Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest.
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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DOI：
10.1186/s12859-022-05105-y
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发表时间：
2022-12-19
影响因子：
3
通讯作者：
中科院分区：
文献类型：
--
作者：
截至2022年6月，GISAID数据库包含超过1100万个SARS-CoV-2基因组，包括最常见变体（如delta或omicron）的数千个核苷酸序列。这些SARS-CoV-2病毒株是自大流行开始以来从世界各地的患者身上收集的。我们首先使用Jaccard指数和主成分分析评估所有对核苷酸序列的相似性。如先前在文献中所示，应用于SARS-CoV-2基因组的无监督聚类分析根据某些特征（例如其菌株或其进化枝）产生序列聚类。重要的是，我们观察到常见变异的核苷酸序列通常是源于大流行期间早期鉴定的变异的序列簇中的离群值。受这一发现的启发，我们有兴趣将离群值检测应用于核苷酸序列。我们证明了常见变异体（如α，δ或omicron）的核苷酸序列可以仅基于统计离群值标准进行鉴定。我们认为，离群值检测可能是一个有用的监测工具，以确定新出现的变种在真实的时间作为大流行的进展。 在线版本包含补充材料，可在10.1186/s12859-022-05105-y获得。
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. The online version contains supplementary material available at 10.1186/s12859-022-05105-y.
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影响因子：
28.3
作者：
通讯作者：
--
影响因子：
4.6
作者：
Schiøler H;Knudsen T;Brøndum RF;Stoustrup J;Bøgsted M
通讯作者：
Bøgsted M
LOCSTRA：全基因组测序研究中区域/全球分层的快速分析。
影响因子：
2.1
作者：
Hahn G;Lutz SM;Hecker J;Prokopenko D;Cho MH;Silverman EK;Weiss ST;Lange C;NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
通讯作者：
NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
DOI：
10.3390/v14081840
发表时间：
2022-08-22
期刊：
Viruses
影响因子：
--
作者：
通讯作者：
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影响因子：
2.1
作者：
Hahn, Georg;Wu, Chloe M.;Lange, Christoph
通讯作者：
Lange, Christoph