m6Acorr: an online tool for the correction and comparison of m6A methylation profiles

Abstract Background The analysis and comparison of RNA m6A methylation profiles have become increasingly important for understanding the post-transcriptional regulations of gene expression. However, current m6A profiles in public databases are not readily intercomparable, where heterogeneous profile...
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Autor*in:	Jianwei Li [verfasserIn] Yan Huang [verfasserIn] Qinghua Cui [verfasserIn] Yuan Zhou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Übergeordnetes Werk:	In: BMC Bioinformatics - BMC, 2003, 21(2020), 1, Seite 8
Übergeordnetes Werk:	volume:21 ; year:2020 ; number:1 ; pages:8

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DOI / URN:	10.1186/s12859-020-3380-6

Katalog-ID:	DOAJ071692096

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520			\|a Abstract Background The analysis and comparison of RNA m6A methylation profiles have become increasingly important for understanding the post-transcriptional regulations of gene expression. However, current m6A profiles in public databases are not readily intercomparable, where heterogeneous profiles from the same experimental report but different cell types showed unwanted high correlations. Results Several normalizing or correcting methods were tested to remove such laboratory bias. And m6Acorr, an effective pipeline for correcting m6A profiles, was presented on the basis of quantile normalization and empirical Bayes batch regression method. m6Acorr could efficiently correct laboratory bias in the simulated dataset and real m6A profiles in public databases. The preservation of biological signals was examined after correction, and m6Acorr was found to better preserve differential methylation signals, m6A regulated targets, and m6A-related biological features than alternative methods. Finally, the m6Acorr server was established. This server could eliminate the potential laboratory bias in m6A methylation profiles and perform profile–profile comparisons and functional analysis of hyper- (hypo-) methylated genes based on corrected methylation profiles. Conclusion m6Acorr was established to correct the existing laboratory bias in RNA m6A methylation profiles and perform profile comparisons on the corrected datasets. The m6Acorr server is available at http://www.rnanut.net/m6Acorr. A stand-alone version with the correction function is also available in GitHub at https://github.com/emersON106/m6Acorr.
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allfields	10.1186/s12859-020-3380-6 doi (DE-627)DOAJ071692096 (DE-599)DOAJ2e8b6ff650c04bbab79c3968bb795e65 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Jianwei Li verfasserin aut m6Acorr: an online tool for the correction and comparison of m6A methylation profiles 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The analysis and comparison of RNA m6A methylation profiles have become increasingly important for understanding the post-transcriptional regulations of gene expression. However, current m6A profiles in public databases are not readily intercomparable, where heterogeneous profiles from the same experimental report but different cell types showed unwanted high correlations. Results Several normalizing or correcting methods were tested to remove such laboratory bias. And m6Acorr, an effective pipeline for correcting m6A profiles, was presented on the basis of quantile normalization and empirical Bayes batch regression method. m6Acorr could efficiently correct laboratory bias in the simulated dataset and real m6A profiles in public databases. The preservation of biological signals was examined after correction, and m6Acorr was found to better preserve differential methylation signals, m6A regulated targets, and m6A-related biological features than alternative methods. Finally, the m6Acorr server was established. This server could eliminate the potential laboratory bias in m6A methylation profiles and perform profile–profile comparisons and functional analysis of hyper- (hypo-) methylated genes based on corrected methylation profiles. Conclusion m6Acorr was established to correct the existing laboratory bias in RNA m6A methylation profiles and perform profile comparisons on the corrected datasets. The m6Acorr server is available at http://www.rnanut.net/m6Acorr. A stand-alone version with the correction function is also available in GitHub at https://github.com/emersON106/m6Acorr. Computer applications to medicine. Medical informatics Biology (General) Yan Huang verfasserin aut Qinghua Cui verfasserin aut Yuan Zhou verfasserin aut In BMC Bioinformatics BMC, 2003 21(2020), 1, Seite 8 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:21 year:2020 number:1 pages:8 https://doi.org/10.1186/s12859-020-3380-6 kostenfrei https://doaj.org/article/2e8b6ff650c04bbab79c3968bb795e65 kostenfrei https://doi.org/10.1186/s12859-020-3380-6 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 8
