Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR

Olson, Robert; Assaf, Rida; Brettin, Thomas; Conrad, Neal; Cucinell, Clark A; Davis, James J.; Dempsey, Donald M.; Dickerman, Allan; Dietrich, Emily; Kenyon, Ronald W.; Kuscuoglu, Mehmet; Lefkowitz, Elliot J.; Lü, Jian; Machi, Dustin; Macken, Catherine A.; Mao, Chunhong; Niewiadomska, Anna Maria; Nguyen, M.; Olsen, Gary J.; Overbeek, Jamie C.; Parrello, Bruce; Parrello, Victoria; Porter, Jacob; Pusch, Gordon D.; Shukla, Maulik; Singh, Indresh; Stewart, Lucy C.; Tan, Gene S.; Thomas, Chris S.; VanOeffelen, Margo; Vonstein, Veronika; Wallace, Zachary S; Warren, Andrew; Wattam, Alice R.; Xia, Fangfang; Yoo, Hyunseung; Zhang, Yun; Zmasek, Christian M.; Scheuermann, Richard H.; Stevens, Rick

doi:10.1093/nar/gkac1003

Cited by 314 publications

(283 citation statements)

References 62 publications

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“…We investigate the development of 21 SARS-CoV-2 lineages in 27 different countries with a weekly resolution, from October Week 3 2020 to September Week 4 2022 (94 weeks in total). The lineages we consider are selected among those monitored by the BV-BRC SARS-CoV-2 tracking system [5, 34], see Table 1. Since shared mutations among lineages transcend into similarities of their corresponding motifs, lineages are selected such that there is no substantial overlap of their characteristic mutations, see the Materials and Methods section for details of this selection procedure.…”

Section: Resultsmentioning

confidence: 99%

“…Lineage selection. We begin with all 28 lineages of concern that are currently or have been previously monitored by the BV-BRC SARS-CoV-2 tracking system [5,34]. A lineage of concern (LOC) is a group of closely related viral sequences that exhibit similar mutations or recombinations that may significantly affect vaccine efficacy, transmissibility or disease outcomes [5].…”

Section: Methodsmentioning

confidence: 99%

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Motifs in Sars-Cov-2 Evolution

Barrett

Bura

et al. 2023

Preprint

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We present a novel framework enhancing the prediction of whether novel lineage poses the threat of eventually dominating the viral population. The framework is based purely on genomic sequence data, without requiring prior established biological analysis. Its building blocks are sets of co-evolving sites in the alignment (motifs), identified via co-evolutionary signals. The collection of such motifs forms a relational structure over the polymorphic sites. Motifs are constructed using distances quantifying the co-evolutionary coupling of pairs and manifest as co-evolving clusters of sites. We present an approach to genomic surveillance based on this notion of relational structure. Our system will issue an alert regarding a lineage, based on its contribution to drastic changes in the relational structure. We then conduct a comprehensive retrospective analysis of the COVID-19 pandemic based on SARS-CoV-2 genomic sequence data in GISAID from October 2020 to September 2022, across 21 lineages and 27 countries with weekly resolution. We investigate the performance of this surveillance system in terms of its accuracy, timeliness and robustness. Lastly, we study how well each lineage is classified by such a system.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Motifs in Sars-Cov-2 Evolution

Barrett

Bura

et al. 2023

Preprint

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“…Two hundred sixty-eight samples were sequenced on an Illumina HiSeq, and 989 of the samples were sequenced using an Oxford Nanopore GridION instrument. Genomes were assembled with the BV-BRC SARS-CoV-2 assembly service (39)(https://www.bv-brc.org/app/ComprehensiveSARS2Analysis), which performs a reference-based assembly against the Wuhan-Hu-1 reference genome (GenBank ID: MN908947.3). The pipeline uses minimap version 2.143 (40) for aligning reads against the reference and iVar version 1.2.2 for primer trimming and SNP calling (41).…”

Section: Methodsmentioning

confidence: 99%

Using Genome Sequence Data to Predict SARS-CoV-2 Detection Cycle Threshold Values

Duesterwald

Nguyen

Christensen

et al. 2022

Preprint

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The continuing emergence of SARS-CoV-2 variants of concern (VOCs) presents a serious public health threat, exacerbating the effects of the COVID19 pandemic. Although millions of genomes have been deposited in public archives since the start of the pandemic, predicting SARS-CoV-2 clinical characteristics from the genome sequence remains challenging. In this study, we used a collection of over 29,000 high quality SARS-CoV-2 genomes to build machine learning models for predicting clinical detection cycle threshold (Ct) values, which correspond with viral load. After evaluating several machine learning methods and parameters, our best model was a random forest regressor that used 10-mer oligonucleotides as features and achieved an R2 score of 0.521 +/- 0.010 (95% confidence interval over 5 folds) and an RMSE of 5.7 +/- 0.034, demonstrating the ability of the models to detect the presence of a signal in the genomic data. In an attempt to predict Ct values for newly emerging variants, we predicted Ct values for Omicron variants using models trained on previous variants. We found that approximately 5% of the data in the model needed to be from the new variant in order to learn its Ct values. Finally, to understand how the model is working, we evaluated the top features and found that the model is using a multitude of k-mers from across the genome to make the predictions. However, when we looked at the top k-mers that occurred most frequently across the set of genomes, we observed a clustering of k-mers that span spike protein regions corresponding with key variations that are hallmarks of the VOCs including G339, K417, L452, N501, and P681, indicating that these sites are informative in the model and may impact the Ct values that are observed in clinical samples.

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“…The section on microbial and viral genomics leads off with a paper from the newly formed Bacterial and Viral Bioinformatics Resource Center ( 46 ), the successor to no fewer than three individual databases familiar to NAR readers - PATRIC ( 47 ), IRD ( 48 ) and ViPR ( 49 ). Resources hitherto confined to either bacterial or viral databases have been made available to both communities.…”

Section: New and Updated Databasesmentioning

confidence: 99%

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection

Rigden

Fernández

2023

Nucleic Acids Research

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The 2023 Nucleic Acids Research Database Issue contains 178 papers ranging across biology and related fields. There are 90 papers reporting on new databases and 82 updates from resources previously published in the Issue. Six more papers are updates from databases most recently published elsewhere. Major nucleic acid databases reporting updates include Genbank, ENA, ChIPBase, JASPAR, mirDIP and the Issue's first Breakthrough Article, NACDDB for Circular Dichroism data. Updates from BMRB and RCSB cover experimental protein structural data while AlphaFold 2 computational structure predictions feature widely. STRING and REBASE are stand-out updates in the signalling and enzymes section. Immunology-related databases include CEDAR, the second Breakthrough Article, for cancer epitopes and receptors alongside returning IPD-IMGT/HLA and the new PGG.MHC. Genomics-related resources include Ensembl, GWAS Central and UCSC Genome Browser. Major returning databases for drugs and their targets include Open Targets, DrugCentral, CTD and Pubchem. The EMPIAR image archive appears in the Issue for the first time. The entire database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, revisiting 463 entries, adding 92 new resources and eliminating 96 discontinued URLs so bringing the current total to 1764 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.

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Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR

Cited by 314 publications

References 62 publications

Motifs in Sars-Cov-2 Evolution

Motifs in Sars-Cov-2 Evolution

Using Genome Sequence Data to Predict SARS-CoV-2 Detection Cycle Threshold Values

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection

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