Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest

Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of th...
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Autor*in:	Georg Hahn [verfasserIn] Sanghun Lee [verfasserIn] Dmitry Prokopenko [verfasserIn] Jonathan Abraham [verfasserIn] Tanya Novak [verfasserIn] Julian Hecker [verfasserIn] Michael Cho [verfasserIn] Surender Khurana [verfasserIn] Lindsey R. Baden [verfasserIn] Adrienne G. Randolph [verfasserIn] Scott T. Weiss [verfasserIn] Christoph Lange [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	SARS-CoV-2 Nucleotide sequences Outlier detection Variants of interest Machine learning

Übergeordnetes Werk:	In: BMC Bioinformatics - BMC, 2003, 23(2022), 1, Seite 18
Übergeordnetes Werk:	volume:23 ; year:2022 ; number:1 ; pages:18

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12859-022-05105-y

Katalog-ID:	DOAJ020773471

Internformat


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520			\|a Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.
650		4	\|a SARS-CoV-2
650		4	\|a Nucleotide sequences
650		4	\|a Outlier detection
650		4	\|a Variants of interest
650		4	\|a Machine learning
653		0	\|a Computer applications to medicine. Medical informatics
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700	0		\|a Scott T. Weiss \|e verfasserin \|4 aut
700	0		\|a Christoph Lange \|e verfasserin \|4 aut
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allfields	10.1186/s12859-022-05105-y doi (DE-627)DOAJ020773471 (DE-599)DOAJ428c807d66334f48a8d811a6960611da DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Georg Hahn verfasserin aut Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses. SARS-CoV-2 Nucleotide sequences Outlier detection Variants of interest Machine learning Computer applications to medicine. Medical informatics Biology (General) Sanghun Lee verfasserin aut Dmitry Prokopenko verfasserin aut Jonathan Abraham verfasserin aut Tanya Novak verfasserin aut Julian Hecker verfasserin aut Michael Cho verfasserin aut Surender Khurana verfasserin aut Lindsey R. Baden verfasserin aut Adrienne G. Randolph verfasserin aut Scott T. Weiss verfasserin aut Christoph Lange verfasserin aut In BMC Bioinformatics BMC, 2003 23(2022), 1, Seite 18 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:23 year:2022 number:1 pages:18 https://doi.org/10.1186/s12859-022-05105-y kostenfrei https://doaj.org/article/428c807d66334f48a8d811a6960611da kostenfrei https://doi.org/10.1186/s12859-022-05105-y 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 23 2022 1 18
spelling	10.1186/s12859-022-05105-y doi (DE-627)DOAJ020773471 (DE-599)DOAJ428c807d66334f48a8d811a6960611da DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Georg Hahn verfasserin aut Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses. SARS-CoV-2 Nucleotide sequences Outlier detection Variants of interest Machine learning Computer applications to medicine. Medical informatics Biology (General) Sanghun Lee verfasserin aut Dmitry Prokopenko verfasserin aut Jonathan Abraham verfasserin aut Tanya Novak verfasserin aut Julian Hecker verfasserin aut Michael Cho verfasserin aut Surender Khurana verfasserin aut Lindsey R. Baden verfasserin aut Adrienne G. Randolph verfasserin aut Scott T. Weiss verfasserin aut Christoph Lange verfasserin aut In BMC Bioinformatics BMC, 2003 23(2022), 1, Seite 18 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:23 year:2022 number:1 pages:18 https://doi.org/10.1186/s12859-022-05105-y kostenfrei https://doaj.org/article/428c807d66334f48a8d811a6960611da kostenfrei https://doi.org/10.1186/s12859-022-05105-y 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 23 2022 1 18
allfields_unstemmed	10.1186/s12859-022-05105-y doi (DE-627)DOAJ020773471 (DE-599)DOAJ428c807d66334f48a8d811a6960611da DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Georg Hahn verfasserin aut Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses. SARS-CoV-2 Nucleotide sequences Outlier detection Variants of interest Machine learning Computer applications to medicine. Medical informatics Biology (General) Sanghun Lee verfasserin aut Dmitry Prokopenko verfasserin aut Jonathan Abraham verfasserin aut Tanya Novak verfasserin aut Julian Hecker verfasserin aut Michael Cho verfasserin aut Surender Khurana verfasserin aut Lindsey R. Baden verfasserin aut Adrienne G. Randolph verfasserin aut Scott T. Weiss verfasserin aut Christoph Lange verfasserin aut In BMC Bioinformatics BMC, 2003 23(2022), 1, Seite 18 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:23 year:2022 number:1 pages:18 https://doi.org/10.1186/s12859-022-05105-y kostenfrei https://doaj.org/article/428c807d66334f48a8d811a6960611da kostenfrei https://doi.org/10.1186/s12859-022-05105-y 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 23 2022 1 18