spelling	10.1186/s12859-020-3380-6 doi (DE-627)DOAJ071692096 (DE-599)DOAJ2e8b6ff650c04bbab79c3968bb795e65 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Jianwei Li verfasserin aut m6Acorr: an online tool for the correction and comparison of m6A methylation profiles 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The analysis and comparison of RNA m6A methylation profiles have become increasingly important for understanding the post-transcriptional regulations of gene expression. However, current m6A profiles in public databases are not readily intercomparable, where heterogeneous profiles from the same experimental report but different cell types showed unwanted high correlations. Results Several normalizing or correcting methods were tested to remove such laboratory bias. And m6Acorr, an effective pipeline for correcting m6A profiles, was presented on the basis of quantile normalization and empirical Bayes batch regression method. m6Acorr could efficiently correct laboratory bias in the simulated dataset and real m6A profiles in public databases. The preservation of biological signals was examined after correction, and m6Acorr was found to better preserve differential methylation signals, m6A regulated targets, and m6A-related biological features than alternative methods. Finally, the m6Acorr server was established. This server could eliminate the potential laboratory bias in m6A methylation profiles and perform profile–profile comparisons and functional analysis of hyper- (hypo-) methylated genes based on corrected methylation profiles. Conclusion m6Acorr was established to correct the existing laboratory bias in RNA m6A methylation profiles and perform profile comparisons on the corrected datasets. The m6Acorr server is available at http://www.rnanut.net/m6Acorr. A stand-alone version with the correction function is also available in GitHub at https://github.com/emersON106/m6Acorr. Computer applications to medicine. Medical informatics Biology (General) Yan Huang verfasserin aut Qinghua Cui verfasserin aut Yuan Zhou verfasserin aut In BMC Bioinformatics BMC, 2003 21(2020), 1, Seite 8 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:21 year:2020 number:1 pages:8 https://doi.org/10.1186/s12859-020-3380-6 kostenfrei https://doaj.org/article/2e8b6ff650c04bbab79c3968bb795e65 kostenfrei https://doi.org/10.1186/s12859-020-3380-6 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 8
allfields_unstemmed	10.1186/s12859-020-3380-6 doi (DE-627)DOAJ071692096 (DE-599)DOAJ2e8b6ff650c04bbab79c3968bb795e65 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Jianwei Li verfasserin aut m6Acorr: an online tool for the correction and comparison of m6A methylation profiles 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The analysis and comparison of RNA m6A methylation profiles have become increasingly important for understanding the post-transcriptional regulations of gene expression. However, current m6A profiles in public databases are not readily intercomparable, where heterogeneous profiles from the same experimental report but different cell types showed unwanted high correlations. Results Several normalizing or correcting methods were tested to remove such laboratory bias. And m6Acorr, an effective pipeline for correcting m6A profiles, was presented on the basis of quantile normalization and empirical Bayes batch regression method. m6Acorr could efficiently correct laboratory bias in the simulated dataset and real m6A profiles in public databases. The preservation of biological signals was examined after correction, and m6Acorr was found to better preserve differential methylation signals, m6A regulated targets, and m6A-related biological features than alternative methods. Finally, the m6Acorr server was established. This server could eliminate the potential laboratory bias in m6A methylation profiles and perform profile–profile comparisons and functional analysis of hyper- (hypo-) methylated genes based on corrected methylation profiles. Conclusion m6Acorr was established to correct the existing laboratory bias in RNA m6A methylation profiles and perform profile comparisons on the corrected datasets. The m6Acorr server is available at http://www.rnanut.net/m6Acorr. A stand-alone version with the correction function is also available in GitHub at https://github.com/emersON106/m6Acorr. Computer applications to medicine. Medical informatics Biology (General) Yan Huang verfasserin aut Qinghua Cui verfasserin aut Yuan Zhou verfasserin aut In BMC Bioinformatics BMC, 2003 21(2020), 1, Seite 8 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:21 year:2020 number:1 pages:8 https://doi.org/10.1186/s12859-020-3380-6 kostenfrei https://doaj.org/article/2e8b6ff650c04bbab79c3968bb795e65 kostenfrei https://doi.org/10.1186/s12859-020-3380-6 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 8
allfieldsGer	10.1186/s12859-020-3380-6 doi (DE-627)DOAJ071692096 (DE-599)DOAJ2e8b6ff650c04bbab79c3968bb795e65 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Jianwei Li verfasserin aut m6Acorr: an online tool for the correction and comparison of m6A methylation profiles 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The analysis and comparison of RNA m6A methylation profiles have become increasingly important for understanding the post-transcriptional regulations of gene expression. However, current m6A profiles in public databases are not readily intercomparable, where heterogeneous profiles from the same experimental report but different cell types showed unwanted high correlations. Results Several normalizing or correcting methods were tested to remove such laboratory bias. And m6Acorr, an effective pipeline for correcting m6A profiles, was presented on the basis of quantile normalization and empirical Bayes batch regression method. m6Acorr could efficiently correct laboratory bias in the simulated dataset and real m6A profiles in public databases. The preservation of biological signals was examined after correction, and m6Acorr was found to better preserve differential methylation signals, m6A regulated targets, and m6A-related biological features than alternative methods. Finally, the m6Acorr server was established. This