BackgroundAssays that are capable of detecting genome-wide chromatin interactions have produced massive amount of data and led to great understanding of the chromosomal three-dimensional (3D) structure. As technology becomes more sophisticated, higher-and-higher resolution data are being produced, going from the initial 1 Megabases (Mb) resolution to the current 10 Kilobases (Kb) or even 1 Kb resolution. The availability of genome-wide interaction data necessitates development of analytical methods to recover the underlying 3D spatial chromatin structure, but challenges abound. Most of the methods were proposed for analyzing data at low resolution (1 Mb). Their behaviors are thus unknown for higher resolution data. For such data, one of the key features is the high proportion of “0” contact counts among all available data, in other words, the excess of zeros.ResultsTo address the issue of excess of zeros, in this paper, we propose a truncated Random effect EXpression (tREX) method that can handle data at various resolutions. We then assess the performance of tREX and a number of leading existing methods for recovering the underlying chromatin 3D structure. This was accomplished by creating in-silico data to mimic multiple levels of resolution and submit the methods to a “stress test”. Finally, we applied tREX and the comparison methods to a Hi-C dataset for which FISH measurements are available to evaluate estimation accuracy.ConclusionThe proposed tREX method achieves consistently good performance in all 30 simulated settings considered. It is not only robust to resolution level and underlying parameters, but also insensitive to model misspecification. This conclusion is based on observations made in terms of 3D structure estimation accuracy and preservation of topologically associated domains. Application of the methods to the human lymphoblastoid cell line data on chromosomes 14 and 22 further substantiates the superior performance of tREX: the constructed 3D structure from tREX is consistent with the FISH measurements, and the corresponding distances predicted by tREX have higher correlation with the FISH measurements than any of the comparison methods.SoftwareAn open-source R-package is available at http://www.stat.osu.edu/~statgen/Software/tRex.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0894-z) contains supplementary material, which is available to authorized users.

Abstract-It is generally recognized that severe video distortions that are transient in space and/or time have a large effect on overall perceived video quality. In order to understand this phenomena, we study the distribution of spatio-temporally local quality scores obtained from several video quality assessment (VQA) algorithms on videos suffering from compression and lossy transmission over communication channels. We propose a content adaptive spatial and temporal pooling strategy based on the observed distribution. Our method adaptively emphasizes "worst" scores along both the spatial and temporal dimensions of a video sequence and also considers the perceptual effect of largearea cohesive motion flow such as egomotion. We demonstrate the efficacy of the method by testing it using three different VQA algorithms on the LIVE Video Quality database and the EPFL-PoliMI video quality database.

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A gene may be controlled by distal enhancers and repressors, not merely by regulatory elements in its promoter. Spatial organization of chromosomes is the mechanism that brings genes and their distal regulatory elements into close proximity. Recent molecular techniques, coupled with Next Generation Sequencing (NGS) technology, enable genome-wide detection of physical contacts between distant genomic loci. In particular, Hi-C is an NGS-aided assay for the study of genome-wide spatial interactions. The availability of such data makes it possible to reconstruct the underlying three-dimensional (3D) spatial chromatin structure. In this article, we present the Poisson Random effect Architecture Model (PRAM) for such an inference. The main feature of PRAM that separates it from previous methods is that it addresses the issue of over-dispersion and takes correlations among contact counts into consideration, thereby achieving greater consistency with observed data. PRAM was applied to Hi-C data to illustrate its performance and to compare the predicted distances with those measured by a Fluorescence In Situ Hybridization (FISH) validation experiment. Further, PRAM was compared to other methods in the literature based on both real and simulated data.