Back

CheckV: assessing the quality of metagenome-assembled viral genomes

Nayfach, S.; Camargo, A. P.; Eloe-Fadrosh, E.; Roux, S.; Kyrpides, N.

2020-05-08 bioinformatics
10.1101/2020.05.06.081778 bioRxiv
Show abstract

Over the last several years, metagenomics has enabled the assembly of millions of new viral sequences that have vastly expanded our knowledge of Earths viral diversity. However, these sequences range from small fragments to complete genomes and no tools currently exist for estimating their quality. To address this problem, we developed CheckV, which is an automated pipeline for estimating the completeness of viral genomes as well as the identification and removal of non-viral regions found on integrated proviruses. After validating the approach on mock datasets, CheckV was applied to large and diverse viral genome collections, including IMG/VR and the Global Ocean Virome, revealing that the majority of viral sequences were small fragments, with just 3.6% classified as high-quality (i.e. > 90% completeness) or complete genomes. Additionally, we found that removal of host contamination significantly improved identification of auxiliary metabolic genes and interpretation of viral-encoded functions. We expect CheckV will be broadly useful for all researchers studying and reporting viral genomes assembled from metagenomes. CheckV is freely available at: http://bitbucket.org/berkeleylab/CheckV.

Matching journals

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

1
Microbiome
○ 154 papers in training set
Top 0.1%
18.0%
Similar papers in this journal
2
Genome Biology
○ 637 papers in training set
Top 0.7%
12.6%
Similar papers in this journal
3
Nature Communications
○ 5641 papers in training set
Top 26%
6.1%
Similar papers in this journal
4
GigaScience
◐ 212 papers in training set
Top 0.4%
6.1%
Similar papers in this journal
5
Nucleic Acids Research
◐ 1281 papers in training set
Top 4%
4.3%
Similar papers in this journal
6
PLOS Computational Biology
● 1863 papers in training set
Top 9%
3.9%
50% of probability mass above
7
mSystems
● 394 papers in training set
Top 3%
3.1%
8
Bioinformatics
◐ 1204 papers in training set
Top 5%
3.1%
9
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
2.7%
10
BMC Bioinformatics
○ 457 papers in training set
Top 3%
2.3%
11
Nature Methods
○ 385 papers in training set
Top 4%
2.3%
12
Viruses
○ 332 papers in training set
Top 2%
2.3%
13
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.3%
14
Microbial Genomics
● 225 papers in training set
Top 1%
2.1%
15
BMC Biology
○ 265 papers in training set
Top 2%
1.9%
16
PLOS Biology
● 486 papers in training set
Top 4%
1.9%
17
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.7%
18
Virus Evolution
◐ 155 papers in training set
Top 0.8%
1.7%
19
PLOS ONE
● 5266 papers in training set
Top 51%
1.3%
20
Molecular Biology and Evolution
◐ 542 papers in training set
Top 4%
1.3%
21
BMC Genomics
○ 406 papers in training set
Top 5%
1.3%
22
iMeta
○ 10 papers in training set
Top 0.1%
1.3%
23
Advanced Science
○ 286 papers in training set
Top 7%
1.1%
24
ISME Communications
◐ 120 papers in training set
Top 2%
1.0%
25
iScience
○ 1154 papers in training set
Top 38%
0.8%
26
Genomics, Proteomics & Bioinformatics
188 papers in training set
Top 2%
0.8%
27
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 42%
0.8%
28
Nature Microbiology
○ 155 papers in training set
Top 4%
0.6%
29
Scientific Reports
○ 3612 papers in training set
Top 78%
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.