156 – fundername: Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada) grantid: PJT-180505 funderid: 501100000024 – fundername: Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada) grantid: RGPIN2022-04399 – fundername: Fonds de Recherche du Québec - Santé (Fonds de la recherche en sante du Quebec) grantid: 295299 – fundername: Fonds de Recherche du Québec - Santé (Fonds de la recherche en sante du Quebec) grantid: 366764 – fundername: Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada) grantid: PJT-180505 |
| GroupedDBID | --- 0R~ 39C 53G 5VS 70F 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAHBH AAJSJ AASML ABUWG ACGFO ACGFS ACIWK ACMJI ACPRK ADBBV ADFRT ADMLS ADRAZ AENEX AEUYN AFKRA AFRAH AHMBA ALIPV ALMA_UNASSIGNED_HOLDINGS AMTXH AOIJS ARAPS ASPBG AVWKF AZFZN BBNVY BCNDV BENPR BGLVJ BHPHI BPHCQ BVXVI C6C CCPQU DIK EBLON EBS EE. EMOBN F5P FEDTE FYUFA GROUPED_DOAJ HCIFZ HMCUK HVGLF HYE HZ~ KQ8 LGEZI LK8 LOTEE M1P M7P M~E NADUK NAO NXXTH O9- OK1 P2P P62 PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO RNS RNT RNTTT RPM SNYQT SV3 TSG UKHRP AAYXX AFFHD CITATION PJZUB PPXIY PQGLB CGR CUY CVF ECM EIF NPM 3V. 7QL 7QP 7QR 7SN 7SS 7ST 7T5 7T7 7TM 7TO 7XB 8FD 8FK AZQEC C1K COVID DWQXO FR3 GNUQQ H94 K9. M48 P64 PKEHL PQEST PQUKI PRINS RC3 SOI 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c494t-e1ae06b4f862fe3e841e25f2fd6b97fa19a35decd44fe89f943ef6a695053b1a3 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001523057700010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2041-1723 |
| IngestDate | Fri Oct 03 12:50:34 EDT 2025 Tue Nov 04 02:04:02 EST 2025 Fri Sep 05 15:44:01 EDT 2025 Tue Oct 07 07:44:42 EDT 2025 Tue Jul 08 01:41:15 EDT 2025 Sat Nov 29 07:47:11 EST 2025 Sat Jul 05 01:11:54 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c494t-e1ae06b4f862fe3e841e25f2fd6b97fa19a35decd44fe89f943ef6a695053b1a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-5183-6885 0009-0003-5326-7593 0009-0008-4580-5247 |
| OpenAccessLink | https://www.proquest.com/docview/3227170748?pq-origsite=%requestingapplication% |
| PMID | 40615408 |
| PQID | 3227170748 |
| PQPubID | 546298 |
| PageCount | 26 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_3b6e719ce6e24e939f8aedfb86bb3cf0 pubmedcentral_primary_oai_pubmedcentral_nih_gov_12227669 proquest_miscellaneous_3227308411 proquest_journals_3227170748 pubmed_primary_40615408 crossref_primary_10_1038_s41467_025_61580_w springer_journals_10_1038_s41467_025_61580_w |
| PublicationCentury | 2000 |
| PublicationDate | 2025-07-04 |
| PublicationDateYYYYMMDD | 2025-07-04 |
| PublicationDate_xml | – month: 07 year: 2025 text: 2025-07-04 day: 04 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Nature communications |
| PublicationTitleAbbrev | Nat Commun |
| PublicationTitleAlternate | Nat Commun |
