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OCTOPUS: Disk-based, Multiplatform, Mobile-friendly Metagenomics Classifier

Marini, S.; Barquero, A.; Wadhwani, A. A.; Bian, J.; Ruiz, J.; Boucher, C.; Prosperi, M.

2024-03-15 molecular biology
10.1101/2024.03.15.585215 bioRxiv
Show abstract

Portable genomic sequencers such as Oxford Nanopores MinION enable real-time applications in clinical and environmental health. However, there is a bottleneck in the downstream analytics when bioinformatics pipelines are unavailable, e.g., when cloud processing is unreachable due to absence of Internet connection, or only low-end computing devices can be carried on site. Here we present a platform-friendly software for portable metagenomic analysis of Nanopore data, the Oligomer-based Classifier of Taxonomic Operational and Pan-genome Units via Singletons (OCTOPUS). OCTOPUS is written in Java, reimplements several features of the popular Kraken2 and KrakenUniq software, with original components for improving metagenomics classification on incomplete/sampled reference databases, making it ideal for running on smartphones or tablets. OCTOPUS obtains sensitivity and precision comparable to Kraken2, while dramatically decreasing (4-to 16-fold) the false positive rate, and yielding high correlation on real-word data. OCTOPUS is available along with customized databases at https://github.com/DataIntellSystLab/OCTOPUS and https://github.com/Ruiz-HCI-Lab/OctopusMobile.

Matching journals

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

1
Genome Biology
○ 637 papers in training set
Top 0.8%
12.3%
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2
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Top 15%
12.1%
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3
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Top 3%
11.5%
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4
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◐ 212 papers in training set
Top 0.4%
6.1%
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5
Nucleic Acids Research
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Top 4%
5.3%
6
Nature Methods
○ 385 papers in training set
Top 2%
5.3%
Similar papers in this journal
50% of probability mass above
7
BMC Bioinformatics
○ 457 papers in training set
Top 2%
4.7%
8
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◐ 1204 papers in training set
Top 5%
4.2%
9
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 1%
3.9%
10
Nature Biotechnology
○ 172 papers in training set
Top 1%
3.1%
11
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○ 88 papers in training set
Top 0.6%
3.1%
12
Communications Biology
○ 993 papers in training set
Top 7%
2.7%
13
Molecular Ecology Resources
○ 171 papers in training set
Top 0.9%
2.3%
14
Genome Research
● 468 papers in training set
Top 4%
1.6%
15
ACS Synthetic Biology
● 287 papers in training set
Top 2%
1.5%
16
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.3%
17
PLOS Computational Biology
● 1863 papers in training set
Top 17%
1.1%
18
Methods in Ecology and Evolution
○ 176 papers in training set
Top 1%
1.1%
19
iScience
○ 1154 papers in training set
Top 29%
1.1%
20
PLOS ONE
● 5266 papers in training set
Top 54%
1.1%
21
BMC Genomics
○ 406 papers in training set
Top 7%
1.0%
22
Nature Computational Science
○ 55 papers in training set
Top 2%
0.8%
23
Journal of Proteome Research
● 234 papers in training set
Top 2%
0.8%
24
PeerJ
◐ 308 papers in training set
Top 11%
0.8%
25
Molecular Biology and Evolution
◐ 542 papers in training set
Top 5%
0.8%
26
Biology Methods and Protocols
◐ 61 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.