A Linear Mixed Model Spline Framework for Analysing Time Course ‘Omics’ Data

Time course ‘omics’ experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. ‘Omics’ technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course ‘omics’ experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course ‘omics’ data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course ‘omics’ studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.     

Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data

We performed computational reconstruction of the in silico gene regulatory networks in the DREAM3 Challenges. Our task was to learn the networks from two types of data, namely gene expression profiles in deletion strains (the ‘deletion data’) and time series trajectories of gene expression after some initial perturbation (the ‘perturbation data’). In the course of developing the prediction method, we observed that the two types of data contained different and complementary information about the underlying network. In particular, deletion data allow for the detection of direct regulatory activities with strong responses upon the deletion of the regulator while perturbation data provide richer information for the identification of weaker and more complex types of regulation. We applied different techniques to learn the regulation from the two types of data. For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method. For perturbation data, we used differential equations to model the change of expression levels of a gene along the trajectories due to the regulation of other genes. We tried different models, and combined their predictions. The final predictions were obtained by merging the results from the two types of data. A comparison with the actual regulatory networks suggests that our approach is effective for networks with a range of different sizes. The success of the approach demonstrates the importance of integrating heterogeneous data in network reconstruction.     

Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data

Single-cell RNA sequencing enables us to characterize the cellular heterogeneity in single cell resolution with the help of cell type identification algorithms. However, the noise inherent in single-cell RNA-sequencing data severely disturbs the accuracy of cell clustering, marker identification and visualization. We propose that clustering based on feature density profiles can distinguish informative features from noise. We named such strategy as ‘entropy subspace’ separation and designed a cell clustering algorithm called ENtropy subspace separation-based Clustering for nOise REduction (ENCORE) by integrating the ‘entropy subspace’ separation strategy with a consensus clustering method. We demonstrate that ENCORE performs superiorly on cell clustering and generates high-resolution visualization across 12 standard datasets. More importantly, ENCORE enables identification of group markers with biological significance from a hard-to-separate dataset. With the advantages of effective feature selection, improved clustering, accurate marker identification and high-resolution visualization, we present ENCORE to the community as an important tool for scRNA-seq data analysis to study cellular heterogeneity and discover group markers.     

HIV-1 DIS stem loop forms an obligatory bent kissing intermediate in the dimerization pathway

The HIV-1 dimerization initiation sequence (DIS) is a conserved palindrome in the apical loop of a conserved hairpin motif in the 5′-untranslated region of its RNA genome. DIS hairpin plays an important role in genome dimerization by forming a ‘kissing complex’ between two complementary hairpins. Understanding the kinetics of this interaction is key to exploiting DIS as a possible human immunodeficiency virus (HIV) drug target. Here, we present a single-molecule Förster resonance energy transfer (smFRET) study of the dimerization reaction kinetics. Our data show the real-time formation and dissociation dynamics of individual kissing complexes, as well as the formation of the mature extended duplex complex that is ultimately required for virion packaging. Interestingly, the single-molecule trajectories reveal the presence of a previously unobserved bent intermediate required for extended duplex formation. The universally conserved A272 is essential for the formation of this intermediate, which is stabilized by Mg²⁺, but not by K⁺ cations. We propose a 3D model of a possible bent intermediate and a minimal dimerization pathway consisting of three steps with two obligatory intermediates (kissing complex and bent intermediate) and driven by Mg²⁺ ions.     

Beyond a Dichotomous View of the Concepts of ‘Sex’ and ‘Gender’ Focus Group Discussions among Gender Researchers at a Medical Faculty

Introduction

The concepts of ‘sex’ and ‘gender’ are both of vital importance in medicine and health sciences. However, the meaning of these concepts has seldom been discussed in the medical literature. The aim of this study was to explore what the concepts of ‘sex’ and ‘gender’ meant for gender researchers based in a medical faculty.

Methods

Sixteen researchers took part in focus group discussions. The analysis was performed in several steps. The participating researchers read the text and discussed ideas for analysis in national and international workshops. The data were analysed using qualitative content analysis. The authors performed independent preliminary analyses, which were further developed and intensively discussed between the authors.

