Evaluation of variant calling algorithms for wastewater-based epidemiology using mixed populations of SARS-CoV-2 variants in synthetic and wastewater samples.
Evaluation of variant calling algorithms for wastewater-based epidemiology using mixed populations of SARS-CoV-2 variants in synthetic and wastewater samples.
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DOI：
10.1099/mgen.0.000933
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发表时间：
2023-04
影响因子：
3.9
通讯作者：
Grimsley, Jasmine M. S.
中科院分区：
文献类型：
--
作者：
Bassano, Irene;Ramachandran, Vinoy K.;Khalifa, Mohammad S.;Lilley, Chris J.;Brown, Mathew R.;van Aerle, Ronny;Denise, Hubert;Rowe, William;George, Airey;Cairns, Edward;Wierzbicki, Claudia;Pickwell, Natalie D.;Carlile, Matthew;Holmes, Nadine;Payne, Alexander;Loose, Matthew;Burke, Terry A.;Paterson, Steve;Wade, Matthew J.;Grimsley, Jasmine M. S.
在COVID-19（冠状病毒病19）大流行期间，广泛使用了基于水的流行病学来检测和监测SARS-CoV-2（严重急性呼吸系统综合征冠状病毒2型）及其变体的传播和流行。它已被证明是临床测序的一个出色的补充工具，支持所获得的见解，并帮助做出明智的公共卫生决策。因此，全球许多团体已经开发了生物信息学管道来分析废水中的测序数据。在这个过程中，准确的突变调用是至关重要的，在分配循环的变体;然而，到目前为止，在废水样品中的变体调用算法的性能还没有被调查。为了解决这个问题，我们比较了生物信息学管道中广泛使用的六种变体调用程序（VarScan，iVar，GATK，FreeBayes，LoFreq和BCFtools）在19个合成样本上的性能，这些样本具有已知比例的三种不同的SARS-CoV-2变体（VOC）（Alpha，Beta和Delta），以及2021年12月15日至18日在伦敦收集的13个废水样本。我们使用召回率（灵敏度）和精确度（特异性）的基本参数来确认定义六个变体调用者中特定变体的突变谱的存在。我们的研究结果表明，BCFtools，FreeBayes和VarScan发现了比GATK或iVar更高精度和召回率的预期变体，尽管后者比其他调用者识别出更多预期的定义突变。由于检测到大量假阳性突变，LoFreq给出的结果最不可靠，导致精度较低。对于合成样品和废水样品两者获得了类似的结果。
Wastewater-based epidemiology has been used extensively throughout the COVID-19 (coronavirus disease 19) pandemic to detect and monitor the spread and prevalence of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and its variants. It has proven an excellent, complementary tool to clinical sequencing, supporting the insights gained and helping to make informed public-health decisions. Consequently, many groups globally have developed bioinformatics pipelines to analyse sequencing data from wastewater. Accurate calling of mutations is critical in this process and in the assignment of circulating variants; yet, to date, the performance of variant-calling algorithms in wastewater samples has not been investigated. To address this, we compared the performance of six variant callers (VarScan, iVar, GATK, FreeBayes, LoFreq and BCFtools), used widely in bioinformatics pipelines, on 19 synthetic samples with known ratios of three different SARS-CoV-2 variants of concern (VOCs) (Alpha, Beta and Delta), as well as 13 wastewater samples collected in London between the 15th and 18th December 2021. We used the fundamental parameters of recall (sensitivity) and precision (specificity) to confirm the presence of mutational profiles defining specific variants across the six variant callers. Our results show that BCFtools, FreeBayes and VarScan found the expected variants with higher precision and recall than GATK or iVar, although the latter identified more expected defining mutations than other callers. LoFreq gave the least reliable results due to the high number of false-positive mutations detected, resulting in lower precision. Similar results were obtained for both the synthetic and wastewater samples.
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影响因子：
3
作者：
Bian X;Zhu B;Wang M;Hu Y;Chen Q;Nguyen C;Hicks B;Meerzaman D
通讯作者：
Meerzaman D
影响因子：
46.9
作者：
Olm, Matthew R.;Crits-Christoph, Alexander;Bouma-Gregson, Keith;Firek, Brian A.;Morowitz, Michael J.;Banfield, Jillian F.
通讯作者：
Banfield, Jillian F.
DOI：
10.3390/ijerph17249251
发表时间：
2020-12-10
影响因子：
--
作者：
Aguiar-Oliveira ML;Campos A;R Matos A;Rigotto C;Sotero-Martins A;Teixeira PFP;Siqueira MM
通讯作者：
Siqueira MM
影响因子：
3.7
作者：
Costea PI;Munch R;Coelho LP;Paoli L;Sunagawa S;Bork P
通讯作者：
Bork P
影响因子：
9.5
作者：
Deng ZL;Dhingra A;Fritz A;Götting J;Münch PC;Steinbrück L;Schulz TF;Ganzenmüller T;McHardy AC
通讯作者：
McHardy AC