| PublicationYear | 2025 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | T Sterne-Weiler (61580_CR121) 2018; 72 61580_CR34 WX Wen (61580_CR65) 2023; 51 Y Hu (61580_CR97) 2013; 41 T Fawcett (61580_CR95) 2006; 27 H Huang (61580_CR59) 2019; 11 M Frankenberger (61580_CR48) 2012; 42 X Chang (61580_CR7) 2024; 66 61580_CR27 61580_CR26 T Pelaseyed (61580_CR76) 2014; 260 W Liu (61580_CR67) 2020; 112 I Korsunsky (61580_CR41) 2019; 16 S Schafer (61580_CR28) 2015; 87 A Dobin (61580_CR100) 2013; 29 D Alexander (61580_CR70) 2000; 12 G Benegas (61580_CR23) 2022; 11 D Garrido-Martín (61580_CR72) 2018; 14 Z Zhang (61580_CR10) 2021; 22 Q Pan (61580_CR93) 2008; 40 J Dardare (61580_CR54) 2020; 21 M Büttner (61580_CR118) 2019; 16 W Mojica (61580_CR78) 2010; 11 A Hamosh (61580_CR113) 2005; 33 PJ Rousseeuw (61580_CR117) 1987; 20 W Jiang (61580_CR89) 2021; 19 M Sultan (61580_CR94) 2008; 321 SA Dudani (61580_CR37) 1976; 6 61580_CR38 X Zhu (61580_CR58) 2020; 66 AE Hall (61580_CR79) 2022; 13 Y Hao (61580_CR44) 2021; 184 X Xiang (61580_CR25) 2024; 15 C Cai (61580_CR3) 2023; 162 A Haber (61580_CR46) 2017; 551 AMA Miranda (61580_CR9) 2023; 20 F Wolf (61580_CR11) 2018; 19 L Vereecke (61580_CR77) 2011; 17 M-L Racu (61580_CR55) 2023; 15 61580_CR14 61580_CR96 CV Theodoris (61580_CR15) 2023; 618 V Hahaut (61580_CR29) 2022; 40 Y Wang (61580_CR30) 2019; 217 S Olkin (61580_CR108) 1958; 29 K Choe (61580_CR2) 2023; 13 Y Liao (61580_CR101) 2014; 30 T Tian (61580_CR84) 2019; 1 C-K Shiau (61580_CR49) 2023; 14 J Wang (61580_CR17) 2024; 15 Y Huang (61580_CR68) 2017; 18 F Yang (61580_CR16) 2022; 4 NL Bray (61580_CR82) 2016; 34 AC Morani (61580_CR51) 2020; 2 J Peng (61580_CR31) 2019; 29 T Hu (61580_CR73) 2020; 10 AM Bolger (61580_CR99) 2014; 30 N Erfanian (61580_CR36) 2023; 165 DA Mogilenko (61580_CR5) 2022; 22 D Grün (61580_CR45) 2015; 525 S Armstrong (61580_CR60) 2019; 18 K Kirschner (61580_CR57) 2010; 30 Y Li (61580_CR107) 2014; 15 MJ Goldman (61580_CR114) 2020; 38 AC Frazee (61580_CR122) 2015; 31 Y Huang (61580_CR24) 2021; 22 DW Barnett (61580_CR103) 2011; 27 J Zhang (61580_CR4) 2020; 21 K Pearson (61580_CR91) 1901; 2 H Hu (61580_CR21) 2023; 157 H-H Oh (61580_CR81) 2022; 240 Y Zhang (61580_CR74) 2013; 49 R Lopez (61580_CR13) 2018; 15 M Martens (61580_CR109) 2021; 49 61580_CR1 Y Song (61580_CR87) 2024; 25 S Shen (61580_CR120) 2014; 111 61580_CR6 61580_CR88 C Martino (61580_CR61) 2020; 49 61580_CR85 RB Darlington (61580_CR106) 2000; 5 F Cunningham (61580_CR98) 2022; 50 G Finak (61580_CR105) 2015; 16 S Rizzetto (61580_CR123) 2017; 7 JN Weinstein (61580_CR62) 2013; 45 CA Arzalluz-Luque (61580_CR33) 2018; 19 A of Genome Resources Consortium (61580_CR110) 2019; 213 WX Wen (61580_CR71) 2020; 16 MJ Landrum (61580_CR111) 2018; 46 M Burset (61580_CR116) 2000; 28 S Guo (61580_CR63) 2025; 35 61580_CR112 CF Buen Abad Najar (61580_CR80) 2020; 9 MA Al Barashdi (61580_CR69) 2021; 74 E Hasanaj (61580_CR18) 2022; 13 A Strehl (61580_CR40) 2002; 3 61580_CR119 Y Song (61580_CR22) 2017; 67 H Tran (61580_CR42) 2020; 21 F Tang (61580_CR32) 2009; 6 L Pan (61580_CR19) 2021; 38 CH Grønbech (61580_CR35) 2020; 36 Z Zeng (61580_CR43) 2024; 15 A McGuigan (61580_CR52) 2018; 24 S Ahmed (61580_CR53) 2017; 6 NX Vinh (61580_CR86) 2010; 11 RF Halperin (61580_CR92) 2021; 11 H Gao (61580_CR47) 2021; 11 H-Y Wang (61580_CR83) 2023; 24 R Satija (61580_CR12) 2015; 33 61580_CR124 VA Traag (61580_CR104) 2019; 9 L Hubert (61580_CR39) 1985; 2 G Zheng (61580_CR102) 2017; 8 61580_CR66 J Chen (61580_CR50) 2009; 37 61580_CR127 61580_CR125 61580_CR64 61580_CR126 D Sun (61580_CR115) 2022; 40 R Carsetti (61580_CR75) 2022; 101 A Joglekar (61580_CR20) 2023; 14 B Van de Sande (61580_CR8) 2023; 22 A Strippoli (61580_CR56) 2016; 7 G Yeo (61580_CR90) 2004; 5 |