Results

The analysis of meanings of the concepts of ‘sex’ and ‘gender’ for gender researchers based in a medical faculty resulted in three categories; “Sex as more than biology”, with the subcategories ‘sex’ is not simply biological, ‘sex’ as classification, and ‘sex’ as fluid and changeable; ”Gender as a multiplicity of power-related constructions”, with the subcategories: ‘gender’ as constructions, ‘gender’ power dimensions, and ‘gender’ as doing femininities and masculinities; “Sex and gender as interwoven”, with the subcategories: ‘sex’ and ‘gender’ as inseparable and embodying ‘sex’ and ‘gender’.

Conclusions

Gender researchers within medicine pointed out the importance of looking beyond a dichotomous view of the concepts of ‘sex’ and ‘gender’. The perception of the concepts was that ‘sex’ and ‘gender’ were intertwined. Further research is needed to explore how ‘sex’ and ‘gender’ interact.     

iPcc: a novel feature extraction method for accurate disease class discovery and prediction

Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define ‘correlation feature space’ for samples based on the gene expression profiles by iterative employment of Pearson’s correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles.     

Blind demixing methods for recovering dense neuronal morphology from barcode imaging data

Cellular barcoding methods offer the exciting possibility of ‘infinite-pseudocolor’ anatomical reconstruction—i.e., assigning each neuron its own random unique barcoded ‘pseudocolor,’ and then using these pseudocolors to trace the microanatomy of each neuron. Here we use simulations, based on densely-reconstructed electron microscopy microanatomy, with signal structure matched to real barcoding data, to quantify the feasibility of this procedure. We develop a new blind demixing approach to recover the barcodes that label each neuron, and validate this method on real data with known barcodes. We also develop a neural network which uses the recovered barcodes to reconstruct the neuronal morphology from the observed fluorescence imaging data, ‘connecting the dots’ between discontiguous barcode amplicon signals. We find that accurate recovery should be feasible, provided that the barcode signal density is sufficiently high. This study suggests the possibility of mapping the morphology and projection pattern of many individual neurons simultaneously, at high resolution and at large scale, via conventional light microscopy.     

Testing the Sensitivity of Tract-Based Spatial Statistics to Simulated Treatment Effects in Preterm Neonates

Early neuroimaging may provide a surrogate marker for brain development and outcome after preterm birth. Tract-Based Spatial Statistics (TBSS) is an advanced Diffusion Tensor Image (DTI) analysis technique that is sensitive to the effects of prematurity and may provide a quantitative marker for neuroprotection following perinatal brain injury or preterm birth. Here, we test the sensitivity of TBSS to detect diffuse microstructural differences in the developing white matter of preterm infants at term-equivalent age by modelling a ‘treatment’ effect as a global increase in fractional anisotropy (FA). As proof of concept we compare these simulations to a real effect of increasing age at scan. 3-Tesla, 15-direction diffusion tensor imaging (DTI) was acquired from 90 preterm infants at term-equivalent age. Datasets were randomly assigned to ‘treated’ or ‘untreated’ groups of increasing size and voxel-wise increases in FA were used to simulate global treatment effects of increasing magnitude in all ‘treated’ maps. ‘Treated’ and ‘untreated’ FA maps were compared using TBSS. Predictions from simulated data were then compared to exemplar TBSS group comparisons based on increasing postmenstrual age at scan. TBSS proved sensitive to global differences in FA within a clinically relevant range, even in relatively small group sizes, and simulated data were shown to predict well a true biological effect of increasing age on white matter development. These data confirm that TBSS is a sensitive tool for detecting global group-wise differences in FA in this population.     

