bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large-scale assessment of olfactory preferences and learning inDrosophila melanogaster: behavioral and genetic measures
Authors: Versace Elisabetta , Reisenberger Julia 
Published: February 2015

AbstractIn the Evolve and Resequence method (E&R), experimental evolution and genomics are combined to investigate evolutionary dynamics and the genotype-phenotype link. This approach requires many replicates with large population sizes, which imposes severe restrictions on the analysis of behavioral phenotypes. Aiming to use E&R for investigating the evolution of behavior inDrosophila, we have developed a simple and effective method to assess spontaneous olfactory preferences and learning in large samples of fruit flies using a T-maze. We tested this procedure on (a) a large wild-caught population and (b) 11 isofemale lines ofDrosophila melanogaster. Compared to previous methods, this procedure reduces the environmental noise and allows for the analysis of large population samples. Consistent with previous results, we show that flies have a spontaneous preference for orange vs. apple odor. With our procedure wild-derived flies exhibit olfactory learning in the absence of previous laboratory selection. Furthermore, we find genetic differences in the olfactory learning with relatively high heritability. We propose this large-scale method as an effective tool for E&R and genome-wide association studies on olfactory preferences and learning.
DeDaL: Cytoscape 3.0 app for producing and morphing data-driven and structure-driven network layouts
Authors: Czerwinska Urszula , Calzone Laurence , Barillot Emmanuel , Zinovyev Andrei 
Published: February 2015

AbstractBackgroundVisualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistical insights into biological functions. Currently, high-throughput data are usually visualized on top of predefined network layouts which are not always adapted to a given data analysis task. We developed a Cytoscape app which allows to construct biological network layouts based on the data from molecular profiles imported as values of nodes attributes.ResultsDeDaL is a Cytoscape 3.0 app which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. Combining these possibilities facilitates creating insightful network layouts representing both structural network features and the correlation patterns in multivariate data.ConclusionsDeDaL is the first method allowing to construct biological network layouts from high-throughput data. DeDaL is freely available for downloading together with step-by-step tutorial athttp://bioinfo-out.curie.fr/projects/dedal/.
Evolution of Conditional Cooperativity Between HOXA11 and FOXO1 Through Allosteric Regulation
Authors: Nnamani Mauris C. , Ganguly Soumya , Lynch Vincent J. , Mizoue Laura S. , Tong Yingchun , Darling Heather L. , Fuxreiter Monika , Meiler Jens , Wagner Günter P. 
Published: February 2015

SUMMARYTranscription factors (TFs) play multiple roles in different cells and stages of development. Given this multitude of functional roles it has been assumed that TFs are evolutionarily highly constrained. Here we investigate the molecular mechanisms for the origin of a derived functional interaction between two TFs that play a key role in mammalian pregnancy, HOXA11 and FOXO1. We have previously shown that the regulatory role of HOXA11 in mammalian endometrial stromal cells requires an interaction with FOXO1, and that the physical interaction between these proteins evolved long before their functional cooperativity. Through a combination of functional, biochemical, and structural approaches, we demonstrate that the derived functional cooperativity between HOXA11 and FOXO1 is due to derived allosteric regulation of HOXA11 by FOXO1. This study shows that TF function can evolve through changes affecting the functional output of a pre-existing protein complex.
Ca2+-activation kinetics modulate successive puff/spark amplitude, duration and inter-event-interval correlations in a Langevin model of stochastic Ca2+release
Authors: Wang Xiao , Hao Yan , Weinberg Seth H. , Smith Gregory D. 
Published: February 2015

AbstractThrough theoretical analysis of the statistics of stochastic calcium (Ca2+) release (i.e., the amplitude, duration and inter-event interval of simulated Ca2+puffs and sparks), we show that a Langevin description of the collective gating of Ca2+channels may be a good approximation to the corresponding Markov chain model when the number of Ca2+channels per Ca2+release unit (CaRU) is in the physiological range. The Langevin description of stochastic Ca2+release facilitates our investigation of correlations between successive puff/spark amplitudes, durations and inter-spark intervals, and how such puff/spark statistics depend on the number of channels per release site and the kinetics of Ca2+-mediated inactivation of open channels. When Ca2+inactivation/de-inactivation rates are intermediate—i.e., the termination of Ca2+puff/sparks is caused by the recruitment of inactivated channels—the correlation between successive puff/spark amplitudes is negative, while the correlations between puff/spark amplitudes and the duration of the preceding or subsequent inter-spark interval are positive. These correlations are significantly reduced when inactivation/deinactivation rates are extreme (slow or fast) and puff/sparks terminate via stochastic attrition.
Ebola virus is evolving but not changing: no evidence for functional change in EBOV from 1976 to the 2014 outbreak
Authors: Olabode Abayomi S , Jiang Xiaowei , Robertson David L , Lovell Simon C 
Published: February 2015

