Translational bioinformatics for diagnostic and prognostic prediction of prostate cancer in the next-generation sequencing era

Abstract

The discovery of prostate cancer biomarkers has been boosted by the advent of next-generation sequencing (NGS) technologies. Nevertheless, many challenges still exist in exploiting the flood of sequence data and translating them into routine diagnostics and prognosis of prostate cancer. Here we review the recent developments in prostate cancer biomarkers by high throughput sequencing technologies. We highlight some fundamental issues of translational bioinformatics and the potential use of cloud computing in NGS data processing for the improvement of prostate cancer treatment.

Figure 1

NGS-based pipeline for cancer marker discovery.

Figure 2

Translational bioinformatics bridges knowledge from molecules to populations. Four subdisciplines of translational bioinformatics and their respective focus areas are depicted in boxes. The success of translational bioinformatics will enable a complete information logistics chain from single molecules to the entire human population and thus link innovations from bench to bedside.

Figure 3

Schematic of the cloud-based NGS analysis. Local computers allocate the cloud-based web services over the internet. Web services comprised a cluster of virtual machines (one master node and a chosen number of worker nodes). Input data are transferred to the cloud storage and the program code driving the computation is uploaded to master nodes, by which worker nodes are provisioned. Each worker node downloads reads from the storage and run computation independently. The final result is stored and meanwhile transferred to the local client computer and the job completes.