通过纠正条形码处理偏差提高批量体质测定的准确性。

IF 11 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Ryan Seamus McGee, Grant Kinsler, Dmitri Petrov, Mikhail Tikhonov
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引用次数: 0

摘要

测量遗传变异的适应性是进化生物学的一个基本目标。批量测量微生物适存性的标准方法包括用独特的序列条形码标记基因变异体库,在批量培养中对标记菌株进行竞争,并使用深度测序跟踪条形码丰度随时间的变化。然而,条形码的特异性会导致扩增不均匀或测序覆盖率不均,从而导致某些条形码在样本中的代表性过高或过低。这种系统性偏差会导致错误的读数计数轨迹和错误的适配性估计。在此,我们开发了一种名为 REBAR 的计算方法,利用数据中系统偏差的结构来推断条形码处理偏差的影响。我们将这种方法应用于两个独立的数据集,并证明这种方法比基于 GC 的校正等标准替代方法更准确地估计和校正偏差。REBAR 可减轻高通量检测中的偏差并提高适配性估计,而不会给实验方案带来额外的复杂性,有望在一系列实验进化和突变筛选中得到应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving the Accuracy of Bulk Fitness Assays by Correcting Barcode Processing Biases.

Measuring the fitnesses of genetic variants is a fundamental objective in evolutionary biology. A standard approach for measuring microbial fitnesses in bulk involves labeling a library of genetic variants with unique sequence barcodes, competing the labeled strains in batch culture, and using deep sequencing to track changes in the barcode abundances over time. However, idiosyncratic properties of barcodes can induce nonuniform amplification or uneven sequencing coverage that causes some barcodes to be over- or under-represented in samples. This systematic bias can result in erroneous read count trajectories and misestimates of fitness. Here, we develop a computational method, named REBAR (Removing the Effects of Bias through Analysis of Residuals), for inferring the effects of barcode processing bias by leveraging the structure of systematic deviations in the data. We illustrate this approach by applying it to two independent data sets, and demonstrate that this method estimates and corrects for bias more accurately than standard proxies, such as GC-based corrections. REBAR mitigates bias and improves fitness estimates in high-throughput assays without introducing additional complexity to the experimental protocols, with potential applications in a range of experimental evolution and mutation screening contexts.

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来源期刊
Molecular biology and evolution
Molecular biology and evolution 生物-进化生物学
CiteScore
19.70
自引率
3.70%
发文量
257
审稿时长
1 months
期刊介绍: Molecular Biology and Evolution Journal Overview: Publishes research at the interface of molecular (including genomics) and evolutionary biology Considers manuscripts containing patterns, processes, and predictions at all levels of organization: population, taxonomic, functional, and phenotypic Interested in fundamental discoveries, new and improved methods, resources, technologies, and theories advancing evolutionary research Publishes balanced reviews of recent developments in genome evolution and forward-looking perspectives suggesting future directions in molecular evolution applications.
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