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2019-06-05 求臻医学企宣

作为科研型企业,求臻医学时刻关注行业发展动态,掌握科研动向和最新科研成果。今日,樊大师将与各位读者分享一波与生物信息学领域相关的的最新科研进展,全部是具备高参考价值的最新文献,干货满满!


Bohannan Z S, Mitrofanova A. Calling Variants in the Clinic: Informed Variant Calling Decisions Based on Biological, Clinical, and Laboratory Variables[J]. Computational and structural biotechnology journal, 2019.


该文是一篇综述性的文章,比较系统的介绍了一些目前应用上的生信解决办法,阅读该篇文章侧重点主要是关注文章所引用的相关资料,有一些是2019年最新发表的文章,很有份量。





Kim S, Scheffler K, Halpern A L, et al. Strelka2: fast and accurate calling of germline and somatic variants[J]. Nature methods, 2018, 15(8): 591.




Dunn T, Berry G, Emig-Agius D, et al. Pisces: An accurate and versatile variant caller for somatic and germline next-generation sequencing data[J]. BioRxiv, 2018: 291641.


如果你一直以为Illumina是一家生产测序仪器的公司那就错了,Illumina发表了最新的call 变异分析的文章,Strelka2已经是该软件的第二个版本 ,该软件可以进行单样本的germline分析,还可以分析成对样本的somatic突变。如果你想进行单样本的somatic分析,你可以一并参考Illumina为此开发的Pisces。




Mandelker D, Donoghue M T A, Talukdar S, et al. Germline-Focused Analysis of Tumour-Only Sequencing: Recommendations from the ESMO Precision Medicine Working Group[J]. Annals of Oncology, 2019.


如今很多公司都推出了自己的gene panel分析,而目前人们往往专注于somatic的分析,这篇高分文章教你如何简单地区分germline与somatic突变,并进行后续分析。




Sallevelt S C E H, De Koning B, Szklarczyk R, et al. A comprehensive strategy for exome-based preconception carrier screening[J]. Genetics in Medicine, 2017, 19(5): 583.


老文章也能结出新硕果,本文对外显子分析的建议对于gene panel分析也同样适用。




Sukhai M A, Misyura M, Thomas M, et al. Somatic Tumor Variant Filtration Strategies to Optimize Tumor-Only Molecular Profiling Using Targeted Next-Generation Sequencing Panels[J]. The Journal of Molecular Diagnostics, 2019, 21(2): 261-273.


变异分析后往往会得到很多位点,如何过滤掉位点多态性,最大限度保留有意义的位点,以便于下一步的遗传咨询师解读,或许本文可以为你解惑。




Sun J X, He Y, Sanford E, et al. A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal[J]. PLoS computational biology, 2018, 14(2): e1005965.




Wood Derrick E,White James R,Georgiadis Andrew et al. A machine learning approach for somatic mutation discovery.[J] .Sci Transl Med, 2018, 10: undefined.


你是不是在单样本区分germline和somatic的路上一去不复返?以上两篇利用机器学习的文章可以助你一臂之力。




Newman A M , Bratman S V , To J , et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage[J]. Nature medicine, 2014, 20(5):548.




Przybyl J , Chabon J J , Spans L , et al. Combination approach for detecting different types of alterations in circulating tumor DNA in leiomyosarcoma[J]. Clinical Cancer Research, 2018:clincanres.3704.2017.




Mayrhofer M, De Laere B, Whitington T, et al. Cell-free DNA profiling of metastatic prostate cancer reveals microsatellite instability, structural rearrangements and clonal hematopoiesis[J]. Genome medicine, 2018, 10(1): 85.


看文章的小伙伴是不是刚刚做完ctDNA的质评,关于实验和数据可以参考本文,或许会有对你有些帮助。另外在低频突变的变异检测软件中,以小编的软件Varscan与Vardict两款分析软件都表现不俗。以后在分析过程可以有效结合利用该分析软件。




Zook J M, McDaniel J, Olson N D, et al. An open resource for accurately benchmarking small variant and reference calls[J]. Nature biotechnology, 2019, 37(5): 561.




Krusche P, Trigg L, Boutros P C, et al. Best practices for benchmarking germline small-variant calls in human genomes[J]. Nature biotechnology, 2019: 1.


近期两篇Nature biotechnology关于变检测标准的文章,试图针对不同的数据形式订立不同的分析标准,可以作为我们以后生信流程搭建的参考。




Cristiano S, Leal A, Phallen J, et al. Genome-wide cell-free DNA fragmentation in patients with cancer[J]. Nature, 2019: 1.


另一篇Nature biotechnology重要的文章,借助机器学习通过分析cfDNA数据预测癌症早筛的文章,热点爆棚,感兴趣的小伙伴可以一览。