Genome sequencing and analysis of two early-flowering cherry (Cerasus × kanzakura) varieties, ‘Kawazu-zakura’ and ‘Atami-zakura’

To gain genetic insights into the early-flowering phenotype of ornamental cherry, also known as sakura, we determined the genome sequences of two early-flowering cherry (Cerasus × kanzakura) varieties, ‘Kawazu-zakura’ and ‘Atami-zakura’. Because the two varieties are interspecific hybrids, likely derived from crosses between Cerasus campanulata (early-flowering species) and Cerasus speciosa, we employed the haplotype-resolved sequence assembly strategy. Genome sequence reads obtained from each variety by single-molecule real-time sequencing (SMRT) were split into two subsets, based on the genome sequence information of the two probable ancestors, and assembled to obtain haplotype-phased genome sequences. The resultant genome assembly of ‘Kawazu-zakura’ spanned 519.8 Mb with 1,544 contigs and an N50 value of 1,220.5 kb, while that of ‘Atami-zakura’ totalled 509.6 Mb with 2,180 contigs and an N50 value of 709.1 kb. A total of 72,702 and 69,528 potential protein-coding genes were predicted in the genome assemblies of ‘Kawazu-zakura’ and ‘Atami-zakura’, respectively. Gene clustering analysis identified 2,634 clusters uniquely presented in the C. campanulata haplotype sequences, which might contribute to its early-flowering phenotype. Genome sequences determined in this study provide fundamental information for elucidating the molecular and genetic mechanisms underlying the early-flowering phenotype of ornamental cherry tree varieties and their relatives.     

The Switch from Low-Pressure Sodium to Light Emitting Diodes Does Not Affect Bat Activity at Street Lights

We used a before-after-control-impact paired design to examine the effects of a switch from low-pressure sodium (LPS) to light emitting diode (LED) street lights on bat activity at twelve sites across southern England. LED lights produce broad spectrum ‘white’ light compared to LPS street lights that emit narrow spectrum, orange light. These spectral differences could influence the abundance of insects at street lights and thereby the activity of the bats that prey on them. Most of the bats flying around the LPS lights were aerial-hawking species, and the species composition of bats remained the same after the switch-over to LED. We found that the switch-over from LPS to LED street lights did not affect the activity (number of bat passes), or the proportion of passes containing feeding buzzes, of those bat species typically found in close proximity to street lights in suburban environments in Britain. This is encouraging from a conservation perspective as many existing street lights are being, or have been, switched to LED before the ecological consequences have been assessed. However, lighting of all spectra studied to date generally has a negative impact on several slow-flying bat species, and LED lights are rarely frequented by these ‘light-intolerant’ bat species.     

sPARTA: a parallelized pipeline for integrated analysis of plant miRNA and cleaved mRNA data sets,including new miRNA target-identification software

Parallel analysis of RNA ends (PARE) is a technique utilizing high-throughput sequencing to profile uncapped, mRNA cleavage or decay products on a genome-wide basis. Tools currently available to validate miRNA targets using PARE data employ only annotated genes, whereas important targets may be found in unannotated genomic regions. To handle such cases and to scale to the growing availability of PARE data and genomes, we developed a new tool, ‘sPARTA’ (small RNA-PARE target analyzer) that utilizes a built-in, plant-focused target prediction module (aka ‘miRferno’). sPARTA not only exhibits an unprecedented gain in speed but also it shows greater predictive power by validating more targets, compared to a popular alternative. In addition, the novel ‘seed-free’ mode, optimized to find targets irrespective of complementarity in the seed-region, identifies novel intergenic targets. To fully capitalize on the novelty and strengths of sPARTA, we developed a web resource, ‘comPARE’, for plant miRNA target analysis; this facilitates the systematic identification and analysis of miRNA-target interactions across multiple species, integrated with visualization tools. This collation of high-throughput small RNA and PARE datasets from different genomes further facilitates re-evaluation of existing miRNA annotations, resulting in a ‘cleaner’ set of microRNAs.     

Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.     

