BackgroundLong non-coding RNAs (lncRNAs) have emerged as key players in a remarkably variety of biological processes and pathologic conditions, including cancer. Next-generation sequencing technologies and bioinformatics procedures predict the existence of tens of thousands of lncRNAs, from which we know the functions of only a handful of them, and very little is known in cancer types such as head and neck squamous cell carcinomas (HNSCCs).ResultsHere, we use RNAseq expression data from The Cancer Genome Atlas (TCGA) and various statistic and software tools in order to get insight about the lncRNome in HNSCC. Based on lncRNA expression across 426 samples, we discover five distinct tumor clusters that we compare with reported clusters based on various genomic/genetic features. Results demonstrate significant associations between lncRNA-based clustering and DNA methylation, TP53 mutation, and human papillomavirus infection. Using “guilt-by-association” procedures, we infer the possible biological functions of representative lncRNAs of each cluster. Furthermore, we found that lncRNA clustering is correlated with some important clinical and pathologic features, including patient survival after treatment, tumor grade, or sub-anatomical location.ConclusionsWe present a landscape of lncRNAs in HNSCC and provide associations with important genotypic and phenotypic features that may help to understand the disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-017-0334-6) contains supplementary material, which is available to authorized users.

Background

This chapter presents an overview of the evolution of computer architecture, giving special attention on those advances which have promoted the current hybrid systems. After that, we focus on the most extended programming strategies applied to hybrid computing. In fact, programming is one of the most important challenges for this kind of systems, as it requires a high knowledge of the hardware to achieve a good performance. Current hybrid systems are basically composed by three components, two processors (multicore and manycore) and an interconnection bus (PCIe), which connects both processors. Each of the components must be managed differently. After presenting the particular features of current hybrid systems, authors focus on introducing some approaches to exploit simultaneously each of the components. Finally, to clarify how to program in these platforms, two cases studies are presented and analyzed in deep. At the end of the chapter, authors outline the main insights behind hybrid computing and introduce upcoming advances in hybrid systems.

Hyperspectral sensors acquire images with hundreds of spectral channels. These images have a lot of information in both spectral and spatial domain, and with this kind of information different research studies can be accomplished. In this work, we present several optimizations for hyperspectral image processing algorithms intended to detect targets in hyperspectral images. The hyperspectral image selected for our study was collected by the NASAs Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center (WTC) in New York, five days after September 11th attack. The algorithm used in our experiments is the automated target generation process (ATGP) and our optimizations comprise parallel versions of the algorithm developed using open multi-processing (OpenMP) and message passing interface (MPI). Our experiments indicate that the ATGP can be successfully implemented in parallel in multicore and cluster computing architectures, including Intel Xeon Phi.