Back

COVID-19 CG: Tracking SARS-CoV-2 mutations by locations and dates of interest

Chen, A. T.; Altschuler, K.; Zhan, S. H.; Chan, Y. A.; Deverman, B. E.

2020-09-24 bioinformatics
10.1101/2020.09.23.310565 bioRxiv
Show abstract

COVID-19 CG is an open resource for tracking SARS-CoV-2 single-nucleotide variations (SNVs) and lineages while filtering by location, date, gene, and mutation of interest. COVID-19 CG provides significant time, labor, and cost-saving utility to diverse projects on SARS-CoV-2 transmission, evolution, emergence, immune interactions, diagnostics, therapeutics, vaccines, and intervention tracking. Here, we describe case studies in which users can interrogate (1) SNVs in the SARS-CoV-2 Spike receptor binding domain (RBD) across different geographic regions to inform the design and testing of therapeutics, (2) SNVs that may impact the sensitivity of commonly used diagnostic primers, and (3) the recent emergence of a dominant lineage harboring an S477N RBD mutation in Australia. To accelerate COVID-19 research and public health efforts, COVID-19 CG will be continually upgraded with new features for users to quickly and reliably pinpoint mutations as the virus evolves throughout the pandemic and in response to therapeutic and public health interventions.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Virus Evolution
◐ 155 papers in training set
Top 0.1%
18.3%
Similar papers in this journal
2
Molecular Biology and Evolution
◐ 542 papers in training set
Top 1%
7.2%
Similar papers in this journal
3
Bioinformatics Advances
◐ 203 papers in training set
Top 1%
4.8%
Similar papers in this journal
4
eLife
● 5828 papers in training set
Top 26%
4.8%
Similar papers in this journal
5
Nucleic Acids Research
◐ 1281 papers in training set
Top 4%
4.8%
Similar papers in this journal
6
Bioinformatics
◐ 1204 papers in training set
Top 5%
4.3%
7
Database
◐ 61 papers in training set
Top 0.2%
3.4%
8
GigaScience
◐ 212 papers in training set
Top 1%
3.2%
50% of probability mass above
9
Nature Communications
○ 5641 papers in training set
Top 36%
3.2%
10
Cell
○ 431 papers in training set
Top 4%
2.8%
11
PeerJ
◐ 308 papers in training set
Top 3%
2.6%
12
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.6%
13
Influenza and Other Respiratory Viruses
○ 46 papers in training set
Top 0.3%
2.4%
14
PLOS ONE
● 5266 papers in training set
Top 46%
1.7%
15
iScience
○ 1154 papers in training set
Top 19%
1.7%
16
G3: Genes, Genomes, Genetics
◐ 252 papers in training set
Top 3%
1.7%
17
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.3%
18
Microbial Genomics
● 225 papers in training set
Top 2%
1.3%
19
Viruses
○ 332 papers in training set
Top 3%
1.3%
20
BMC Bioinformatics
○ 457 papers in training set
Top 4%
1.3%
21
Nature Methods
○ 385 papers in training set
Top 5%
1.1%
22
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 3%
1.1%
23
Frontiers in Bioinformatics
○ 49 papers in training set
Top 1%
1.1%
24
PLOS Computational Biology
● 1863 papers in training set
Top 18%
1.0%
25
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 6%
1.0%
26
Wellcome Open Research
○ 67 papers in training set
Top 1%
1.0%
27
Journal of Open Source Software
● 25 papers in training set
Top 0.4%
0.8%
28
Genome Medicine
○ 183 papers in training set
Top 5%
0.8%
29
Journal of Bioinformatics and Systems Biology
○ 15 papers in training set
Top 0.4%
0.6%
30
mSphere
● 302 papers in training set
Top 8%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.