AbstractThe Ebola epidemic is having a devastating impact in West Africa. Sequencing of Ebola viruses from infected individuals has revealed extensive genetic variation, leading to speculation that the virus may be adapting to the human host and accounting for the scale of the 2014 outbreak. We show that so far there is no evidence for adaptation of EBOV to humans. We analyze the putatively functional changes associated with the current and previous Ebola outbreaks, and find no significant molecular changes. Observed amino acid replacements have minimal effect on protein structure, being neither stabilizing nor destabilizing. Replacements are not found in regions of the proteins associated with known functions and tend to occur in disordered regions. This observation indicates that the difference between the current and previous outbreaks is not due to the observed evolutionary change of the virus. Instead, epidemiological factors must be responsible for the unprecedented spread of EBOV.
Modulating pathological oscillations by rhythmic non-invasive brain stimulation – a therapeutic concept?
Authors: Krawinkel LK , Engel AK , Hummel FC 
Published: February 2015

AbstractA large amount of studies of the last decades revealed an association between human behaviour and oscillatory activity in the human brain. Alike, abnormalities of oscillatory activity were related with pathological behaviour in many neuropsychiatric disorders, such as in Parkinson’s disease (PD) or in schizophrenia (SCZ). As a therapeutic tool, non-invasive brain stimulation (NIBS) has demonstrated the potential to improve behavioural performance in patients suffering from neuropsychiatric disorders. Since evidence accumulates that NIBS might be able to modulate oscillatory activity and related behaviour in a scientific setting, this review focuses on discussing potential interventional strategies to target abnormalities in oscillatory activity in neuropsychiatric disorders. In particular, we will review oscillatory changes described in patients after stroke, with PD or suffering from SCZ. Potential ways of targeting interventionally the underlying pathological oscillations to improve related pathological behaviour will be further discussed.
Permutation Testing in the Presence of Polygenic Variation
Authors: Abney Mark 
Published: February 2015

AbstractThis article discusses problems with and solutions to performing valid permutation tests for quantitative trait loci in the presence of polygenic effects. Although permutation testing is a popular approach for determining statistical significance of a test statistic with an unknown distribution–for instance, the maximum of multiple correlated statistics or some omnibus test statistic for a gene, gene-set or pathway–naive application of permutations may result in an invalid test. The risk of performing an invalid permutation test is particularly acute in complex trait mapping where polygenicity may combine with a structured population resulting from the presence of families, cryptic relatedness, admixture or population stratification. I give both analytical derivations and a conceptual understanding of why typical permutation procedures fail and suggest an alternative permutation based algorithm, MVNpermute, that succeeds. In particular, I examine the case where a linear mixed model is used to analyze a quantitative trait and show that both phenotype and genotype permutations may result in an invalid permutation test. I provide a formula that predicts the amount of inflation of the type 1 error rate depending on the degree of misspecification of the covariance structure of the polygenic effect and the heritability of the trait. I validate this formula by doing simulations, showing that the permutation distribution matches the theoretical expectation, and that my suggested permutation based test obtains the correct null distribution. Finally, I discuss situations where naive permutations of the phenotype or genotype are valid and the applicability of the results to other test statistics.
Transition densities and sample frequency spectra of diffusion processes with selection and variable population size
Authors: Živković Daniel , Steinrücken Matthias , Song Yun S. , Stephan Wolfgang 
Published: February 2015

AbstractAdvances in empirical population genetics have made apparent the need for models that simultaneously account for selection and demography. To address this need, we here study the Wright-Fisher diffusion under selection and variable effective population size. In the case of genic selection and piecewise-constant effective population sizes, we obtain the transition density function by extending a recently developed method for computing an accurate spectral representation for a constant population size. Utilizing this extension, we show how to compute the sample frequency spectrum (SFS) in the presence of genic selection and an arbitrary number of instantaneous changes in the effective population size. We also develop an alternate, efficient algorithm for computing the SFS using a method of moments. We apply these methods to answer the following questions: If neutrality is incorrectly assumed when there is selection, what effects does it have on demographic parameter estimation? Can the impact of negative selection be observed in populations that undergo strong exponential growth?
SWORD - a highly efficient protein database search
Authors: Vaser Robert , Pavlovi Dario , Korpar Matija , Sikic Mile 
Published: February 2015