server could eliminate the potential laboratory bias in m6A methylation profiles and perform profile–profile comparisons and functional analysis of hyper- (hypo-) methylated genes based on corrected methylation profiles. Conclusion m6Acorr was established to correct the existing laboratory bias in RNA m6A methylation profiles and perform profile comparisons on the corrected datasets. The m6Acorr server is available at http://www.rnanut.net/m6Acorr. A stand-alone version with the correction function is also available in GitHub at https://github.com/emersON106/m6Acorr. Computer applications to medicine. Medical informatics Biology (General) Yan Huang verfasserin aut Qinghua Cui verfasserin aut Yuan Zhou verfasserin aut In BMC Bioinformatics BMC, 2003 21(2020), 1, Seite 8 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:21 year:2020 number:1 pages:8 https://doi.org/10.1186/s12859-020-3380-6 kostenfrei https://doaj.org/article/2e8b6ff650c04bbab79c3968bb795e65 kostenfrei https://doi.org/10.1186/s12859-020-3380-6 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 8
allfieldsSound	10.1186/s12859-020-3380-6 doi (DE-627)DOAJ071692096 (DE-599)DOAJ2e8b6ff650c04bbab79c3968bb795e65 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Jianwei Li verfasserin aut m6Acorr: an online tool for the correction and comparison of m6A methylation profiles 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The analysis and comparison of RNA m6A methylation profiles have become increasingly important for understanding the post-transcriptional regulations of gene expression. However, current m6A profiles in public databases are not readily intercomparable, where heterogeneous profiles from the same experimental report but different cell types showed unwanted high correlations. Results Several normalizing or correcting methods were tested to remove such laboratory bias. And m6Acorr, an effective pipeline for correcting m6A profiles, was presented on the basis of quantile normalization and empirical Bayes batch regression method. m6Acorr could efficiently correct laboratory bias in the simulated dataset and real m6A profiles in public databases. The preservation of biological signals was examined after correction, and m6Acorr was found to better preserve differential methylation signals, m6A regulated targets, and m6A-related biological features than alternative methods. Finally, the m6Acorr server was established. This server could eliminate the potential laboratory bias in m6A methylation profiles and perform profile–profile comparisons and functional analysis of hyper- (hypo-) methylated genes based on corrected methylation profiles. Conclusion m6Acorr was established to correct the existing laboratory bias in RNA m6A methylation profiles and perform profile comparisons on the corrected datasets. The m6Acorr server is available at http://www.rnanut.net/m6Acorr. A stand-alone version with the correction function is also available in GitHub at https://github.com/emersON106/m6Acorr. Computer applications to medicine. Medical informatics Biology (General) Yan Huang verfasserin aut Qinghua Cui verfasserin aut Yuan Zhou verfasserin aut In BMC Bioinformatics BMC, 2003 21(2020), 1, Seite 8 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:21 year:2020 number:1 pages:8 https://doi.org/10.1186/s12859-020-3380-6 kostenfrei https://doaj.org/article/2e8b6ff650c04bbab79c3968bb795e65 kostenfrei https://doi.org/10.1186/s12859-020-3380-6 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 8
language	English
source	In BMC Bioinformatics 21(2020), 1, Seite 8 volume:21 year:2020 number:1 pages:8
sourceStr	In BMC Bioinformatics 21(2020), 1, Seite 8 volume:21 year:2020 number:1 pages:8
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Computer applications to medicine. Medical informatics Biology (General)
isfreeaccess_bool	true
container_title	BMC Bioinformatics
authorswithroles_txt_mv	Jianwei Li @@aut@@ Yan Huang @@aut@@ Qinghua Cui @@aut@@ Yuan Zhou @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	326644814
id	DOAJ071692096
language_de	englisch
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callnumber-first	R - Medicine
author	Jianwei Li
spellingShingle	Jianwei Li misc R858-859.7 misc QH301-705.5 misc Computer applications to medicine. Medical informatics misc Biology (General) m6Acorr: an online tool for the correction and comparison of m6A methylation profiles
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topic_title	R858-859.7 QH301-705.5 m6Acorr: an online tool for the correction and comparison of m6A methylation profiles
topic	misc R858-859.7 misc QH301-705.5 misc Computer applications to medicine. Medical informatics misc Biology (General)
topic_unstemmed	misc R858-859.7 misc QH301-705.5 misc Computer applications to medicine. Medical informatics misc Biology (General)
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title	m6Acorr: an online tool for the correction and comparison of m6A methylation profiles
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title_full	m6Acorr: an online tool for the correction and comparison of m6A methylation profiles
author_sort	Jianwei Li
journal	BMC Bioinformatics
journalStr	BMC Bioinformatics
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author_browse	Jianwei Li Yan Huang Qinghua Cui Yuan Zhou
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author-letter	Jianwei Li
doi_str_mv	10.1186/s12859-020-3380-6
author2-role	verfasserin
title_sort	m6acorr: an online tool for the correction and comparison of m6a methylation profiles
callnumber	R858-859.7
