دانلود رایگان مقاله چالش ها در آنالیز محاسبات ژنومی کلان داده

Abstract

DNA, RNA and protein are three major kinds of biological macromolecules with up to billions of basic elements in such biological organisms as human or mouse. They function at molecular, cellular and organismal levels individually and interactively. Traditional assays on such macromolecules are largely experimentally based, which are usually time consuming and laborious. In the past few years, high-throughput technologies, such as microarray and next-generation sequencing (NGS), were developed. Consequently, large genomic datasets are being generated and computational tools to analyzing these data are in urgent demand. This paper reviews several state-of-the-art high-throughput methodologies, representative projects, available databases and bioinformatics tools at different molecular levels. Finally, challenges and perspectives in processing genomic big data are discussed.

6. Conclusions and perspectives

Biological and biomedicine sciences are now coming into multidimensional OMICS era with high revolutions. The big data are generated on different biological components and are greatly speeding up clinical translational use. In this paper, we discuss several state-of-the-art high-throughput methodologies and data integrative approaches to solve biomedical questions or reveal biological mechanisms. We also demonstrate that NGS data facilitates the discovery of genetic variants associated with diseases; transcriptomics data creates a landscape of all transcripts in different cell types; proteomics data help quantitatively measure the presence of proteins and monitor the PTMs of proteins. Big data is also generated for multilevel molecular interactions (interactomics) and used to help us in understanding how organisms work as biological systems. Epigenomics data could further open another view and assist us to interpret how epigenetic modifications affect gene expression. At the same time, several major projects, public databases and consortiums regarding big data production and usefulness are introduced.