Realistic artificial DNA sequences as negative controls for computational genomics

A common practice in computational genomic analysis is to use a set of ‘background’ sequences as negative controls for evaluating the false-positive rates of prediction tools, such as gene identification programs and algorithms for detection of cis-regulatory elements. Such ‘background’ sequences are generally taken from regions of the genome presumed to be intergenic, or generated synthetically by ‘shuffling’ real sequences. This last method can lead to underestimation of false-positive rates. We developed a new method for generating artificial sequences that are modeled after real intergenic sequences in terms of composition, complexity and interspersed repeat content. These artificial sequences can serve as an inexhaustible source of high-quality negative controls. We used artificial sequences to evaluate the false-positive rates of a set of programs for detecting interspersed repeats, ab initio prediction of coding genes, transcribed regions and non-coding genes. We found that RepeatMasker is more accurate than PClouds, Augustus has the lowest false-positive rate of the coding gene prediction programs tested, and Infernal has a low false-positive rate for non-coding gene detection. A web service, source code and the models for human and many other species are freely available at http://repeatmasker.org/garlic/.     

Search Query Data to Monitor Interest in Behavior Change: Application for Public Health

There is a need for effective interventions and policies that target the leading preventable causes of death in the U.S. (e.g., smoking, overweight/obesity, physical inactivity). Such efforts could be aided by the use of publicly available, real-time search query data that illustrate times and locations of high and low public interest in behaviors related to preventable causes of death.

Objectives

This study explored patterns of search query activity for the terms ‘weight’, ‘diet’, ‘fitness’, and ‘smoking’ using Google Insights for Search.

Methods

Search activity for ‘weight’, ‘diet’, ‘fitness’, and ‘smoking’ conducted within the United States via Google between January 4^th, 2004 (first date data was available) and November 28^th, 2011 (date of data download and analysis) were analyzed. Using a generalized linear model, we explored the effects of time (month) on mean relative search volume for all four terms.

Results

Models suggest a significant effect of month on mean search volume for all four terms. Search activity for all four terms was highest in January with observable declines throughout the remainder of the year.

Conclusions

These findings demonstrate discernable temporal patterns of search activity for four areas of behavior change. These findings could be used to inform the timing, location and messaging of interventions, campaigns and policies targeting these behaviors.     

Real-time single-molecule tethered particle motion analysis reveals mechanistic similarities and contrasts of Flp site-specific recombinase with Cre and λ Int

Flp, a tyrosine site-specific recombinase coded for by the selfish two micron plasmid of Saccharomyces cerevisiae, plays a central role in the maintenance of plasmid copy number. The Flp recombination system can be manipulated to bring about a variety of targeted DNA rearrangements in its native host and under non-native biological contexts. We have performed an exhaustive analysis of the Flp recombination pathway from start to finish by using single-molecule tethered particle motion (TPM). The recombination reaction is characterized by its early commitment and high efficiency, with only minor detraction from ‘non-productive’ and ‘wayward’ complexes. The recombination synapse is stabilized by strand cleavage, presumably by promoting the establishment of functional interfaces between adjacent Flp monomers. Formation of the Holliday junction intermediate poses a rate-limiting barrier to the overall reaction. Isomerization of the junction to the conformation favoring its resolution in the recombinant mode is not a slow step. Consistent with the completion of nearly every initiated reaction, the chemical steps of strand cleavage and exchange are not reversible during a recombination event. Our findings demonstrate similarities and differences between Flp and the mechanistically related recombinases λ Int and Cre. The commitment and directionality of Flp recombination revealed by TPM is consistent with the physiological role of Flp in amplifying plasmid DNA.     

Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses

Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean–variance relationship of the log-counts-per-million using ‘voom’. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source ‘limma’ package.     

Mapping the genetic and tissular diversity of 64 phenolic compounds in Citrus species using a UPLC–MS approach