ABSTRACTMotivationProtein database search is one of the fundamental problems in bioinformatics. For decades, it has been explored and solved using different exact and heuristic approaches. However, exponential growth of data in size in recent years has brought significant challenges in improving already existing algorithms. BLASTP has been the most successful tool for protein database search, but is also becoming a bottleneck in many applications. Due to that, many different approaches have been developed to comple-ment or replace BLASTP. In this paper, we present SWORD, an efficient protein database search implementation that runs 3-4 faster than BLASTP in the sensitive mode and up to 18 faster in the fast and less accurate mode and also provides guaranteed optimal alignments for candidate sequences. SWORD is designed to be used in nearly all database search environments, but is especially suitable for large databases. Its sensitivity exceeds that of BLASTP for majority of input datasets.AvailabilitySword is freely available for download fromhttps://github.com/rvaser/swordContactrobert.vaser@fer.hr,mile.sikic@fer.hr
Side-chain-directed model and map validation for 3D Electron Cryomicroscopy
Authors: Barad Benjamin A , Echols Nathaniel , Wang Ray Y-R , Cheng Yifan , DiMaio Frank , Adams Paul D , Fraser James S 
Published: February 2015

AbstractAdvances in electron cryomicroscopy allow for the building ofde novoatomic models into high resolution Coulomb potential maps. While established validation metrics independently assess map quality and model geometry, methods to assess the precise fitting of an atomic model into the map and to validate the interpretation of high resolution features are less well developed. Here, we present EMRinger, which tests model-to-map agreement using side-chain dihedral-directed map density measurements. These measurements reveal local map density peaks and show that peaks located at rotameric angles are a sensitive marker of whether the backbone is correctly positioned. The EMRinger Score can be improved by model refinement, suggesting its utility as an effective model-to-map validation metric. Additionally, EMRinger sampling identifies how radiation damage alters scattering from negatively charged amino acids during data collection. EMRinger will be useful in assessing how advances in cryo-EM increase the ability to resolve and model high-resolution features.
A mechanistic link between cellular trade-offs, gene expression and growth
Authors: Weiβe Andrea Y. , Oyarzún Diego A. , Danos Vincent , Swain Peter S. 
Published: February 2015

AbstractIntracellular processes rarely work in isolation but continually, interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that because of limitations in levels of cellular energy, free ribosomes, and proteins are faced by all living cells and construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod's law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth related effects in dosage compensation by paralogs and predict host-circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modelling framework has potentially wide application, including in both biotechnology and medicine.
dbSUPER: a database of super-enhancers in mouse and human genome
Authors: Khan Aziz , Zhang Xuegong 
Published: February 2015

ABSTRACTSuper-enhancers are the clusters of transcriptional enhancers that can drive cell-type-specific gene expression and also crucial in cell identity. Many disease-associated sequence variations are enriched in super-enhancer regions of disease-relevant cell types. Thus, super-enhancers can be used as potential biomarkers for disease diagnosis and therapeutics. Current studies have identified super-enhancers in more than 100 cell types and demonstrated their functional importance. However, no centralized resource to integrate all these findings is available yet. We developed dbSUPER (http://bioinfo.au.tsinghua.edu.cn/dbsuper/), the first integrated and interactive database of super-enhancers, with the primary goal of providing a resource for assistance in further studies related to transcriptional control of cell identity and disease. dbSUPER provides a responsive and user-friendly web interface to facilitate efficient and comprehensive search and browsing. The data can be easily sent to Galaxy instances, GREAT and Cistrome web servers for downstream analysis, and can be visualized in UCSC genome browser while custom tracks added automatically. The data can be downloaded and exported in variety of formats. Further, dbSUPER lists genes associated with the super-enhancers and links to various other databases such as GeneCards, UniProt and Entrez. dbSUPER also provides an overlap analysis tool, to annotate user defined regions. We believe dbSUPER is a valuable resource for the biologists and genetic research communities.
Epistasis and Entropy
Authors: Crona Kristina 
Published: February 2015

ABSTRACTEpistasis is a key concept in the theory of adaptation. Indicators of epistasis are of interest for large system where systematic fitness measurements may not be possible. Some recent approaches depend on information theory. We show that considering shared entropy for pairs of loci can be misleading. The reason is that shared entropy does not imply epistasis for the pair. This observation holds true also in the absence of higher order epistasis. We discuss a refined approach for identifying pairwise interactions using entropy.
iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing cancer driver genes in personal genomes
Authors: Dong Chengliang , Yang Hui , He Zeyu , Liu Xiaoming , Wang Kai 
Published: February 2015

