Back

Diploid donor-specific assembly enhances somatic structural variant detection in cancer genomes

Zhang, Y.; Qu, H.; Qin, Q.; Li, H.; Park, P. J.

2025-10-29 genomics
10.1101/2025.10.28.685155 bioRxiv
Show abstract

Somatic structural variants (SVs) play a crucial role in tumor development and evolution, yet their identification remains challenging, partly due to limitations in current reference genomes. We present a systematic evaluation of diploid donor-specific assemblies (DSAs)--generated based on hybrid long-read sequencing data--as the reference genome for detecting somatic SVs. We analyzed six tumor-normal cell line pairs, using the EchoSV tool we developed to consolidate haplotype-based SVs into a single DSA-based set and to compare SVs across reference genomes. Across Illumina, PacBio HiFi, and Oxford Nanopore Technology (ONT) data, DSA-based analysis improved read-mapping quality, identified over 20% additional SVs compared to GRCh38 and CHM13, and reduced germline artifacts. Most DSA-specific SVs were isolated deletions or insertions in repetitive elements, especially in satellite regions. By tracking sequence-context differences, we identified scenarios in which DSAs enabled detection of SVs missed on GRCh38/CHM13, and confirmed their functional impact with RNA-seq. These results highlight the value of integrating diploid DSAs into somatic SV analysis.

Matching journals

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

1
Science
● 477 papers in training set
Top 0.4%
14.8%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 19%
9.6%
Similar papers in this journal
3
Genome Biology
○ 637 papers in training set
Top 1%
9.6%
Similar papers in this journal
4
Nucleic Acids Research
◐ 1281 papers in training set
Top 2%
7.7%
Similar papers in this journal
5
Cell Genomics
○ 172 papers in training set
Top 0.5%
6.6%
Similar papers in this journal
6
Nature Biotechnology
○ 172 papers in training set
Top 0.7%
5.4%
50% of probability mass above
7
Nature Genetics
○ 286 papers in training set
Top 2%
4.2%
8
Nature Methods
○ 385 papers in training set
Top 3%
4.0%
9
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 13%
4.0%
10
Genome Medicine
○ 183 papers in training set
Top 1%
3.2%
11
Cell
○ 431 papers in training set
Top 3%
3.1%
12
Nature
○ 645 papers in training set
Top 5%
2.6%
13
Genome Research
● 468 papers in training set
Top 3%
2.4%
14
Cancer Discovery
● 66 papers in training set
Top 1%
2.1%
15
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
2.1%
16
Science Advances
● 1243 papers in training set
Top 18%
1.9%
17
eLife
● 5828 papers in training set
Top 56%
1.3%
18
Cell Reports
○ 1498 papers in training set
Top 25%
1.0%
19
Science Translational Medicine
● 127 papers in training set
Top 3%
1.0%
20
Cancer Research
● 130 papers in training set
Top 3%
0.9%
21
Molecular Cell
○ 350 papers in training set
Top 5%
0.8%
22
Nature Medicine
○ 125 papers in training set
Top 4%
0.8%
23
The American Journal of Human Genetics
○ 234 papers in training set
Top 3%
0.6%
24
Cell Reports Medicine
○ 153 papers in training set
Top 6%
0.6%
25
Bioinformatics
◐ 1204 papers in training set
Top 10%
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.