TACO produces robust multisample transcriptome assemblies from RNA-seq.

Accurate transcript structure and abundance inference from RNA sequencing (RNA-seq) data is foundational for molecular discovery. Here we present TACO, a computational method to reconstruct a consensus transcriptome from multiple RNA-seq data sets. TACO employs novel change-point detection to demarcate transcript start and end sites, leading to improved reconstruction accuracy compared with other tools in its class. The tool is available at http://tacorna.github.io and can be readily incorporated into RNA-seq analysis workflows.

The landscape of long noncoding RNAs in the human transcriptome.

Long noncoding RNAs (lncRNAs) are emerging as important regulators of tissue physiology and disease processes including cancer. To delineate genome-wide lncRNA expression, we curated 7,256 RNA sequencing (RNA-seq) libraries from tumors, normal tissues and cell lines comprising over 43 Tb of sequence from 25 independent studies. We applied ab initio assembly methodology to this data set, yielding a consensus human transcriptome of 91,013 expressed genes. Over 68% (58,648) of genes were classified as lncRNAs, of which 79% were previously unannotated. About 1% (597) of the lncRNAs harbored ultraconserved elements, and 7% (3,900) overlapped disease-associated SNPs. To prioritize lineage-specific, disease-associated lncRNA expression, we employed non-parametric differential expression testing and nominated 7,942 lineage- or cancer-associated lncRNA genes. The lncRNA landscape characterized here may shed light on normal biology and cancer pathogenesis and may be valuable for future biomarker development.

Human Visual Sensitivity to Plumes with a Gaussian.

This paper discusses results of a research project designed to develop an empirical model that could be used as a tool to predict human visual sensitivity to plumes. The resultant probability of detection algorithm (PROBDET) allows one to estimate the probability of a plume of known size, shape and contrast being detected visually. As a basis for the algorithm, a series of laboratory experiments using a high threshold signal detection procedure and computer generated images of plumes with Gaussian luminance distributions was conducted to measure human visual sensitivity to plumes. Results of the laboratory experiments are compared with results of contrast sensitivity experiments that examined visual sensitivity to stimuli with square and sine wave luminance distributions. An example of the PROBDET algorithm is presented to demonstrate its potential usefulness for assessing how probability of detection estimates change as plume size and contrast parameters vary. Since this research was designed to build on existing knowledge, a discussion of that knowledge and how it relates to the research conducted is also presented. The focus of this discussion is on the human visual system (HVS) and on how visual sensitivity is affected by factors such as the luminance of the stimulus and the surround, the luminance distribution of the stimulus, the size of the surround, and the size and spatial frequency characteristics of the stimulus.