Highly sensitive and specific detection of rare variants in mixed viral populations from massively parallel sequence data.
Highly sensitive and specific detection of rare variants in mixed viral populations from massively parallel sequence data.
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DOI：
10.1371/journal.pcbi.1002417
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发表时间：
2012
影响因子：
4.3
通讯作者：
Henn MR
中科院分区：
文献类型：
--
作者：
Macalalad AR;Zody MC;Charlebois P;Lennon NJ;Newman RM;Malboeuf CM;Ryan EM;Boutwell CL;Power KA;Brackney DE;Pesko KN;Levin JZ;Ebel GD;Allen TM;Birren BW;Henn MR
随着时间的推移，病毒在宿主体内发生多样化，通常会削弱宿主防御和治疗干预的有效性。为了设计成功的疫苗和治疗方法，更好地了解病毒多样化至关重要，包括全面表征病毒体内宿主群体的遗传变异，并对从传播到感染过程的变化进行建模。大规模并行测序技术可以克服旧测序方法的成本限制，并获得检测感染宿主内罕见遗传变异（<1%）所需的高序列覆盖率，并在没有先验知识的情况下分析变异。解释深度序列数据集的关键是能够以高灵敏度和特异性区分生物变异和过程错误。为了应对这一挑战，我们描述了 V-Phaser，一种能够识别混合群体中罕见生物变异的算法。 V-Phaser 使用观察到的变异之间的协变（即定相）来提高灵敏度，并使用期望最大化算法迭代地重新校准碱基质量分数以提高特异性。总体而言，V-Phaser 在对照读数集上实现了 >97% 的灵敏度和 >97% 的特异性。根据从感染 HIV-1 四年后的患者获得的数据，V-Phaser 在 ∼10 kb 基因组中检测到了 2,015 个变异，其中包括仅使用相位信息检测到的 603 个罕见变异（频率<1%）。 V-Phaser 识别出频率低至 0.2% 的变异，与等位基因特异性 PCR 的检测阈值相当，该方法需要事先了解变异。 V-Phaser 的高灵敏度和特异性能够识别和跟踪混合群体（例如 RNA 病毒）中低频变异的变化。新的测序技术为研究病原体群体提供了前所未有的分辨率，例如单链 RNA 病毒 HIV、登革热 (DENV) 和西尼罗河 (WNV)，以及它们如何在感染个体中进化以应对免疫、治疗和疫苗压力。虽然这些新技术提供了大量数据，但这些数据包含过程错误。为了检测生物变异，尤其是群体中低频率发生的生物变异，这些技术需要一种以高灵敏度和特异性区分生物变异和过程错误的方法。为了应对这一挑战，我们引入了 V-Phaser 算法，该算法将生物变异的协变与过程错误的协变区分开来。我们通过测量该方法在已知变异的实际读取集上正确识别变异和错误的频率来验证该方法。此外，利用来自 HIV-1 感染四年的患者的数据，我们表明 V-Phaser 可以以与需要先验知识的方法相当的频率检测生物变异。 V-Phaser 可从以下网址下载：http://www.broadinstitute.org/scientific-community/software。
Viruses diversify over time within hosts, often undercutting the effectiveness of host defenses and therapeutic interventions. To design successful vaccines and therapeutics, it is critical to better understand viral diversification, including comprehensively characterizing the genetic variants in viral intra-host populations and modeling changes from transmission through the course of infection. Massively parallel sequencing technologies can overcome the cost constraints of older sequencing methods and obtain the high sequence coverage needed to detect rare genetic variants (<1%) within an infected host, and to assay variants without prior knowledge. Critical to interpreting deep sequence data sets is the ability to distinguish biological variants from process errors with high sensitivity and specificity. To address this challenge, we describe V-Phaser, an algorithm able to recognize rare biological variants in mixed populations. V-Phaser uses covariation (i.e. phasing) between observed variants to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates base quality scores to increase specificity. Overall, V-Phaser achieved >97% sensitivity and >97% specificity on control read sets. On data derived from a patient after four years of HIV-1 infection, V-Phaser detected 2,015 variants across the ∼10 kb genome, including 603 rare variants (<1% frequency) detected only using phase information. V-Phaser identified variants at frequencies down to 0.2%, comparable to the detection threshold of allele-specific PCR, a method that requires prior knowledge of the variants. The high sensitivity and specificity of V-Phaser enables identifying and tracking changes in low frequency variants in mixed populations such as RNA viruses. New sequencing technologies provide unprecedented resolution to study pathogen populations, such as the single stranded RNA viruses HIV, dengue (DENV), and West Nile (WNV), and how they evolve within infected individuals in response to immune, therapeutic, and vaccine pressures. While these new technologies provide high volumes of data, these data contain process errors. To detect biological variants, especially those occurring at low frequencies in the population, these technologies require a method to differentiate biological variants from process errors with high sensitivity and specificity. To address this challenge, we introduce the V-Phaser algorithm, which distinguished the covariation of biological variants from that of process errors. We validate the method by measuring how frequently it correctly identifies variants and errors on actual read sets with known variation. Further, using data derived from a patient following four years of HIV-1 infection, we show that V-Phaser can detect biological variants at frequencies comparable to approaches that require prior knowledge. V-Phaser is available for download at: http://www.broadinstitute.org/scientific-community/software.
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DOI：
10.1093/bioinformatics/btn565
发表时间：
2009-01-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Malhis N;Butterfield YS;Ester M;Jones SJ
通讯作者：
Jones SJ
影响因子：
1.7
作者：
Zagordi, Osvaldo;Geyrhofer, Lukas;Beerenwinkel, Niko
通讯作者：
Beerenwinkel, Niko
影响因子：
9.4
作者：
Palmer, S;Kearney, M;Coffin, JM
通讯作者：
Coffin, JM
使用下一代 DNA 测序数据进行变异发现和基因分型的框架。
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
14.9
作者：
Hoffmann C;Minkah N;Leipzig J;Wang G;Arens MQ;Tebas P;Bushman FD
通讯作者：
Bushman FD