bluuebunny/crossref_metadata_2025_split huggingface dataset:
MetaMap: An atlas of metatranscriptomic reads in human disease-related RNA-seq data
Authors: Simon LM , Karg S , Westermann AJ , Engel M , Elbehery AHA , Hense B , Heinig M , Deng L , Theis FJ 
Published: February 2018

AbstractBackgroundWith the advent of the age of big data in bioinformatics, large volumes of data and high performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts, but its generic nature also enables the detection of microbial and viral transcripts.FindingsWe developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-human-mapping read fraction. We validated this approach by recapitulating outcomes from 6 independent controlled infection experiments of cell line models and comparison with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from >17,000 samples from >400 studies relevant to human disease using state-of-the-art high performance computing systems. The resulting data of this large-scale re-analysis are made available in the presented MetaMap resource.ConclusionsOur results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation towards the role of the microbiome in human disease.
The application of text mining algorithms in summarizing trends in anti-epileptic drug research
Authors: Singh Shatrunjai P. , Karkare Swagata , Baswan Sudhir M. , Singh Vijendra P. 
Published: February 2018

1.AbstractContent summarization is an important area of research in traditional data mining. The volume of studies published on anti-epileptic drugs (AED) has increased exponentially over the last two decades, making it an important area for the application of text mining based summarization algorithms. In the current study, we use text analytics algorithms to mine and summarize 10,000 PubMed abstracts related to anti-epileptic drugs published within the last 10 years. A Text Frequency – Inverse Document Frequency based filtering was applied to identify drugs with highest frequency of mentions within these abstracts. The US Food and Drug database was scrapped and linked to the results to quantify the most frequently mentioned modes of action and elucidate the pharmaceutical entities marketing these drugs. A sentiment analysis model was created to score the abstracts for sentiment positivity or negativity. Finally, a modified Latent Dirichlet Allocation topic model was generated to extract key topics associated with the most frequently mentioned AEDs. Results of this study provide accurate and data intensive insights on the progress of anti-epileptic drug research.
Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects
Authors: Regier Allison A. , Farjoun Yossi , Larson David , Krasheninina Olga , Kang Hyun Min , Howrigan Daniel P. , Chen Bo-Juen , Kher Manisha , Banks Eric , Ames Darren C. , English Adam C. , Li Heng , Xing Jinchuan , Zhang Yeting , Matise Tara ,   the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, Abecasis Goncalo R. , Salerno Will , Zody Michael C. , Neale Benjamin M. , Hall Ira M. 
Published: February 2018

AbstractHundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years to interrogate a broad range of traits, across diverse populations. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power for trait mapping, and will enable studies of genome biology, population genetics and genome function at unprecedented scale. A central challenge for joint analysis is that different WGS data processing and analysis pipelines cause substantial batch effects in combined datasets, necessitating computationally expensive reprocessing and harmonization prior to variant calling. This approach is no longer tenable given the scale of current studies and data volumes. Here, in a collaboration across multiple genome centers and NIH programs, we define WGS data processing standards that allow different groups to produce “functionally equivalent” (FE) results suitable for joint variant calling with minimal batch effects. Our approach promotes broad harmonization of upstream data processing steps, while allowing for diverse variant callers. Importantly, it allows each group to continue innovating on data processing pipelines, as long as results remain compatible. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results – including single nucleotide (SNV), insertion/deletion (indel) and structural variation (SV) – and produce significantly less variability than sequencing replicates. Residual inter-pipeline variability is concentrated at low quality sites and repetitive genomic regions prone to stochastic effects. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for broad data sharing and community-wide “big-data” human genetics studies.
Integration and multiplexing of positional and contextual information by the hippocampal network
Authors: Posani Lorenzo , Cocco Simona , Monasson Rémi 
Published: February 2018