allfieldsGer	10.1186/s12859-022-05105-y doi (DE-627)DOAJ020773471 (DE-599)DOAJ428c807d66334f48a8d811a6960611da DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Georg Hahn verfasserin aut Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses. SARS-CoV-2 Nucleotide sequences Outlier detection Variants of interest Machine learning Computer applications to medicine. Medical informatics Biology (General) Sanghun Lee verfasserin aut Dmitry Prokopenko verfasserin aut Jonathan Abraham verfasserin aut Tanya Novak verfasserin aut Julian Hecker verfasserin aut Michael Cho verfasserin aut Surender Khurana verfasserin aut Lindsey R. Baden verfasserin aut Adrienne G. Randolph verfasserin aut Scott T. Weiss verfasserin aut Christoph Lange verfasserin aut In BMC Bioinformatics BMC, 2003 23(2022), 1, Seite 18 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:23 year:2022 number:1 pages:18 https://doi.org/10.1186/s12859-022-05105-y kostenfrei https://doaj.org/article/428c807d66334f48a8d811a6960611da kostenfrei https://doi.org/10.1186/s12859-022-05105-y 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 23 2022 1 18
allfieldsSound	10.1186/s12859-022-05105-y doi (DE-627)DOAJ020773471 (DE-599)DOAJ428c807d66334f48a8d811a6960611da DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Georg Hahn verfasserin aut Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses. SARS-CoV-2 Nucleotide sequences Outlier detection Variants of interest Machine learning Computer applications to medicine. Medical informatics Biology (General) Sanghun Lee verfasserin aut Dmitry Prokopenko verfasserin aut Jonathan Abraham verfasserin aut Tanya Novak verfasserin aut Julian Hecker verfasserin aut Michael Cho verfasserin aut Surender Khurana verfasserin aut Lindsey R. Baden verfasserin aut Adrienne G. Randolph verfasserin aut Scott T. Weiss verfasserin aut Christoph Lange verfasserin aut In BMC Bioinformatics BMC, 2003 23(2022), 1, Seite 18 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:23 year:2022 number:1 pages:18 https://doi.org/10.1186/s12859-022-05105-y kostenfrei https://doaj.org/article/428c807d66334f48a8d811a6960611da kostenfrei https://doi.org/10.1186/s12859-022-05105-y 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 23 2022 1 18
language	English
source	In BMC Bioinformatics 23(2022), 1, Seite 18 volume:23 year:2022 number:1 pages:18
sourceStr	In BMC Bioinformatics 23(2022), 1, Seite 18 volume:23 year:2022 number:1 pages:18
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	SARS-CoV-2 Nucleotide sequences Outlier detection Variants of interest Machine learning Computer applications to medicine. Medical informatics Biology (General)
isfreeaccess_bool	true
container_title	BMC Bioinformatics
authorswithroles_txt_mv	Georg Hahn @@aut@@ Sanghun Lee @@aut@@ Dmitry Prokopenko @@aut@@ Jonathan Abraham @@aut@@ Tanya Novak @@aut@@ Julian Hecker @@aut@@ Michael Cho @@aut@@ Surender Khurana @@aut@@ Lindsey R. Baden @@aut@@ Adrienne G. Randolph @@aut@@ Scott T. Weiss @@aut@@ Christoph Lange @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	326644814
id	DOAJ020773471
language_de	englisch
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callnumber-first	R - Medicine
author	Georg Hahn
spellingShingle	Georg Hahn misc R858-859.7 misc QH301-705.5 misc SARS-CoV-2 misc Nucleotide sequences misc Outlier detection misc Variants of interest misc Machine learning misc Computer applications to medicine. Medical informatics misc Biology (General) Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest
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topic_title	R858-859.7 QH301-705.5 Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest SARS-CoV-2 Nucleotide sequences Outlier detection Variants of interest Machine learning
topic	misc R858-859.7 misc QH301-705.5 misc SARS-CoV-2 misc Nucleotide sequences misc Outlier detection misc Variants of interest misc Machine learning misc Computer applications to medicine. Medical informatics misc Biology (General)
topic_unstemmed	misc R858-859.7 misc QH301-705.5 misc SARS-CoV-2 misc Nucleotide sequences misc Outlier detection misc Variants of interest misc Machine learning misc Computer applications to medicine. Medical informatics misc Biology (General)
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title	Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest
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title_full	Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest
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author_browse	Georg Hahn Sanghun Lee Dmitry Prokopenko Jonathan Abraham Tanya Novak Julian Hecker Michael Cho Surender Khurana Lindsey R. Baden Adrienne G. Randolph Scott T. Weiss Christoph Lange
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title_sort	unsupervised outlier detection applied to sars-cov-2 nucleotide sequences can identify sequences of common variants and other variants of interest
callnumber	R858-859.7
title_auth	Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest
abstract	Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.
abstractGer	Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.
abstract_unstemmed	Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.
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title_short	Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest
url	https://doi.org/10.1186/s12859-022-05105-y https://doaj.org/article/428c807d66334f48a8d811a6960611da https://doaj.org/toc/1471-2105
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Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest

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