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A curated dataset and web application for Integrative Analysis of Ancient DNA from Britain and Ireland

Metz, S.; Fischer, C.-E.; Bleasdale, M.; Castells Navarro, L.; Buster, L.; Legge, M.; Reich, D.; Armit, I.

2025-06-02 genetics
10.1101/2025.05.30.657009 bioRxiv
Show abstract

In recent years, genome-wide data from thousands of ancient human remains have been sequenced, offering unprecedented insights into past populations. This vast dataset of ancient human genomes and their associated metadata holds immense value for archaeologists, anthropologists, and biologists. However, extracting insights often requires advanced bioinformatics knowledge, creating barriers for non-experts. To address this challenge, we present a curated dataset and user-friendly web application for published genome-wide data from ancient Britain and Ireland, including new data produced by the COMMIOS Project. The platform integrates visualization tools, enabling researchers to explore genetic connections between ancient and modern populations without requiring bioinformatics expertise.

Matching journals

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

1
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50% of probability mass above
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8
Nature
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3.5%
9
iScience
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Top 11%
2.4%
10
Communications Biology
○ 993 papers in training set
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11
eLife
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12
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1.8%
13
Human Genetics and Genomics Advances
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1.4%
14
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1.1%
15
Frontiers in Genetics
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1.1%
16
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1.1%
17
The American Journal of Human Genetics
○ 234 papers in training set
Top 2%
1.1%
18
Molecular Biology and Evolution
◐ 542 papers in training set
Top 4%
1.1%
19
Proceedings of the National Academy of Sciences
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Top 33%
1.1%
20
Current Biology
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Top 9%
1.1%
21
PLOS ONE
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1.1%
22
Science Advances
● 1243 papers in training set
Top 30%
0.9%
23
BMC Genomics
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Top 7%
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24
American Journal of Biological Anthropology
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Top 0.1%
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Cell Genomics
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Top 5%
0.6%
27
Nature Aging
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28
PeerJ
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Top 12%
0.6%
29
Nature Ecology & Evolution
131 papers in training set
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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.