AbstractThe hippocampus is known to store cognitive representations, or maps, that encode both positional and contextual information, critical for episodic memories and functional behavior. How path integration and contextual cues are dynamically combined and processed by the hippocampus to maintain these representations accurate over time remains unclear. To answer this question, we propose a two-way data analysis and modeling approach to CA3 multi-electrode recordings of a moving rat submitted to rapid changes of contextual (light) cues, triggering back-and-forth instabitilies between two cognitive representations (Jezek et al, Nature 478, p 246 (2011)). We develop a dual neural activity decoder, capable of independently identifying the recalled cognitive map at high temporal resolution (comparable to theta cycle) and the position of the rodent given a map. Remarkably, position can be reconstructed at any time with an accuracy comparable to fixed-context periods, even during highly unstable periods. These findings provide evidence for the capability of the hippocampal neural activity to maintain an accurate encoding of spatial and contextual variables, while one of these variables undergoes rapid changes independently of the other. To explain this result we introduce an attractor neural network model for the hippocampal activity that process inputs from external cues and the path integrator. Our model allows us to make predictions on the frequency of the cognitive map instability, its duration, and the detailed nature of the place-cell population activity, which are validated by a further analysis of the data. Our work therefore sheds light on the mechanisms by which the hippocampal network achieves and updates multi-dimensional neural representations from various input streams.Author summaryAs an animal moves in space and receives external sensory inputs, it must dynamically maintain the representations of its position and environment at all times. How the hippocampus, the brain area crucial for spatial representations, achieves this task, and manages possible conflicts between different inputs remains unclear. We propose here a comprehensive attractor neural network-based model of the hippocampus and of its multiple input streams (including self-motion). We show that this model is capable of maintaining faithful representations of positional and contextual information, and resolves conflicts by adapting internal representations to match external cues. Model predictions are confirmed by the detailed analysis of hippocampal recordings of a rat submitted to quickly varying and conflicting contextual inputs.
Bioscience-scale automated detection of figure element reuse
Authors: Acuna Daniel E. , Brookes Paul S. , Kording Konrad P. 
Published: February 2018

AbstractScientists reuse figure elements sometimes appropriately, e.g. when comparing methods, and sometimes inappropriately, e.g. when presenting an old experiment as a new control. To understand such reuse, automatically detecting it would be important. Here we present an analysis of figure element reuse on a large dataset comprising 760 thousand open access articles and 2 million figures. Our algorithm detects figure region reuse, while being robust to rotation, cropping, resizing, and contrast changes, and estimates which of the reuses have biological meaning. Then a three-person panel analyzes how problematic these biological reuses are using contextual information such as captions and full texts. Based on the panel reviews, we estimate that 9% of the biological reuses would be unanimously perceived as at least suspicious. We further estimate that 0.6% of all articles would be unanimously perceived as fraudulent, with inappropriate reuses occurring 43% across articles, 28% within article, and 29% within a figure. Our tool rapidly detects image reuse at scale, promising to be useful to a broad range of people that campaign for scientific integrity. We suggest that a great deal of scientific fraud will be, sooner or later, detectable by automatic methods.
SOX2 Regulation by Hedgehog Signaling Controls Adult Lingual Epithelium Homeostasis
Authors: Castillo-Azofeifa David , Seidel Kerstin , Gross Lauren , Jacquez Belkis , Klein Ophir D. , Barlow Linda A. 
Published: February 2018

AbstractThe adult tongue epithelium is continuously renewed from epithelial progenitor cells, and this process relies on intact Hedgehog (HH) signaling. In mice, inhibition of the HH pathway using Smoothened antagonists (HH pathway inhibitors or HPIs) leads to taste bud loss over a span of several weeks. Previously, we demonstrated that overexpression of Sonic Hedgehog (SHH) in lingual epithelial progenitors induces formation of ectopic taste buds accompanied by locally increased SOX2 expression, consistent with the hypothesis that taste bud differentiation depends on SOX2 downstream of HH. To test this idea, we inhibited HH signaling by treating SOX2-GFP mice with HPI and found a rapid and drastic decline in SOX2-GFP expression in taste progenitors and taste buds. Using a conditional Cre-lox system to deleteSox2, we found that loss of SOX2 blocks differentiation of both taste buds and non-taste epithelium that comprises the majority of the tongue surface; progenitor cells increase in number at the expense of differentiated taste cells and lingual keratinocytes. In contrast to the normal pattern of basally restricted proliferation, dividing cells are overabundant, disorganized and present in suprabasal epithelial layers inSox2deleted tongues. Additionally, SOX2 loss in taste progenitors leads non-cell autonomously to rapid loss of taste bud cells via apoptosis, dramatically shortening taste cell lifespans. Finally, whenSox2is conditionally deleted in mice with constitutive overexpression of SHH, ectopic taste buds fail to form and endogenous taste buds disappear; instead, robust hyperproliferation takes over the entire lingual epithelium. In sum, our experiments suggest that SOX2 functions downstream of HH signaling to regulate lingual epithelium homeostasis.
Iso-Seq allows genome-independent transcriptome profiling of grape berry development
Authors: Minio Andrea , Massonnet Mélanie , Figueroa-Balderas Rosa , Vondras Amanda M. , Blanco-Ulate Barbara , Cantu Dario 
Published: February 2018

