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National Scale Genomic Engine for Precision Medicine: Singapore PRECISE-SG100K Experience

Bellis, C.; Kolle, G.; Yong, J.; Hebrard, M.; Bertin, N.; Lin, C.; Koh, J.; Cheng, P.; Sunmugam, S.; Heng, K. K.; Xie, Z.; Meah, W. Y.; Chen, X. Y.; Lim, E.; Alberts, R.; Lim, S.; Leong, S. H.; Ow, J. L.; Toro Jimenez, R.; Wong, E.; Tong, T.; Kerk, S. K.; Yao, J.; Chee, M. L.; PRECISE Genomic Engine Consortium, ; Yeo, K. K.; Cheng, C.-Y.; Sim, X.; Zheng, W.; Khor, C. C.; Tai, E. S.; Chambers, J. C.; Tan, P.

2025-03-15 genomics
10.1101/2025.03.13.642552 bioRxiv
Show abstract

Precision medicine (PM) research in recent years has witnessed a remarkable surge in large-scale population genomics programs. In 2017, Singapore initiated the National Precision Medicine (NPM) program, a three-phase national strategy driving PM research, innovation, and enterprise capitalizing on Singapores unique multi-ancestral Asian population. Coordinated by Precision Health Research, Singapore (PRECISE), NPM Phase I assembled an Asian reference genome of 10,000 individuals completed using highly manual and laborious laboratory sample workflows. NPM Phase II, targeting a 100,000 population cohort (PRECISE-SG100K), required the transformation of the genome sequencing workflow to a production-scale operation leveraging high-throughput automation and robotics. Here we share detailed technical blueprints for implementing and maintaining Singapores PRECISE-SG100K Genomic Engine, learning from and overcoming Phase Is pain-points and bottlenecks. The Genomic Engine allowed the successful scale up of genomic sequencing and throughput, enabling the anticipated completion of sequencing of 100,000 whole genomes in three years.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
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Top 0.1%
15.0%
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2
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11.8%
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Top 0.7%
5.1%
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4
PLOS ONE
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5
Nature Communications
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3.2%
7
Genome Biology
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Top 4%
2.8%
8
Nucleic Acids Research
◐ 1281 papers in training set
Top 6%
2.8%
9
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 2%
2.6%
50% of probability mass above
10
BMC Medical Genomics
○ 50 papers in training set
Top 0.2%
2.6%
11
Molecular Ecology Resources
○ 171 papers in training set
Top 0.9%
2.4%
12
G3: Genes, Genomes, Genetics
◐ 252 papers in training set
Top 2%
2.4%
13
Genome Research
● 468 papers in training set
Top 3%
2.1%
14
Gigabyte
● 62 papers in training set
Top 0.5%
2.1%
15
The Journal of Molecular Diagnostics
○ 39 papers in training set
Top 0.3%
1.7%
16
Scientific Reports
○ 3612 papers in training set
Top 52%
1.7%
17
Scientific Data
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Top 1%
1.7%
18
Nature Biotechnology
○ 172 papers in training set
Top 3%
1.7%
19
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○ 64 papers in training set
Top 0.8%
1.7%
20
Frontiers in Bioinformatics
○ 49 papers in training set
Top 0.6%
1.5%
21
Genome Medicine
○ 183 papers in training set
Top 3%
1.5%
22
Clinical Chemistry
◐ 22 papers in training set
Top 0.2%
1.1%
23
Frontiers in Genetics
○ 230 papers in training set
Top 4%
1.1%
24
BMC Bioinformatics
○ 457 papers in training set
Top 5%
1.1%
25
Journal of Clinical Microbiology
● 130 papers in training set
Top 1%
1.1%
26
International Journal of Molecular Sciences
○ 494 papers in training set
Top 13%
1.0%
27
Cell Genomics
○ 172 papers in training set
Top 4%
0.8%
28
Viruses
○ 332 papers in training set
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0.8%
29
Communications Biology
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30
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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.