| References_xml | – volume: 162 start-page: 114631 year: 2023 ident: 61580_CR3 publication-title: Biomed. Pharmacother. doi: 10.1016/j.biopha.2023.114631 – ident: 61580_CR127 doi: 10.5281/zenodo.15602232 – volume: 66 start-page: 1497 year: 2024 ident: 61580_CR7 publication-title: Mol. Biotechnol. doi: 10.1007/s12033-023-00777-0 – volume: 16 start-page: 1289 year: 2019 ident: 61580_CR41 publication-title: Nat. Methods doi: 10.1038/s41592-019-0619-0 – ident: 61580_CR38 doi: 10.21105/joss.00861 – ident: 61580_CR14 doi: 10.1038/s41592-024-02201-0 – volume: 5 start-page: 1 year: 2004 ident: 61580_CR90 publication-title: Genome Biol. doi: 10.1186/gb-2004-5-10-r74 – volume: 618 start-page: 616 year: 2023 ident: 61580_CR15 publication-title: Nature doi: 10.1038/s41586-023-06139-9 – volume: 11 start-page: e73520 year: 2022 ident: 61580_CR23 publication-title: eLife doi: 10.7554/eLife.73520 – ident: 61580_CR96 – volume: 41 start-page: e39 year: 2013 ident: 61580_CR97 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gks1026 – volume: 13 year: 2022 ident: 61580_CR79 publication-title: Nat. Commun. doi: 10.1038/s41467-022-30489-z – volume: 67 start-page: 148 year: 2017 ident: 61580_CR22 publication-title: Mol. Cell doi: 10.1016/j.molcel.2017.06.003 – volume: 6 start-page: 325 year: 1976 ident: 61580_CR37 publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1976.5408784 – volume: 22 start-page: bbab105 year: 2021 ident: 61580_CR10 publication-title: Brief. Bioinform. doi: 10.1093/bib/bbab105 – ident: 61580_CR66 doi: 10.2174/0115748936376211250429102627 – volume: 22 start-page: 496 year: 2023 ident: 61580_CR8 publication-title: Nat. Rev. Drug Discov. doi: 10.1038/s41573-023-00688-4 – volume: 5 start-page: 496 year: 2000 ident: 61580_CR106 publication-title: Psychol. Methods doi: 10.1037/1082-989X.5.4.496 – volume: 16 start-page: e1008195 year: 2020 ident: 61580_CR71 publication-title: PLOS Comput. Biol. doi: 10.1371/journal.pcbi.1008195 – volume: 19 year: 2018 ident: 61580_CR11 publication-title: Genome Biol. doi: 10.1186/s13059-017-1382-0 – volume: 15 start-page: 3765 year: 2023 ident: 61580_CR55 publication-title: Cancers doi: 10.3390/cancers15153765 – volume: 13 year: 2022 ident: 61580_CR18 publication-title: Nat. Commun. doi: 10.1038/s41467-022-29744-0 – ident: 61580_CR88 doi: 10.1142/9789812704856_0007 – ident: 61580_CR126 doi: 10.5281/zenodo.15611935 – volume: 22 year: 2021 ident: 61580_CR24 publication-title: Genome Biol. doi: 10.1186/s13059-021-02461-5 – volume: 213 start-page: 1189 year: 2019 ident: 61580_CR110 publication-title: Genetics doi: 10.1534/genetics.119.302523 – volume: 6 start-page: 377 year: 2009 ident: 61580_CR32 publication-title: Nat. Methods doi: 10.1038/nmeth.1315 – volume: 8 year: 2017 ident: 61580_CR102 publication-title: Nat. Commun. doi: 10.1038/ncomms14049 – volume: 7 year: 2017 ident: 61580_CR123 publication-title: Sci. Rep. doi: 10.1038/s41598-017-12989-x – volume: 16 start-page: 43 year: 2019 ident: 61580_CR118 publication-title: Nat. Methods doi: 10.1038/s41592-018-0254-1 – volume: 112 start-page: 2418 year: 2020 ident: 61580_CR67 