AbstractTranscriptomics has been widely applied to study grape berry development. With few exceptions, transcriptomic studies in grape are performed using the available genome sequence, PN40024, as reference. However, differences in gene content among grape accessions, which contribute to phenotypic differences among cultivars, suggest that a single reference genome does not represent the species’ entire gene space. Though whole genome assembly and annotation can reveal the relatively unique or “private” gene space of any particular cultivar, transcriptome reconstruction is a more rapid, less costly, and less computationally intensive strategy to accomplish the same goal. In this study, we used single molecule-real time sequencing (Iso-Seq) to sequence full-length cDNA and reconstruct the transcriptome of Cabernet Sauvignon berries during berry ripening. In addition, Illumina short reads from ripening berries were used to error-correct low-expression isoforms and to profile isoform expression. By comparing the annotated gene space of Cabernet Sauvignon to other grape cultivars, we demonstrate that the transcriptome reference built with Iso-Seq data represents most of the expressed genes in the grape berries and includes 1,501 cultivar-specific genes. Iso-Seq produced transcriptome profiles similar to those obtained after mapping on a complete genome reference. Together, these results justify the application of Iso-Seq to identify cultivar-specific genes and build a comprehensive reference for transcriptional profiling that circumvents the necessity of a genome reference with its associated costs and computational weight.
Principal component based adaptive association test of multiple traits using GWAS summary statistics
Authors: Guo Bin , Wu Baolin 
Published: February 2018

AbstractGenetics hold great promise to precision medicine by tailoring treatment to the individual patient based on their genetic profiles. Toward this goal, many large-scale genome-wide association studies (GWAS) have been performed in the last decade to identify genetic variants associated with various traits and diseases. They have successfully identified tens of thousands of disease-related variants. However they have explained only a small proportion of the overall trait heritability for most traits and are of very limited clinical use. This is partly owing to the small effect sizes of most genetic variants, and the common practice of “testing association between one trait and one genetic variant at a time” in most GWAS, even when multiple related traits are often measured for each individual. Increasing evidence suggests that many genetic variants can influence multiple traits simultaneously, and we can gain more power by testing association of multiple traits simultaneously. It is appealing to develop novel multi-trait association test methods that need only GWAS summary data, since it is generally very hard to access the individual-level GWAS phenotype and genotype data.Most existing GWAS summary data based association test methods have relied on ad hoc approach or crude Monte Carlo approximation. In this paper we develop rigorous statistical methods for efficient and powerful multi-trait association test. We develop robust and efficient methods to accurately estimate the marginal trait correlation matrix using only GWAS summary data. We construct the principal component (PC) based association test from the summary statistics. PC based test has optimal power when the underlying multi-trait signal can be captured by the first PC, and otherwise it will have suboptimal performance. We develop an adaptive test by optimally weighting the PC based test and the omnibus chi-square test to achieve robust performance under various scenarios. We develop efficient numerical algorithms to compute the analytical p-values for all the proposed tests without the need of Monte Carlo sampling. We illustrate the utility of proposed methods through application to the GWAS meta-analysis summary data for multiple lipids and glycemic traits. We identify multiple novel loci that were missed by individual trait based association test.All the proposed methods are implemented in an R package available athttp://www.github.com/baolinwu/MTAR. The developed R programs are extremely efficient: it takes less than two minutes to compute the list of genome-wide significant SNPs for all proposed multi-trait tests for the lipids GWAS summary data with 2.5 million SNPs on a single Linux desktop.
Structural Capacitance in Protein Evolution and Human Diseases
Authors: Li Chen , Clark Liah V. T. , Zhang Rory , Porebski Benjamin T. , McCoey Julia M. , Borg Natalie A. , Webb Geoffrey I. , Kass Itamar , Buckle Malcolm , Song Jiangning , Woolfson Adrian , Buckle Ashley M. 
Published: February 2018

AbstractCanonical mechanisms of protein evolution include the duplication and diversification of pre-existing folds through genetic alterations that include point mutations, insertions, deletions, and copy number amplifications, as well as post-translational modifications that modify processes such as folding efficiency and cellular localization. Following a survey of the human mutation database, we have identified an additional mechanism, that we term ‘structural capacitance’, which results in thede novogeneration of microstructure in previously disordered regions. We suggest that the potential for structural capacitance confers select proteins with the capacity to evolve over rapid timescales, facilitating saltatory evolution as opoposed to exclusively canonical Darwinian mechanisms. Our results implicate the elements of protein microstructure generated by this distinct mechanism in the pathogenesis of a wide variety of human diseases. The benefits of rapidly furnishing the potential for evolutionary change conferred by structural capacitance are consequently counterbalanced by this accompanying risk, with the extent of this determined by the host immune system. The phenomenon of structural capacitance has implications ranging from the ancestral diversification of protein folds to the engineering of synthetic proteins with enhanced evolvability.
Comparing EEG/MEG neuroimaging methods based on localization error, false positive activity, and false positive connectivity
Authors: Pascual-Marqui Roberto D. , Faber Pascal , Kinoshita Toshihiko , Kochi Kieko , Milz Patricia , Nishida Keiichiro , Yoshimura Masafumi 
Published: February 2018

