LBPO.BCS01 · 生物信息与计算 · Late-Breaking
SMURFS：一个用于共识体细胞变异检测的综合性Nextflow流程
SMURFS: A comprehensive Nextflow pipeline for consensus somatic variant detection
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
准确识别体细胞变异仍然是癌症基因组学中的一项根本性挑战，其中算法的差异性和技术性伪影会显著损害变异检出的准确性。单一caller方法往往导致较高的假阳性率和较差的可重复性，而实施多caller共识策略又需要复杂的生物信息学整合和标准化的质量控制框架。在此，我们提出SMURFS（Somatic MUtation Recognition Framework & Suite，体细胞突变识别框架与套件），这是一个综合性Nextflow流程，通过集成变异检出、严格的质量控制以及对拷贝数和结构变异检测的额外整合来应对上述挑战。SMURFS实施了四种互补的变异检出算法——Mutect2、SAGE、Strelka2和MuSE2——采用至少两个独立caller进行共识检出，在维持对真实体细胞变异高灵敏度的同时，降低潜在的假阳性突变。该流程纳入了关键的质量控制措施，包括通过Conpair进行污染检测和肿瘤-正常一致性验证、Mosdepth覆盖度分析以及DKFZBiasFilter系统性伪影识别。除点突变外，SMURFS还整合了用于等位基因特异性拷贝数分析的ASCAT、用于总拷贝数评估的CNVkit，以及用于结构变异检测的Manta/Delly，从而提供全面的体细胞改变特征刻画。该流程支持灵活的分析入口，可接受原始FASTQ文件、比对后的BAM、标记重复后的BAM或重校准后的BAM，并兼容四种参考基因组（GRCh38、GRCh37、mm39、RN7），覆盖超过80%的癌症基因组学研究场景。使用TCGA全外显子测序样本进行的性能测试显示，在AWS m4.16xlarge实例上，每对样本平均运行时间为277.8分钟，成本为17.33。SMURFS通过提供一个将最先进算法与全面质量控制相结合的标准化、易用框架，应对了体细胞变异检出中的可重复性危机。模块化架构可根据特定研究需求进行定制，同时保持分析的严谨性。这一统一框架代表了癌症基因组学方法学的重大进步，能够在多种癌症类型中更可靠地识别驱动突变、突变特征和治疗靶点。
查看英文原文 English abstract
Accurate somatic variant identification remains a fundamental challenge in cancer genomics, where algorithmic variability and technical artifacts can significantly compromise variant calling accuracy. Single-caller approaches often lead to high false positive rates and poor reproducibility, while implementing multi-caller consensus strategies requires complex bioinformatics integration and standardized quality control frameworks. Here, we present SMURFS (Somatic MUtation Recognition Framework & Suite), a comprehensive Nextflow pipeline that addresses these challenges through ensemble variant calling, rigorous quality control, and additional integration of copy number and structural variant detection. SMURFS implements four complementary variant calling algorithms-Mutect2, SAGE, Strelka2, and MuSE2-with consensus calling by at least two independent callers, reducing potential false positive mutations while maintaining high sensitivity for true somatic variants. The pipeline incorporates critical quality control measures including contamination detection and tumor-normal concordance verification by Conpair, Mosdepth coverage profiling, and DKFZBiasFilter systematic artifact identification. Beyond point mutations, SMURFS integrates ASCAT for allele-specific copy number profiling, CNVkit for total copy number assessment, and Manta/Delly for structural variant detection, providing comprehensive somatic alteration characterization. The pipeline supports flexible analysis entry points accommodating raw FASTQ files, aligned BAMs, duplicate-marked BAMs, or recalibrated BAMs, with compatibility across four reference genomes (GRCh38, GRCh37, mm39, RN7) covering over 80% of cancer genomics research scenarios. Performance test using TCGA whole-exome sequencing samples demonstrated an average runtime of 277.8 minutes at a cost of 17.33 per sample pair on AWS m4.16xlarge instances. SMURFS addresses the reproducibility crisis in somatic variant calling by providing a standardized, accessible framework that combines state-of-the-art algorithms with comprehensive quality control. The modular architecture enables customization for specific research requirements while maintaining analytical rigor. This unified framework represents a significant advancement in cancer genomics methodology, enabling more reliable identification of driver mutations, mutational signatures, and therapeutic targets across diverse cancer types.
利益披露 Disclosure
T. Yang, None..
R. Wu, None.