Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data
This paper proposed a novel in-silico framework for automatically screening disease-related variants and applied it to over 200,000 transcriptomes, providing an example to acquire medically relevant knowledge from publicly available sequence data.
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-09-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-32887-9 |
| _version_ | 1797998552728731648 |
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| author | Yuichi Shiraishi Ai Okada Kenichi Chiba Asuka Kawachi Ikuko Omori Raúl Nicolás Mateos Naoko Iida Hirofumi Yamauchi Kenjiro Kosaki Akihide Yoshimi |
| author_facet | Yuichi Shiraishi Ai Okada Kenichi Chiba Asuka Kawachi Ikuko Omori Raúl Nicolás Mateos Naoko Iida Hirofumi Yamauchi Kenjiro Kosaki Akihide Yoshimi |
| author_sort | Yuichi Shiraishi |
| collection | DOAJ |
| description | This paper proposed a novel in-silico framework for automatically screening disease-related variants and applied it to over 200,000 transcriptomes, providing an example to acquire medically relevant knowledge from publicly available sequence data. |
| first_indexed | 2024-04-11T10:50:33Z |
| format | Article |
| id | doaj.art-02ab6b33e13246a8bb0ad1e5da9d463b |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-11T10:50:33Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-02ab6b33e13246a8bb0ad1e5da9d463b2022-12-22T04:28:55ZengNature PortfolioNature Communications2041-17232022-09-0113111310.1038/s41467-022-32887-9Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing dataYuichi Shiraishi0Ai Okada1Kenichi Chiba2Asuka Kawachi3Ikuko Omori4Raúl Nicolás Mateos5Naoko Iida6Hirofumi Yamauchi7Kenjiro Kosaki8Akihide Yoshimi9Division of Genome Analysis Platform Development, National Cancer Center Research InstituteDivision of Genome Analysis Platform Development, National Cancer Center Research InstituteDivision of Genome Analysis Platform Development, National Cancer Center Research InstituteCancer RNA Research Unit, National Cancer Center Research InstituteCancer RNA Research Unit, National Cancer Center Research InstituteDivision of Genome Analysis Platform Development, National Cancer Center Research InstituteDivision of Genome Analysis Platform Development, National Cancer Center Research InstituteCancer RNA Research Unit, National Cancer Center Research InstituteCenter for Medical Genetics, Keio University School of MedicineCancer RNA Research Unit, National Cancer Center Research InstituteThis paper proposed a novel in-silico framework for automatically screening disease-related variants and applied it to over 200,000 transcriptomes, providing an example to acquire medically relevant knowledge from publicly available sequence data.https://doi.org/10.1038/s41467-022-32887-9 |
| spellingShingle | Yuichi Shiraishi Ai Okada Kenichi Chiba Asuka Kawachi Ikuko Omori Raúl Nicolás Mateos Naoko Iida Hirofumi Yamauchi Kenjiro Kosaki Akihide Yoshimi Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data Nature Communications |
| title | Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data |
| title_full | Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data |
| title_fullStr | Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data |
| title_full_unstemmed | Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data |
| title_short | Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data |
| title_sort | systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data |
| url | https://doi.org/10.1038/s41467-022-32887-9 |
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