1.AbstractEEG/MEG neuroimaging consists of estimating the cortical distribution of time varying signals of electric neuronal activity, for the study of functional localization and connectivity. Currently, many different imaging methods are being used, with very different capabilities of correct localization of activity and of correct localization of connectivity. The aim here is to provide a guideline for choosing the best (i.e. least bad) imaging method. This first study is limited to the comparison of the following methods for EEG signals: sLORETA and eLORETA (standardized and exact low resolution electromagnetic tomography), MNE (minimum norm estimate), dSPM (dynamic statistical parametric mapping), and LCMVBs (linearly constrained minimum variance beamformers). These methods are linear, except for the LCMVBs that make use of the quadratic EEG covariances. To achieve a fair comparison, it is assumed here that the generators are independent and widely distributed (i.e. not few in number), giving a well-defined theoretical population EEG covariance matrix for use with the LCMVBs. Measures of localization error, false positive activity, and false positive connectivity are defined and computed under ideal no-noise conditions. It is empirically shown with extensive simulations that: (1) MNE, dSPM, and all LCMVBs are in general incapable of correct localization, while sLORETA and eLORETA have exact (zero-error) localization; (2) the brain volume with false positive activity is significantly larger for MN, dSPM, and all LCMVBs, as compared to sLORETA and eLORETA; and (3) the number of false positive connections is significantly larger for MN, dSPM, all LCMVBs, and sLORETA, as compared to eLORETA. Non-vague and fully detailed equations are given. PASCAL program codes and data files are available. It is noted that the results reported here do not apply to the LCMVBs based on EEG covariance matrices generated from extremely few generators, such as only one or two independent point sources.
Intron-mediated enhancement is not limited to introns
Authors: Gallegos Jenna E , Rose Alan B 
Published: February 2018

AbstractCertain introns strongly increase mRNA accumulation by a poorly understood mechanism known as Intron-Mediated Enhancement (IME). Introns that boost expression by IME have no effect when located upstream of or more than ~1 Kb downstream from the start of transcription. The sequence TTNGATYTG, which is over-represented in promoter-proximal introns in Arabidopsis thaliana, can convert a non-stimulating intron into one that strongly increases mRNA accumulation. We tested the ability of an intron containing this motif to stimulate expression from different locations and found that it had the same positional requirements as naturally occurring IME introns. The motif also stimulated gene expression from within the 5’-UTR and coding sequences of an intronless construct. Furthermore, the 5’-UTR of another gene increased expression when inserted into an otherwise non-stimulating intron in coding sequences. These results demonstrate that splicing is not required for intron-mediated enhancement, and suggest that other sequences downstream of the transcription start site in addition to introns may stimulate expression by a similar mechanism.
Programmable single and multiplex base-editing in Bombyx mori using RNA-guided cytidine deaminases
Authors: Li Yufeng , Ma Sanyuan , Sun Le , Zhang Tong , Chang Jiasong , Lu Wei , Chen Xiaoxu , Liu Yue , Wang Xiaogang , Shi Run , Zhao Ping , Xia Qingyou 
Published: February 2018

ABSTRACTStandard genome editing tools (ZFN, TALEN and CRISPR/Cas9) edited genome depending on DNA double strand breaks (DSBs). A series of new CRISPR tools that convert cytidine to thymine (C to T) without the requirement for DNA double-strand breaks were developed recently, which have changed this status and have been quickly applied in a variety of organisms. Here, we demonstrate that CRISPR/Cas9-dependent base editor (BE3) converts C to T with a high frequency in the invertebrate Bombyx mori silkworm. Using BE3 as a knock-out tool, we inactivated exogenous and endogenous genes through base-editing-induced nonsense mutations with an efficiency of up to 66.2%. Furthermore, genome-scale analysis showed that 96.5% of B. mori genes have one or more targetable sites being knocked out by BE3 with a median of 11 sites per gene. The editing window of BE3 reached up to 13 bases (from C1 to C13 in the range of gRNA) in B. mori. Notably, up to 14 bases were substituted simultaneously in a single DNA molecule, with a low indel frequency of 0.6%, when 32 gRNAs were co-transfected. Collectively, our data show for the first time that RNA-guided cytidine deaminases are capable of programmable single and multiplex base-editing in an invertebrate model.