title_auth	m6Acorr: an online tool for the correction and comparison of m6A methylation profiles
abstract	Abstract Background The analysis and comparison of RNA m6A methylation profiles have become increasingly important for understanding the post-transcriptional regulations of gene expression. However, current m6A profiles in public databases are not readily intercomparable, where heterogeneous profiles from the same experimental report but different cell types showed unwanted high correlations. Results Several normalizing or correcting methods were tested to remove such laboratory bias. And m6Acorr, an effective pipeline for correcting m6A profiles, was presented on the basis of quantile normalization and empirical Bayes batch regression method. m6Acorr could efficiently correct laboratory bias in the simulated dataset and real m6A profiles in public databases. The preservation of biological signals was examined after correction, and m6Acorr was found to better preserve differential methylation signals, m6A regulated targets, and m6A-related biological features than alternative methods. Finally, the m6Acorr server was established. This server could eliminate the potential laboratory bias in m6A methylation profiles and perform profile–profile comparisons and functional analysis of hyper- (hypo-) methylated genes based on corrected methylation profiles. Conclusion m6Acorr was established to correct the existing laboratory bias in RNA m6A methylation profiles and perform profile comparisons on the corrected datasets. The m6Acorr server is available at http://www.rnanut.net/m6Acorr. A stand-alone version with the correction function is also available in GitHub at https://github.com/emersON106/m6Acorr.
abstractGer	Abstract Background The analysis and comparison of RNA m6A methylation profiles have become increasingly important for understanding the post-transcriptional regulations of gene expression. However, current m6A profiles in public databases are not readily intercomparable, where heterogeneous profiles from the same experimental report but different cell types showed unwanted high correlations. Results Several normalizing or correcting methods were tested to remove such laboratory bias. And m6Acorr, an effective pipeline for correcting m6A profiles, was presented on the basis of quantile normalization and empirical Bayes batch regression method. m6Acorr could efficiently correct laboratory bias in the simulated dataset and real m6A profiles in public databases. The preservation of biological signals was examined after correction, and m6Acorr was found to better preserve differential methylation signals, m6A regulated targets, and m6A-related biological features than alternative methods. Finally, the m6Acorr server was established. This server could eliminate the potential laboratory bias in m6A methylation profiles and perform profile–profile comparisons and functional analysis of hyper- (hypo-) methylated genes based on corrected methylation profiles. Conclusion m6Acorr was established to correct the existing laboratory bias in RNA m6A methylation profiles and perform profile comparisons on the corrected datasets. The m6Acorr server is available at http://www.rnanut.net/m6Acorr. A stand-alone version with the correction function is also available in GitHub at https://github.com/emersON106/m6Acorr.
abstract_unstemmed	Abstract Background The analysis and comparison of RNA m6A methylation profiles have become increasingly important for understanding the post-transcriptional regulations of gene expression. However, current m6A profiles in public databases are not readily intercomparable, where heterogeneous profiles from the same experimental report but different cell types showed unwanted high correlations. Results Several normalizing or correcting methods were tested to remove such laboratory bias. And m6Acorr, an effective pipeline for correcting m6A profiles, was presented on the basis of quantile normalization and empirical Bayes batch regression method. m6Acorr could efficiently correct laboratory bias in the simulated dataset and real m6A profiles in public databases. The preservation of biological signals was examined after correction, and m6Acorr was found to better preserve differential methylation signals, m6A regulated targets, and m6A-related biological features than alternative methods. Finally, the m6Acorr server was established. This server could eliminate the potential laboratory bias in m6A methylation profiles and perform profile–profile comparisons and functional analysis of hyper- (hypo-) methylated genes based on corrected methylation profiles. Conclusion m6Acorr was established to correct the existing laboratory bias in RNA m6A methylation profiles and perform profile comparisons on the corrected datasets. The m6Acorr server is available at http://www.rnanut.net/m6Acorr. A stand-alone version with the correction function is also available in GitHub at https://github.com/emersON106/m6Acorr.
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title_short	m6Acorr: an online tool for the correction and comparison of m6A methylation profiles
url	https://doi.org/10.1186/s12859-020-3380-6 https://doaj.org/article/2e8b6ff650c04bbab79c3968bb795e65 https://doaj.org/toc/1471-2105
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doi_str	10.1186/s12859-020-3380-6
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up_date	2024-07-03T21:40:28.050Z
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m6Acorr: an online tool for the correction and comparison of m6A methylation profiles

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