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Cenote-Taker 3 for Fast and Accurate Virus Discovery and Annotation of the Virome

Tisza, M. J.; Petrosino, J. F.; Cregeen, S. J. J.

2025-08-24 bioinformatics
10.1101/2025.08.20.671380 bioRxiv
Show abstract

Viruses are abundant across all Earths environments and infect all classes of cellular life. Despite this, viruses are something of a black box for genomics scientists. Their genetic diversity is greater than all other lifeforms combined, their genomes are often overlooked in sequencing datasets, they encode polyproteins, and no function can be inferred for a large majority of their encoded proteins. For these reasons, scientists need robust, performant, well-documented, extensible tools that can be deployed to conduct sensitive and specific analyses of sequencing data to discover virus genomes - even those with high divergence from known references - and annotate their genes. Here, we present Cenote-Taker 3. This command line interface tool processes genome assemblies and/or metagenomic assemblies with modules for virus discovery, prophage extraction, and annotation of genes and other genetic features. Benchmarks show that Cenote-Taker 3 outperforms most tools for virus gene annotation in both speed (wall time) and accuracy. For virus discovery benchmarks, Cenote-Taker 3 performs well compared to geNomad, and these tools produce complementary results. Cenote-Taker 3 is freely available on Bioconda, and its open-source code is maintained on GitHub (https://github.com/mtisza1/Cenote-Taker3).

Published in Peer Community Journal · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Genome Biology
○ 637 papers in training set
Top 0.9%
11.5%
Similar papers in this journal
2
Nature Biotechnology
○ 172 papers in training set
Top 0.2%
10.3%
Similar papers in this journal
3
Nature Methods
○ 385 papers in training set
Top 1.0%
10.3%
Similar papers in this journal
4
Nature Communications
○ 5641 papers in training set
Top 23%
7.0%
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5
Bioinformatics
◐ 1204 papers in training set
Top 4%
6.5%
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6
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 0.7%
5.3%
50% of probability mass above
7
iMeta
○ 10 papers in training set
Top 0.1%
4.7%
8
Virus Evolution
◐ 155 papers in training set
Top 0.5%
3.9%
9
Nucleic Acids Research
◐ 1281 papers in training set
Top 5%
3.9%
10
Microbiome
○ 154 papers in training set
Top 1%
3.1%
11
GigaScience
◐ 212 papers in training set
Top 1%
2.7%
12
Molecular Biology and Evolution
◐ 542 papers in training set
Top 3%
2.3%
13
mSystems
● 394 papers in training set
Top 4%
2.0%
14
Briefings in Bioinformatics
◐ 354 papers in training set
Top 4%
2.0%
15
PLOS Computational Biology
● 1863 papers in training set
Top 15%
1.4%
16
Viruses
○ 332 papers in training set
Top 4%
1.1%
17
Genome Research
● 468 papers in training set
Top 5%
1.1%
18
Bioinformatics Advances
◐ 203 papers in training set
Top 4%
1.1%
19
BMC Bioinformatics
○ 457 papers in training set
Top 5%
1.1%
20
Communications Biology
○ 993 papers in training set
Top 24%
1.1%
21
Scientific Reports
○ 3612 papers in training set
Top 68%
1.0%
22
iScience
○ 1154 papers in training set
Top 32%
1.0%
23
PeerJ
◐ 308 papers in training set
Top 10%
0.9%
24
Cell Reports Methods
○ 165 papers in training set
Top 4%
0.8%
25
Nature Protocols
○ 33 papers in training set
Top 0.6%
0.8%
26
Nature Microbiology
○ 155 papers in training set
Top 3%
0.8%
27
Nature
○ 645 papers in training set
Top 11%
0.8%
28
BMC Genomics
○ 406 papers in training set
Top 9%
0.6%
29
Microbial Genomics
● 225 papers in training set
Top 3%
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.