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Orchestrating Microbiome Analysis with Bioconductor

Borman, T.; Benedetti, G.; Muluh, G.; Raulo, A.; Valderamma, B.; Sannikov, A.; Peschel, S.; Liu, Y.; Hindström, R.; Pärnänen, K.; Müller, C. L.; Havulinna, A. S.; Shetty, S.; Ramos, M.; Braccia, D. J.; Bravo, H. C.; Ernst, F. M.; Waldron, L.; Bastiaanssen, T. F.; Mallick, H.; Lahti, L.

2025-10-30 bioinformatics
10.1101/2025.10.29.685036 bioRxiv
Show abstract

The expansion of microbiome research has led to the accumulation of interlinked datasets encompassing versatile taxonomic and functional assays. The analysis of increasingly large and heterogeneous multi-modal microbiome data would benefit from unified approaches supporting the design of modular data science workflows through interoperable methods. The Bioconductor project has recently developed an optimized statistical programming framework for multi-assay data integration. Building on this foundation, we introduce a community-developed open source ecosystem for microbiome data science. In contrast to the previous alternatives, the methodology is specifically designed to support joint analysis of hierarchical, interlinked, and heterogeneous multi-table datasets that are increasingly common in modern microbiome research. This data science ecosystem encompasses open data, methods, tutorials, and an active online community. These resources support standardized and reproducible data wrangling, joint analysis, and reporting. We have detailed the functionality and usage in the online book https://microbiome.github.io/OMA, which offers guidance for prospective users and contributors.

Matching journals

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

1
Nature Communications
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Top 15%
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4.2%
7
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8
Scientific Reports
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Top 27%
3.9%
50% of probability mass above
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GigaScience
◐ 212 papers in training set
Top 0.9%
3.9%
10
Briefings in Bioinformatics
◐ 354 papers in training set
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3.4%
11
Nature Biotechnology
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Top 1%
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12
PLOS Computational Biology
● 1863 papers in training set
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13
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Top 0.1%
2.7%
14
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Top 0.2%
2.5%
15
Bioinformatics
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Top 6%
2.3%
16
Communications Biology
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2.0%
17
NAR Genomics and Bioinformatics
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Top 2%
1.8%
18
F1000Research
○ 88 papers in training set
Top 1%
1.8%
19
BMC Genomics
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Top 5%
1.3%
20
Journal of Proteome Research
● 234 papers in training set
Top 1%
1.3%
21
Bioinformatics Advances
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Top 4%
1.0%
22
Nature Computational Science
○ 55 papers in training set
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1.0%
23
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 6%
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24
mSphere
● 302 papers in training set
Top 7%
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BMC Bioinformatics
○ 457 papers in training set
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26
Frontiers in Bioinformatics
○ 49 papers in training set
Top 2%
0.8%
27
Frontiers in Microbiology
○ 427 papers in training set
Top 9%
0.6%
28
Genome Research
● 468 papers in training set
Top 7%
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"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.