2-AIN-506, 2-AIN-252: Seminar in Bioinformatics (2), (4)
Summer 2025
Abstrakt

Megan K. Le, Qian Qin, Heng Li. colorSV: Long-range Somatic Structural Variation Calling from Matched Tumor-normal Co-assembly Graphs. Genomics, proteomics & bioinformatics, 2025.

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Abstract:

The accurate identification of somatic structural variants (SVs) is important for 
understanding the basis and evolution of cancerous tumor growth. Though long-read 
sequencing has facilitated the development of more accurate SV calling methods, 
existing somatic SV callers still struggle with achieving simultaneously high 
precision and high recall. In this work, we present colorSV (COassembly-based 
LOng-Range SV caller), a long-read-based method for calling long-range SVs by 
examining the local topology of joint assembly graphs from matched tumor-normal 
samples. colorSV is the first somatic SV calling method that uses a co-assembly 
approach, as well as the first SV caller that identifies variants by examining 
characteristics of the assembly graph itself. We demonstrate near-perfect 
precision and sensitivity for calling translocations on the COLO829 cell line, 
outperforming four existing somatic SV callers (Severus, Sniffles2, nanomonsv, 
and SAVANA) in both metrics. We also evaluated colorSV for calling translocations 
on the HCC1395 cell line, finding that our method achieved a good balance between 
sensitivity and precision (where the sensitivity was outperformed by Severus and 
SAVANA, and the precision was only outperformed by nanomonsv). Our work 
establishes a novel joint assembly-based strategy for characterizing long-range 
somatic variation, which could be further expanded or modified for the 
identification of SVs of different types and sizes. colorSV is available at 
https://github.com/mktle/colorSV.