小擎子 (2022-12-31 22:08):
#paper doi: 10.1016/j.xgen.2022.100179. Cell Genom, 2022, Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor。COSMIC网站推荐的检测肿瘤特征的工具,文献主推SigProfilerExtractor,也测试了另外13种检测肿瘤特征的工具。文献做了广泛的数据模拟,添加不同的噪声干扰,比较不同工具的算法表现,做了肿瘤特征提取较为可靠的benchmarking。
IF:11.100Q1 Cell genomics, 2022-Nov-09. DOI: 10.1016/j.xgen.2022.100179 PMID: 36388765
Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor
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Abstract:
Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.
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