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OmicsLake: Versioned, Agent-Aware Data Lineage for R/Bioconductor Workflows

Matsui, Y.

2026-07-21 bioinformatics
10.64898/2026.07.17.739088 bioRxiv
Show abstract

OmicsLake is an R package for version-aware, dataset-level lineage in R/Bioconductor exploratory workflows. It associates stored versions with dependencies captured within tracked ref()-dplyr-save_as() pipelines or supplied explicitly, and can attach caller-supplied human or software-agent identifiers. Controlled tests recovered all ground-truth edges within the supported path and enabled explicit recovery of an overwritten tagged intermediate. OmicsLake 0.99.3 is MIT-licensed software available at https://github.com/matsui-lab/OmicsLake (tag v0.99.3); source, tests, and evaluation files are archived at https://doi.org/10.5281/zenodo.21391428. Supplementary Material accompanies this preprint.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 1%
22.0%
Similar papers in this journal
2
Nature Methods
○ 385 papers in training set
Top 0.6%
15.1%
Similar papers in this journal
3
Nature Communications
○ 5641 papers in training set
Top 25%
6.3%
Similar papers in this journal
4
Cell Systems
○ 201 papers in training set
Top 0.7%
5.5%
Similar papers in this journal
5
GigaScience
◐ 212 papers in training set
Top 0.5%
5.5%
Similar papers in this journal
50% of probability mass above
6
Nature Biotechnology
○ 172 papers in training set
Top 0.9%
4.5%
7
Genome Biology
○ 637 papers in training set
Top 3%
4.0%
8
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
3.2%
9
Nucleic Acids Research
◐ 1281 papers in training set
Top 5%
3.2%
10
Scientific Data
○ 209 papers in training set
Top 1.0%
2.4%
11
eLife
● 5828 papers in training set
Top 43%
2.4%
12
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
1.7%
13
PLOS Computational Biology
● 1863 papers in training set
Top 14%
1.7%
14
Molecular Systems Biology
○ 162 papers in training set
Top 2%
1.5%
15
Briefings in Bioinformatics
◐ 354 papers in training set
Top 6%
1.1%
16
Journal of Open Source Software
● 25 papers in training set
Top 0.3%
1.0%
17
Frontiers in Genetics
○ 230 papers in training set
Top 5%
0.8%
18
Genome Research
● 468 papers in training set
Top 6%
0.8%
19
Cell Genomics
○ 172 papers in training set
Top 4%
0.8%
20
Patterns
○ 78 papers in training set
Top 3%
0.8%
21
Cell Reports Methods
○ 165 papers in training set
Top 4%
0.8%
22
Journal of Molecular Biology
○ 232 papers in training set
Top 4%
0.8%
23
Peer Community Journal
● 281 papers in training set
Top 6%
0.6%
24
BMC Bioinformatics
○ 457 papers in training set
Top 6%
0.6%
25
Molecular Biology and Evolution
◐ 542 papers in training set
Top 5%
0.6%
26
F1000Research
○ 88 papers in training set
Top 4%
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.