AbstractAll cancers arise as a result of the acquisition of somatic mutations that drive the disease progression. A number of computational tools have been developed to identify driver genes for a specific cancer from a group of cancer samples. However, it remains a challenge to identify driver mutations/genes for an individual patient and design drug therapies. We developed iCAGES, a novel statistical framework to rapidly analyze patient-specific cancer genomic data, prioritize personalized cancer driver events and predict personalized therapies. iCAGES includes three consecutive layers: the first layer integrates contributions from coding, non-coding and structural variations to infer driver variants. For coding mutations, we developed a radial support vector machine using manually curated mutations to predict their driver potential. The second layer identifies driver genes, by using information from the first layer and integrating prior biological knowledge on gene-gene and gene-phenotype networks. The third layer prioritizes personalized drug treatment, by classifying potential driver genes into different categories and querying drug-gene databases. Compared to currently available tools, iCAGES achieves better performance by correctly classifying point coding driver mutations (AUC=0.97, 95% CI: 0.97-0.97, significantly better than the second best tool with P=0.01) and genes (AUC=0.93, 95% CI: 0.93-0.94, significantly better than MutSigCV with P<1×10−15). We also illustrated two examples where iCAGES correctly nominated two targeted drugs for two advanced cancer patients with exceptional response, based on their somatic mutation profiles. iCAGES leverages personal genomic information and prior biological knowledge, effectively identifies cancer driver genes and predicts treatment strategies. iCAGES is available athttp://icages.usc.edu.
Relationship between tumor grade and geometrical complexity in prostate cancer
Authors: Llanos-Pérez J.A. , Tejera-Puente E. , Izquierdo-Kulich E. , Betancourt Mar J.A. , Nistal M. , González-Peramato P. , Royuela-García M. , Nieto-Villar J.M. , De Miguel M. P. 
Published: February 2015

AbstractProstate cancer exhibits high mathematical complexity due to the disruption of tissue architecture. An important part of the diagnostic of prostate tumor samples is the histological evaluation of cellular and glandular organization. The Gleason grade and score, a commonly used prognostic indicator of patient outcome, is based on the match of glandular architectural patterns with standard patterns. Unfortunately, the subjective nature of visual grading leads to variations in scoring by different pathologists. We proposed the fractal dimension of the lumen and the Lempel-Zip complexity of the histopathological patterns as useful descriptors aiding pathologist to standardize histological classification and thus prognosis and therapy planning.Highlightsgeometrical complexity of prostate cancer
Decoding of human hand actions to handle missing limbs in Neuroprosthetics
Authors: Belić Jovana J. , Faisal Aldo A. 
Published: February 2015

AbstractThe only way we can interact with the world is through movements, and our primary interactions are via the hands, thus any loss of hand function has immediate impact on our quality of life. However, to date it has not been systematically assessed how coordination in the hand’s joints affects every day actions. This is important for two fundamental reasons. Firstly, to understand the representations and computations underlying motor control “in-the-wild” situations, and secondly to develop smarter controllers for prosthetic hands that have the same functionality as natural limbs. In this work we exploit the correlation structure of our hand and finger movements in daily-life. The novelty of our idea is that instead of averaging variability out, we take the view that the structure of variability may contain valuable information about the task being performed. We asked seven subjects to interact in 17 daily-life situations, and quantified behaviour in a principled manner using CyberGlove body sensor networks that, after accurate calibration, track all major joints of the hand. Our key findings are: 1. We confirmed that hand control in daily-life tasks is very low-dimensional, with four to five dimensions being sufficient to explain 80-90% of the variability in the natural movement data. 2. We established a universally applicable measure of manipulative complexity that allowed us to measure and compare limb movements across tasks. We used Bayesian latent variable models to model the low-dimensional structure of finger joint angles in natural actions. 3. This allowed us to build a naïve classifier that within the first 1000ms of action initiation (from a flat hand start configuration) predicted which of the 17 actions was going to be executed - enabling us to reliably predict the action intention from very short-time-scale initial data, further revealing the foreseeable nature of hand movements for control of neuroprosthetics and tele operation purposes. 4. Using the Expectation-Maximization algorithm on our latent variable model permitted us to reconstruct with high accuracy (<5°-6°MAE) the movement trajectory of missing fingers by simply tracking the remaining fingers. Overall, our results suggest the hypothesis that specific hand actions are orchestrated by the brain in such a way that in the natural tasks of daily-life there is sufficient redundancy and predictability to be directly exploitable for neuroprosthetics.