publication-title: Genomics doi: 10.1016/j.ygeno.2020.01.014 – volume: 260 start-page: 8 year: 2014 ident: 61580_CR76 publication-title: Immunol. Rev. doi: 10.1111/imr.12182 – volume: 42 start-page: 957 year: 2012 ident: 61580_CR48 publication-title: Eur. J. Immunol. doi: 10.1002/eji.201141907 – volume: 49 start-page: 3320 year: 2013 ident: 61580_CR74 publication-title: Eur. J. Cancer doi: 10.1016/j.ejca.2013.06.005 – ident: 61580_CR119 doi: 10.1101/2021.05.05.442755 – volume: 19 start-page: 183 year: 2021 ident: 61580_CR89 publication-title: Comput. Struct. Biotechnol. J. doi: 10.1016/j.csbj.2020.12.009 – volume: 51 start-page: e29 year: 2023 ident: 61580_CR65 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkac1260 – ident: 61580_CR124 – volume: 14 year: 2023 ident: 61580_CR49 publication-title: Nat. Commun. doi: 10.1038/s41467-023-39813-7 – volume: 7 start-page: 35159 year: 2016 ident: 61580_CR56 publication-title: Oncotarget doi: 10.18632/oncotarget.9063 – ident: 61580_CR27 – volume: 2 start-page: 193 year: 1985 ident: 61580_CR39 publication-title: J. Classif. doi: 10.1007/BF01908075 – volume: 11 start-page: 638099 year: 2021 ident: 61580_CR47 publication-title: Front. Oncol. doi: 10.3389/fonc.2021.638099 – volume: 17 start-page: 584 year: 2011 ident: 61580_CR77 publication-title: Trends Mol. Med. doi: 10.1016/j.molmed.2011.05.011 – volume: 33 start-page: D514 year: 2005 ident: 61580_CR113 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gki033 – volume: 33 start-page: 495 year: 2015 ident: 61580_CR12 publication-title: Nat. Biotechnol. doi: 10.1038/nbt.3192 – volume: 29 start-page: 201 year: 1958 ident: 61580_CR108 publication-title: Ann. Math. Stat. doi: 10.1214/aoms/1177706717 – volume: 14 start-page: 92 year: 2023 ident: 61580_CR20 publication-title: Transcription doi: 10.1080/21541264.2023.2213514 – volume: 37 start-page: W305 year: 2009 ident: 61580_CR50 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkp427 – volume: 6 start-page: 5 year: 2017 ident: 61580_CR53 publication-title: J. Clin. Med. doi: 10.3390/jcm6010005 – volume: 30 start-page: 3223 year: 2010 ident: 61580_CR57 publication-title: Anticancer Res. – volume: 2 start-page: 559 year: 1901 ident: 61580_CR91 publication-title: Lond. Edinb. Dublin Philos. Mag. J. Sci. doi: 10.1080/14786440109462720 – volume: 40 start-page: 1413 year: 2008 ident: 61580_CR93 publication-title: Nat. Genet. doi: 10.1038/ng.259 – volume: 157 start-page: 106733 year: 2023 ident: 61580_CR21 publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2023.106733 – volume: 525 start-page: 251 year: 2015 ident: 61580_CR45 publication-title: Nature doi: 10.1038/nature14966 – volume: 28 start-page: 4364 year: 2000 ident: 61580_CR116 publication-title: Nucleic Acids Res. doi: 10.1093/nar/28.21.4364 – volume: 15 year: 2024 ident: 61580_CR17 publication-title: Nat. Commun. doi: 10.1038/s41467-024-50150-1 – volume: 12 start-page: 349 year: 2000 ident: 61580_CR70 publication-title: Semin. Immunol. doi: 10.1006/smim.2000.0218 – volume: 27 start-page: 861 year: 2006 ident: 61580_CR95 publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2005.10.010 – volume: 31 start-page: 2778 year: 2015 ident: 61580_CR122 