Background and Aims Phenolic compounds contribute to food quality and have potential health benefits. Consequently, they are an important target of selection for Citrus species. Numerous studies on this subject have revealed new molecules, potential biosynthetic pathways and linkage between species. Although polyphenol profiles are correlated with gene expression, which is responsive to developmental and environmental cues, these factors are not monitored in most studies. A better understanding of the biosynthetic pathway and its regulation requires more information about environmental conditions, tissue specificity and connections between competing sub-pathways. This study proposes a rapid method, from sampling to analysis, that allows the quantitation of multiclass phenolic compounds across contrasting tissues and cultivars.Methods Leaves and fruits of 11 cultivated citrus of commercial interest were collected from adult trees grown in an experimental orchard. Sixty-four phenolic compounds were simultaneously quantified by ultra-high-performance liquid chromatography coupled with mass spectrometry.Key Results Combining data from vegetative tissues with data from fruit tissues improved cultivar classification based on polyphenols. The analysis of metabolite distribution highlighted the massive accumulation of specific phenolic compounds in leaves and the external part of the fruit pericarp, which reflects their involvement in plant defence. The overview of the biosynthetic pathway obtained confirmed some regulatory steps, for example those catalysed by rhamnosyltransferases. The results suggest that three other steps are responsible for the different metabolite profiles in ‘Clementine’ and ‘Star Ruby’ grapefruit.Conclusions The method described provides a high-throughput method to study the distribution of phenolic compounds across contrasting tissues and cultivars in Citrus, and offers the opportunity to investigate their regulation and physiological roles. The method was validated in four different tissues and allowed the identification and quantitation of 64 phenolic compounds in 20 min, which represents an improvement over existing methods of analysing multiclass polyphenols.     

Recognizing Complex Upper Extremity Activities Using Body Worn Sensors

To evaluate arm-hand therapies for neurological patients it is important to be able to assess actual arm-hand performance objectively. Because instruments that measure the actual quality and quantity of specific activities in daily life are lacking, a new measure needs to be developed. The aims of this study are to a) elucidate the techniques used to identify upper extremity activities, b) provide a proof-of-principle of this method using a set of activities tested in a healthy adult and in a stroke patient, and c) provide an example of the method’s applicability in daily life based on readings taken from a healthy adult. Multiple devices, each of which contains a tri-axial accelerometer, a tri-axial gyroscope and a tri-axial magnetometer were attached to the dominant hand, wrist, upper arm and chest of 30 healthy participants and one stroke patient, who all performed the tasks ‘drinking’, ‘eating’ and ‘brushing hair’ in a standardized environment. To establish proof-of-principle, a prolonged daily life recording of 1 participant was used to identify the task ‘drinking’. The activities were identified using multi-array signal feature extraction and pattern recognition algorithms and 2D-convolution. The activities ‘drinking’, ‘eating’ and ‘brushing hair’ were unambiguously recognized in a sequence of recordings of multiple standardized daily activities in a healthy participant and in a stroke patient. It was also possible to identify a specific activity in a daily life recording. The long term aim is to use this method to a) identify arm-hand activities that someone performs during daily life, b) determine the quantity of activity execution, i.e. amount of use, and c) determine the quality of arm-hand skill performance.     

Variability of Stepping during a Virtual Reality Paradigm in Parkinson’s Disease Patients with and without Freezing of Gait

Background

Freezing of gait is a common and debilitating symptom affecting many patients with advanced Parkinson’s disease. Although the pathophysiology of freezing of gait is not fully understood, a number of observations regarding the pattern of gait in patients with this symptom have been made. Increased ‘Stride Time Variability’ has been one of the most robust of these features. In this study we sought to identify whether patients with freezing of gait demonstrated similar fluctuations in their stepping rhythm whilst performing a seated virtual reality gait task that has recently been used to demonstrate the neural correlate of the freezing phenomenon.

Methods

Seventeen patients with freezing and eleven non-freezers performed the virtual reality task twice, once whilst ‘On’ their regular Parkinsonian medication and once in their practically defined ‘Off’ state.

Results

All patients displayed greater step time variability during their ‘Off’ state assessment compared to when medicated. Additionally, in the ‘Off’ state, patients with freezing of gait had greater step time variability compared to non-freezers. The five steps leading up to a freezing episode in the virtual reality environment showed a significant increase in step time variability although the final three steps preceding the freeze were not characterized by a progressive shortening of latency.

Conclusions

The results of this study suggest that characteristic features of gait disturbance observed in patients with freezing of gait can also be demonstrated with a virtual reality paradigm. These findings suggest that virtual reality may offer the potential to further explore the freezing phenomenon in Parkinson’s disease.