技术决定算法：读段比对的最新进展
Technology dictates algorithms: Recent developments in read alignment
AI总结：
本文综述了1988至2020年间107种读段比对算法，评估11种比对器性能，探讨长读段优劣及多领域定制应用。
摘要：
大规模并行测序技术通过提供对人类、动物和微生物基因组的空前洞察，彻底改变了生物和医学科学。现代测序平台以核苷酸序列或读段的形式产生海量基因组数据。将读段比对到参考基因组上，能够识别个体特异的遗传变异，并且是大多数基因组分析流程中的关键步骤。比对后的读段对于回答重要的生物学问题至关重要，例如检测导致各种人类疾病和复杂性状的突变，以及识别宏基因组样本中存在的物种。由于分析数据集规模庞大以及测序平台存在众多技术限制，读段比对问题极具挑战性，研究人员已开发出新颖的生物信息学算法来应对这些困难。重要的是，计算算法随着技术的进步而演变和多样化，导致了当今多样化的生物信息学工具。我们的综述调查了1988年至2020年间发表的107种比对方法的算法基础和方法论，涵盖短读段和长读段。我们对11种读段比对器进行了严格的实验评估，以展示这些底层算法对读段比对器速度和效率的影响。我们分别讨论了更长的读段长度如何为读段比对技术带来独特的优势和限制。我们还讨论了通用比对算法如何被定制以适应生物学各领域的特定需求，包括全转录组、适应性免疫库和人类微生物组研究。
英文摘要：
Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to todays diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies.
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