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btv272 – volume: 14 start-page: e1006360 year: 2018 ident: 61580_CR72 publication-title: PLOS Comput. Biol. doi: 10.1371/journal.pcbi.1006360 – ident: 61580_CR1 doi: 10.3390/ijms24032943 – volume: 9 start-page: 1 year: 2019 ident: 61580_CR104 publication-title: Sci. Rep. doi: 10.1038/s41598-019-41695-z – volume: 13 start-page: 156 year: 2023 ident: 61580_CR2 publication-title: Biomolecules doi: 10.3390/biom13010156 – volume: 16 year: 2015 ident: 61580_CR105 publication-title: Genome Biol. doi: 10.1186/s13059-015-0844-5 – volume: 24 start-page: 4846 year: 2018 ident: 61580_CR52 publication-title: World J. Gastroenterol. doi: 10.3748/wjg.v24.i43.4846 – volume: 46 start-page: D1062 year: 2018 ident: 61580_CR111 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx1153 – volume: 30 start-page: 923 year: 2014 ident: 61580_CR101 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btt656 – volume: 10 start-page: 4056 year: 2020 ident: 61580_CR73 publication-title: Theranostics doi: 10.7150/thno.40860 – volume: 27 start-page: 1691 year: 2011 ident: 61580_CR103 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr174 – volume: 19 year: 2018 ident: 61580_CR33 publication-title: Genome Biol. doi: 10.1186/s13059-018-1496-z – volume: 9 start-page: e54603 year: 2020 ident: 61580_CR80 publication-title: eLife doi: 10.7554/eLife.54603 – volume: 40 start-page: 1447 year: 2022 ident: 61580_CR29 publication-title: Nat. Biotechnol. doi: 10.1038/s41587-022-01312-3 – volume: 15 year: 2024 ident: 61580_CR43 publication-title: Nat. Commun. doi: 10.1038/s41467-024-50194-3 – volume: 49 start-page: 143 year: 2020 ident: 61580_CR61 publication-title: Pancreas doi: 10.1097/MPA.0000000000001461 – volume: 66 start-page: 109470 year: 2020 ident: 61580_CR58 publication-title: Cell. Signal. doi: 10.1016/j.cellsig.2019.109470 – ident: 61580_CR34 – volume: 21 year: 2020 ident: 61580_CR42 publication-title: Genome Biol. doi: 10.1186/s13059-019-1850-9 – volume: 25 year: 2024 ident: 61580_CR87 publication-title: Genome Biol. doi: 10.1186/s13059-024-03323-6 – volume: 21 start-page: 3534 year: 2020 ident: 61580_CR54 publication-title: Int. J. Mol. Sci. doi: 10.3390/ijms21103534 – volume: 15 start-page: 1 year: 2014 ident: 61580_CR107 publication-title: BMC Bioinform. doi: 10.1186/1471-2105-15-S8-S1 – volume: 184 start-page: 3573 year: 2021 ident: 61580_CR44 publication-title: Cell doi: 10.1016/j.cell.2021.04.048 – volume: 240 start-page: 154220 year: 2022 ident: 61580_CR81 publication-title: Pathol. Res. Pract. doi: 10.1016/j.prp.2022.154220 – volume: 36 start-page: 4415 year: 2020 ident: 61580_CR35 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btaa293 – ident: 61580_CR125 – volume: 34 start-page: 525 year: 2016 ident: 61580_CR82 publication-title: Nat. Biotechnol. doi: 10.1038/nbt.3519 – volume: 24 start-page: bbac585 year: 2023 ident: 61580_CR83 publication-title: Brief. Bioinform. doi: 10.1093/bib/bbac585 – volume: 30 start-page: 2114 year: 2014 ident: 61580_CR99 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btu170 – volume: 74 start-page: 548 year: 2021 ident: 61580_CR69 publication-title: J. Clin. Pathol. doi: 10.1136/jclinpath-2020-206927 – volume: 29 start-page: 15 year: 2013 ident: 61580_CR100 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts635 – volume: 551 start-page: 333 year: 2017 ident: 61580_CR46 publication-title: Nature doi: 10.1038/nature24489 – volume: 11 start-page: 1 year: 2010 ident: 61580_CR78 publication-title: BMC Genom. doi: 10.1186/1471-2164-11-5 – volume: 1 start-page: 191 year: 2019 ident: 61580_CR84 publication-title: Nat. Mach. Intell. doi: 10.1038/s42256-019-0037-0 – volume: 321 start-page: 956 year: 2008 ident: 61580_CR94 publication-title: Science doi: 10.1126/science.1160342 – volume: 15 start-page: 1053 year: 2018 ident: 61580_CR13 publication-title: Nat. Methods doi: 10.1038/s41592-018-0229-2 – volume: 29 start-page: 725 year: 2019 ident: 61580_CR31 publication-title: Cell Res. doi: 10.1038/s41422-019-0195-y – volume: 35 start-page: 147 year: 2025 ident: 61580_CR63 publication-title: Genome Res. doi: 10.1101/gr.278822.123 – volume: 72 start-page: 187–200.e6 year: 2018 ident: 61580_CR121 publication-title: Mol. Cell doi: 10.1016/j.molcel.2018.08.018 – volume: 18 start-page: 1 year: 2017 ident: 61580_CR68 publication-title: Genome Biol. doi: 10.1186/s13059-017-1248-5 – volume: 15 year: 2024 ident: 61580_CR25 publication-title: Nat. Commun. doi: 10.1038/s41467-024-46480-9 – volume: 111 start-page: E5593 year: 2014 ident: 61580_CR120 publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.1419161111 – volume: 101 start-page: 131 year: 2022 ident: 61580_CR75 publication-title: Cytom. Part A doi: 10.1002/cyto.a.24507 – volume: 18 start-page: 1899 year: 2019 ident: 61580_CR60 publication-title: Mol. Cancer Ther. doi: 10.1158/1535-7163.MCT-19-0208 – volume: 50 start-page: D988 year: 2022 ident: 61580_CR98 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkab1049 – volume: 49 start-page: D613 year: 2021 ident: 61580_CR109 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkaa1024 – volume: 20 start-page: 289 year: 2023 ident: 61580_CR9 publication-title: Nat. Rev. Cardiol. doi: 10.1038/s41569-022-00805-7 – volume: 11 start-page: 2837 year: 2010 ident: 61580_CR86 publication-title: J. Mach. Learn. Res. – volume: 40 start-page: 527 year: 2022 ident: 61580_CR115 publication-title: Nat. Biotechnol. doi: 10.1038/s41587-021-01091-3 – volume: 21 start-page: 1107 year: 2020 ident: 61580_CR4 publication-title: Nat. Immunol. doi: 10.1038/s41590-020-0762-x – volume: 2 start-page: e190020 year: 2020 ident: 61580_CR51 publication-title: Radiol. Imaging Cancer doi: 10.1148/rycan.2020190020 – volume: 20 start-page: 53 year: 1987 ident: 61580_CR117 publication-title: J. Comput. Appl. Math. doi: 10.1016/0377-0427(87)90125-7 – volume: 4 start-page: 852 year: 2022 ident: 61580_CR16 publication-title: Nat. Mach. Intell. doi: 10.1038/s42256-022-00534-z – ident: 61580_CR112 doi: 10.1093/nar/gkw943 – volume: 11 start-page: e10515 year: 2019 ident: 61580_CR59 publication-title: EMBO Mol. Med. doi: 10.15252/emmm.201910515 – volume: 45 start-page: 1113 year: 2013 ident: 61580_CR62 publication-title: Nat. Genet. doi: 10.1038/ng.2764 – ident: 61580_CR6 doi: 10.1038/s41551-025-01423-7 – ident: 61580_CR64 doi: 10.1007/978-1-4419-6646-9_2 – volume: 11 year: 2021 ident: 61580_CR92 publication-title: Sci. Rep. doi: 10.1038/s41598-021-89938-2 – volume: 38 start-page: 1287 year: 2021 ident: 61580_CR19 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btab807 – volume: 22 start-page: 484 year: 2022 ident: 61580_CR5 publication-title: Nat. Rev. Immunol. doi: 10.1038/s41577-021-00646-4 – volume: 217 start-page: e20191130 year: 2019 ident: 61580_CR30 publication-title: J. Exp. Med. doi: 10.1084/jem.20191130 – ident: 61580_CR26 – ident: 61580_CR85 – volume: 38 start-page: 675 year: 2020 ident: 61580_CR114 publication-title: Nat. Biotechnol. doi: 10.1038/s41587-020-0546-8 – volume: 165 start-page: 115077 year: 2023 ident: 61580_CR36 publication-title: Biomed. Pharmacother. doi: 10.1016/j.biopha.2023.115077 – volume: 3 start-page: 583 year: 2002 ident: 61580_CR40 publication-title: J. Mach. Learn. Res. – volume: 87 start-page: 11 year: 2015 ident: 61580_CR28 publication-title: Curr. Protoc. Hum. Genet. |
| SSID | ssj0000391844 |
| Score | 2.4767582 |
| Snippet | The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and... Abstract The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 6202 |
| SubjectTerms | 631/114/1305 631/114/1314 631/114/2397 631/114/794 Alternative splicing Alternative Splicing - genetics Animals Aquatic mammals Biomarkers Clustering Datasets Deep Learning Exons - genetics Gene expression Gene Expression Profiling - methods Graph neural networks Graph representations Graphical representations Heterogeneity Humanities and Social Sciences Humans multidisciplinary Science Science (multidisciplinary) Single-Cell Analysis - methods Therapeutic targets Transcriptome - genetics Transcriptomics |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3daxQxEB9KseCL1O-tVSL4pks3m2wueVTbUqGcfbDQt5BsJlgpe-X2au1_7yS7d_aq4otvYZOFYWYy85skMwPwRkQTYqToxNVBlhJrXnrfGBpJbHzrQytyovDxZDrVZ2fm5Farr_QmbCgPPDBuT3iFE25aVFhLNMJE7TBEr5X3oo05Wq8m5lYwlW2wMBS6yDFLphJ6r5fZJqTureTEdVVer3miXLD_Tyjz98eSd25MsyM63IYHI4Jk7wfKH8IGdo9ga-gpefMYwv7n45OjT1M23u73LJ0GXGCZjujZIrmmbChSNnLPfE5gYaRFyC7S-yHmb_JgnrsXMfwx65ijFd_I_yUZMgKZoX8Cp4cHXz4elWMrhbKVRi5K5A4r5WWkACaiQC051k2sY1DeTKLjxokmYBukjKhNNFJgVE4ZAkjCcyeewmY36_A5sKatG-TCkYxTl9_odMJ93rlao_BRFfB2yVZ7OVTMsPmmW2g7CMGSEGwWgr0u4EPi_GplqnadP5AO2FEH7L90oIDdpdzsuAV7S5aKQlVCSLqA16tp2jyJ3a7D2dWwRlTEDF7As0HMK0oS0iE4S3_rNQVYI3V9pjv_mgt085RgrJQp4N1SV37R9Xde7PwPXryA-3VS8nQCLXdhczG_wpdwr_2-OO_nr_Iu-Qm5EBpK priority: 102 providerName: Directory of Open Access Journals |
| Title | DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads |
| URI | https://link.springer.com/article/10.1038/s41467-025-61580-w https://www.ncbi.nlm.nih.gov/pubmed/40615408 https://www.proquest.com/docview/3227170748 https://www.proquest.com/docview/3227308411 https://pubmed.ncbi.nlm.nih.gov/PMC12227669 https://doaj.org/article/3b6e719ce6e24e939f8aedfb86bb3cf0 |
| Volume | 16 |
