PO.BCS01.15 · 生物信息与计算
CASTLE: long-read sequencing panel of cancer cell lines to improve standards of somatic variant calling and benchmarking
作者与单位
摘要 Abstract
Most current large-scale whole-genome sequencing projects rely on short-read sequencing to call germline and somatic SVs, however it provides an incomplete view of the somatic variation landscape because of mappability limitations. In contrast, long-read sequencing can resolve highly repetitive regions of the human genome, and assemble variants into contiguous haplotypes, and therefore is a promising approach to resolve the hidden complexity of a cancer genome. However there is still a limited number of publicly available datasets for benchmarking and development of new methods.
To motivate the development of new short- and long-read tools for cancer genomics, we created the CASTLE panel, based on multi-technology whole-genome sequencing of six commercially available tumor/normal cell line pairs (HCC1954, HCC1937, H1437, H2009, Hs578T and HCC1395). The panel represents two lung and three breast cancer cell lines. Genomic sequencing currently includes PacBio, Oxford Nanopore, Illumina, Hi-C and PoreC, in most cases sequenced from the same DNA extraction or cell line passage.
We further generated high-confidence benchmarking somatic variant calls for SNPs, small indels and structural variants using the ensemble method. For structural variants, We used Severus, nanomonsv, SAVANA, Sniffles2, SvABA, GRIDSS, and Manta to generate initial variant calls; confident calls were defined if supported by at least two (out of three) technologies and at least 4 (out of 11) callers. For small variant benchmarking sets, we used a combination of Strelka2, DeepSomatic and ClairS.
Overall, we release a new public resource for cancer genomic developments and benchmarking, which we are aiming to complement with additional genomic and transcriptomic technologies. The data and benchmarking datasets are openly available at: https://github.com/CASTLE-Panel/castle.
利益披露 Disclosure
M. Kolmogorov, None.