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论文范文
1. Introduction Recent advances in next-generation sequencing technologies provide revolutionary opportunities to characterize the genomic landscapes of individuals at single base resolution for identifying actionable mutations for disease treatment and management [1, 2]. Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome, that is, all coding regions of known genes in a genome. For example, more than 85% of disease-causing mutations in Mendelian diseases are found in the exome, and WES provides an unbiased approach to detect these variants in the era of personalized and precision medicine. Next-generation sequencing technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. For example, the Exome Aggregation Consortium (ExAC) has assembled and reanalyzed WES data of 60,706 unrelated individuals from various disease-specific and population genetic studies [3]. To gain insights in WES, novel computational algorithms and bioinformatics methods represent a critical component in modern biomedical research to analyze and interpret these massive datasets. Genomic studies that employ WES have increased over the years, and new bioinformatics methods and computational tools have developed to assist the analysis and interpretation of this data (Figure 1). The majority of WES computational tools are centered on the generation of a Variant Calling Format (VCF) file from raw sequencing data. Once the VCF files have been generated, further downstream analyses can be performed by other computational methods. Therefore, in this review we have classified bioinformatics methods and computational tools into Pre-VCF and Post-VCF categories. Pre-VCF workflows include tools for aligning the raw sequencing reads to a reference genome, variant detection, and annotation. Post-VCF workflows include methods for somatic mutation detection, pathway analysis, copy number alterations, INDEL identification, and driver prediction. Depending on the nature of the hypothesis, beyond VCF analysis can also include methods that link variants to clinical data as well as potential therapeutics (Figure 2). Computational tools developed to align raw sequencing data to an annotated VCF file have been well established. Most studies tend to follow workflows associated with GATK [4–6], SAMtools [7], or a combination of these. In general, workflows start with aligning WES reads to a reference genome and noting reads that vary. The most common of these variants are single nucleotide variants (SNVs) but also include insertions, deletions, and rearrangements. The location of these variants is used to annotate them to a specific gene. After annotation, the SNVs found can be compared to databases of SNVs found in other studies. This allows for the determination of frequency of a particular SNV in a given population. In some studies, such as those relating to cancer, rare somatic mutations are of interest. However, in Mendelian studies, the germline mutational landscape will be of more interest than somatic mutations. Before a final VCF file is produced for a given sample, software can be used to predict if the variant will be functionally damaging to the protein for prioritizing candidate genes for further study. 
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