| WOSCitedRecordID | wos001523057700010&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: DOA dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: P5Z dateStart: 20100101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: M7P dateStart: 20100101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: 7X7 dateStart: 20100101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: BENPR dateStart: 20100101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: PIMPY dateStart: 20100101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZoCxKX8iwEyspI3CBqEjtZ-4QotGqlskQIpIVLZMdjKKqSdrOl9N8z42S3Wl4XLpYVO5Lt-ewZj-fB2DPhtfMebycmczKWkKWxtbnGmoTc1tbVIjgKH40nEzWd6nJQuHWDWeXiTAwHtWtr0pHvIPDw5oEMT708PYspaxS9rg4pNNbYBkUqQ5xv7O5NyvdLLQvFP1dSDt4yiVA7nQxnA2VxRWaukvhihSOFwP1_kjZ_N5r85eU0MKT9W_87ldtscxBF-aseO3fYNWjusht9csrLe8y9eXdUHhxO-GAm0HFSK5xATLp-PiceF04ccmvuuA2eMBzhCPyEDJG4vQyVWUiDxOFH23CDPb4hIyUwcJRWXXeffdzf-_D6IB5yMsS11HIeQ2ogKaz0eBPyIEDJFLLcZ94VVo-9SbURuYPaSelBaa-lAF-YQqOkJWxqxBZbb9oGHjKe11kOqTAIFkoX7I0iAdIakykQ1hcRe76gS3Xah96owpO5UFVPxQqpWAUqVhcR2yXSLXtS2OzwoZ19qYZdWAlbwDjVNRSQSdBCe2XAeasKa0Xtk4htLyhWDXu5q67IFbGny2bchbTcpoH2vO8jElyMNGIPepwsR0IiE8rF-LdaQdDKUFdbmuOvIdJ3Sp7KRaEj9mIBtqtx_X0tHv17Go_ZzYzwT0pquc3W57NzeMKu19_nx91sxNbG03Eo1WjYTqOgqRiRXWyJZZl_xpby8G356SchDy5Z |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKAcGF9yNQwEhwgqiJ7WTtA0JAqXbVZdlDkXozdjKGoiopmy3L_il-I2Mn2Wp53XrgZsVOZDvfjD-PZzyEPOFOlc7h7sSwUsQCWBpbmyksCchsYcuCh0Dh8WAykQcHarpBfvSxMN6tsteJQVGXdeFt5NsIPNx54IInXx5_jX3WKH-62qfQaGGxB8sFbtmaF6Md_L9PGdt9u_9mGHdZBeJCKDGPITWQ5FY45PIOOEiRAsscc2Vu1cCZVBmelVCUQjiQyinBweUmV8gVuE0Nx--eI-eRRrAkuApOVzYdf9u6FKKLzUm43G5E0EQ-ZyxSB5nEi7X1L6QJ-BO3_d1F85dz2rD87V793ybuGrnSEW36qpWM62QDqhvkYpt6c3mTlDvvx9PhaEI7J4iGeqPJEcT-JIPO_Qoe9KkP2m6oDXE-FIUN6JF3s6J2GQqzkOSJwve6ogZbfEGa4KFOkYuXzS3y4UyGeJtsVnUFdwnNCpZByg2Kgk-G7Iz09NgawyRw6_KIPOtxoI_bi0V0cAjgUreo0YgaHVCjFxF57aGyaukvBQ8P6tkn3ekYzW0Og1QVkAMToLhy0kDprMyt5YVLIrLVI0R3mqrRp_CIyONVNeoYP92mgvqkbcMTnIw0IndaXK564gkhsn58W64hdq2r6zXV4edwj3nq47DzXEXkeQ_u0379fS7u_XsYj8il4f67sR6PJnv3yWXmZc-b48UW2ZzPTuABuVB8mx82s4dBeCn5eNag_wkB6YXG |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lj9MwELaW5SEuvB-BBYwEJ4iaxI5rHxACSrXVVqUHkFZcvHYyhkWrdGm6lP41fh1jJ-mqvG574GbFTmQ734w_j2c8hDxhTpXO4e7EZCWPOWRpbG2usMQht4UtCxYChcf9yUTu76vpFvnRxcJ4t8pOJwZFXc4KbyPvIfBw54ELnuy51i1iOhi-PP4a-wxS_qS1S6fRQGQPVkvcvtUvRgP810-zbPj2_ZvduM0wEBdc8UUMqYFEWO6Q1ztgIHkKWe4yVwqr-s6kyrC8hKLk3IFUTnEGThihkDcwmxqG3z1Hzve5kN6dbJp_XNt3_M3rkvM2TidhslfzoJV8_likETKJlxtrYUgZ8Cee-7u75i9ntmEpHF79nyfxGrnSEnD6qpGY62QLqhvkYpOSc3WTlIN34-nuaEJb54iaemPKEcT-hIMu_Moe9KwP5q6pDfE_FIUQ6JF3v6J2FQrzkPyJwvdZRQ22-IL0wYsARY5e1rfIhzMZ4m2yXc0quEtoXmQ5pMygiPgkyc5IT5utMZkEZp2IyLMOE_q4uXBEB0cBJnWDII0I0gFBehmR1x4265b-svDwYDb_pFvdo5kV0E9VAQIyDoopJw2UzkphLStcEpGdDi261WC1PoVKRB6vq1H3-Ok2FcxOmjYswclII3Knwei6J54o4m4A35Yb6N3o6mZNdfg53G-e-vhsIVREnndAP-3X3-fi3r-H8YhcQqzr8Wiyd59czrwYeis93yHbi_kJPCAXim-Lw3r-MMgxJQdnjfmfXL-OsA |
| 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=DOLPHIN+advances+single-cell+transcriptomics+beyond+gene+level+by+leveraging+exon+and+junction+reads&rft.jtitle=Nature+communications&rft.au=Song%2C+Kailu&rft.au=Zheng%2C+Yumin&rft.au=Zhao%2C+Bowen&rft.au=Eidelman%2C+David+H.&rft.date=2025-07-04&rft.issn=2041-1723&rft.eissn=2041-1723&rft.volume=16&rft.issue=1&rft_id=info:doi/10.1038%2Fs41467-025-61580-w&rft.externalDBID=n%2Fa&rft.externalDocID=10_1038_s41467_025_61580_w |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2041-1723&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2041-1723&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2041-1723&client=summon |