来自杂志 Cell genomics 的文献。
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1.
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。
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 …
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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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2.
颜林林
(2022-08-17 23:55):
#paper doi:10.1016/j.xgen.2022.100168 Cell Genomics, 2022, Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes. 高通量测序技术的发展、降价和普及，拉动了一大批人类群体基因组学的研究。本文又是这样一篇大规模人群的全外显子组数据及其分析结果的发布，该人群来自UK biobank，入组人数超过39万。文章开发并使用了一个混合模型分析框架SAIGE-GENE，会同时考虑点突变的水平、基因水平的突变负荷、以及两者的组合，由此分析与4529种疾病或表型（包括II型糖尿病、心脏代谢等）存在关联关系的各类罕见突变。在此基础上，本文还提供了一个在线浏览器Genebass，以展示这些表型相关的罕见突变。作为一个实例，文章在结果部分还特意强调了所发现的一个基因SCRIB，以及它与MRI脑成像特征之间的关系。类似的大规模人群基因组分析文章层出不穷，分析方法各有侧重或不同，若有可能，倒是值得研究下它们之间的方法差异，是否可能对所报道的结果产生影响。
Abstract:
Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at …
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Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results.
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