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MuDoGeR: Multi-Domain Genome Recovery from metagenomes made easy

Nunes da Rocha, U.; Kasmanas, J.; Kallies, R.; Saraiva, J. P.; Brizola Toscan, R.; Stefanic, P.; Bicalho, M. F.; Correa, F. B.; Basturk, M. N.; Fousekis, E.; Barbosa, L. M. V.; Plewka, J.; Probst, A. J.; Baldrian, P.; Stadler, P. F.; CLUE-TERRA consortium,

2022-08-11 bioinformatics Community evaluation
10.1101/2022.06.21.496983 bioRxiv
Show abstract

Several computational frameworks and workflows that recover genomes from prokaryotes, eukaryotes, and viruses from metagenomes exist. However, it is difficult for scientists with little bioinformatics experience to evaluate quality, annotate genes, dereplicate, assign taxonomy and calculate relative abundance and coverage of genomes belonging to different domains. MuDoGeR is a user-friendly tool accessible for non-bioinformaticians that make it easy to recover genomes of prokaryotes, eukaryotes, and viruses from metagenomes, either alone or in combination. We tested MuDoGer using 24 individual-isolated genomes and 574 metagenomes, demonstrating the applicability for a few samples and high throughput. MuDoGeR is open-source software available at https://github.com/mdsufz/MuDoGeR.

Matching journals

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

1
GigaScience
◐ 212 papers in training set
Top 0.1%
12.4%
Similar papers in this journal
2
Microbiome
○ 154 papers in training set
Top 0.2%
11.7%
Similar papers in this journal
3
Genome Biology
○ 637 papers in training set
Top 1%
9.5%
Similar papers in this journal
4
BMC Bioinformatics
○ 457 papers in training set
Top 1%
7.7%
Similar papers in this journal
5
Bioinformatics
◐ 1204 papers in training set
Top 4%
6.6%
Similar papers in this journal
6
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 0.7%
5.4%
50% of probability mass above
7
Nucleic Acids Research
◐ 1281 papers in training set
Top 4%
4.7%
8
Briefings in Bioinformatics
◐ 354 papers in training set
Top 2%
4.2%
9
iMeta
○ 10 papers in training set
Top 0.1%
4.0%
10
Nature Communications
○ 5641 papers in training set
Top 36%
3.2%
11
PLOS Computational Biology
● 1863 papers in training set
Top 10%
3.2%
12
mSystems
● 394 papers in training set
Top 3%
2.6%
13
BMC Genomics
○ 406 papers in training set
Top 4%
2.1%
14
Bioinformatics Advances
◐ 203 papers in training set
Top 3%
1.7%
15
ISME Communications
◐ 120 papers in training set
Top 1%
1.5%
16
PLOS ONE
● 5266 papers in training set
Top 54%
1.1%
17
Microbial Genomics
● 225 papers in training set
Top 2%
1.1%
18
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 5%
1.1%
19
Molecular Ecology Resources
○ 171 papers in training set
Top 2%
1.1%
20
Nature Methods
○ 385 papers in training set
Top 6%
1.0%
21
Molecular Biology and Evolution
◐ 542 papers in training set
Top 5%
1.0%
22
BMC Biology
○ 265 papers in training set
Top 5%
0.9%
23
Microorganisms
○ 106 papers in training set
Top 4%
0.8%
24
iScience
○ 1154 papers in training set
Top 37%
0.8%
25
Scientific Reports
○ 3612 papers in training set
Top 74%
0.8%
26
Genome Research
● 468 papers in training set
Top 6%
0.8%
27
International Journal of Molecular Sciences
○ 494 papers in training set
Top 18%
0.6%
28
Viruses
○ 332 papers in training set
Top 5%
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.