Back

An optimised computational approach for the identification of somatic structural variants in cancer

Waise, S.; Mensah, N.; Lesluyes, T.; Demeulemeester, J.; Flanagan, A. M.; Pillay, N.; Van Loo, P.

2025-07-02 genomics
10.1101/2025.07.01.662575 bioRxiv
Show abstract

Structural variants play a critical role in tumorigenesis. At present, these events are most commonly identified using short-read whole-genome sequencing data, and a number of computational tools are available for this purpose. Consensus approaches have been used to improve precision, but may reduce sensitivity. The optimal number and combination of callers remains unclear, in part due to the lack of gold standard real-world datasets for validation. Here, we benchmark the performance of Delly, GRIDSS, LUMPY, Manta and SvABA, using a validation set of consensus calls from the Pan-Cancer Analysis of Whole Genomes Consortium. Manta showed the best standalone performance, identifying 88% of the validation set calls, and was included in all of the best-performing caller combinations. A consensus approach comprising Delly, GRIDSS, Manta and SvABA was selected as the optimum approach from those tested. We provide a NextFlow implementation of our optimised consensus approach as a resource for the cancer genomics community.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Genome Medicine
○ 183 papers in training set
Top 0.1%
10.9%
Similar papers in this journal
2
Genome Biology
○ 637 papers in training set
Top 1%
9.6%
Similar papers in this journal
3
Genetics in Medicine
○ 78 papers in training set
Top 0.2%
7.8%
Similar papers in this journal
4
Nature Communications
○ 5641 papers in training set
Top 21%
7.8%
Similar papers in this journal
5
The Journal of Molecular Diagnostics
○ 39 papers in training set
Top 0.1%
6.2%
Similar papers in this journal
6
Scientific Reports
○ 3612 papers in training set
Top 16%
5.4%
7
BMC Medical Genomics
○ 50 papers in training set
Top 0.1%
4.3%
50% of probability mass above
8
BMC Genomics
○ 406 papers in training set
Top 2%
3.2%
9
The American Journal of Human Genetics
○ 234 papers in training set
Top 1%
3.1%
10
Nature Medicine
○ 125 papers in training set
Top 1%
2.4%
11
Genomics
○ 64 papers in training set
Top 0.5%
2.4%
12
Genetics in Medicine Open
○ 11 papers in training set
Top 0.1%
2.1%
13
Bioinformatics
◐ 1204 papers in training set
Top 6%
2.1%
14
Cell Genomics
○ 172 papers in training set
Top 2%
2.1%
15
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.1%
16
Nature Genetics
○ 286 papers in training set
Top 3%
1.7%
17
Genome Research
● 468 papers in training set
Top 4%
1.7%
18
Human Genetics and Genomics Advances
○ 84 papers in training set
Top 1%
1.5%
19
Communications Biology
○ 993 papers in training set
Top 17%
1.5%
20
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.3%
21
Bioinformatics Advances
◐ 203 papers in training set
Top 4%
1.1%
22
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 3%
1.1%
23
npj Genomic Medicine
○ 36 papers in training set
Top 0.6%
1.1%
24
BMC Bioinformatics
○ 457 papers in training set
Top 5%
0.9%
25
European Journal of Human Genetics
○ 58 papers in training set
Top 1%
0.8%
26
iScience
○ 1154 papers in training set
Top 36%
0.8%
27
PLOS Computational Biology
● 1863 papers in training set
Top 21%
0.6%
28
GigaScience
◐ 212 papers in training set
Top 5%
0.6%
29
Nucleic Acids Research
◐ 1281 papers in training set
Top 15%
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.