Abstract
Integrative Next Generation Sequencing (NGS) DNA and RNA analyses have very recently become feasible, and the published to date studies have discovered critical disease implicated pathways, and diagnostic and therapeutic targets. A growing number of exomes, genomes and transcriptomes from the same individual are quickly accumulating, providing unique venues for mechanistic and regulatory features analysis, and, at the same time, requiring new exploration strategies. In this study, we have integrated variation and expression information of four NGS datasets from the same individual: normal and tumor breast exomes and transcriptomes. Focusing on SNPcentered variant allelic prevalence, we illustrate analytical algorithms that can be applied to extract or validate potential regulatory elements, such as expression or growth advantage, imprinting, loss of heterozygosity (LOH), somatic changes, and RNA editing. In addition, we point to some critical elements that might bias the output and recommend alternative measures to maximize the confidence of findings. The need for such strategies is especially recognized within the growing appreciation of the concept of systems biology: integrative exploration of genome and transcriptome features reveal mechanistic and regulatory insights that reach far beyond linear addition of the individual datasets.
Highlights
With the evolution of next-generation sequencing (NGS) technologies, the time, cost and amount of the material needed are constantly declining, making applications such as genome/ exome and transcriptome sequencing increasingly feasible
While whole exome capture is designed based on the knowledge on all coding genomic sequences, transcriptome does not employ previous knowledge and captures the collection of expressed genes in the studied sample at the moment of harvesting
In terms of number of called variations, two major sources define the significant deviation between the two datasets – the transcriptome will not cover variations in genes that are not expressed, and the exome design does not include most of the large untranslated (UTR), but expressed regions
Summary
With the evolution of next-generation sequencing (NGS) technologies, the time, cost and amount of the material needed are constantly declining, making applications such as genome/ exome and transcriptome sequencing increasingly feasible. A rapidly growing number of exomes, genomes and transcriptomes from the same individual are accumulating, providing unique venues for mechanistic and regulatory feature analysis, and, at the same time, requiring new exploration strategies. Only a handful of studies have integrated NGS genome scale datasets. These studies have provided essential functional and regulatory insights, reaching far beyond linear addition of individual NGS dataset information layers, and often unraveling novel diagnostic and therapeutic targets [1,2,3,4,5,6]. We focus on one relatively unexplored aspect of integrative genomic analysis: SNP-centered allelic preferential expression at nucleotide resolution using exome and transcriptome data from the same individual
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