Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 62,232 bioRxiv papers from 276,288 authors.
Most downloaded bioRxiv papers, since beginning of last month
49,529 results found. For more information, click each entry to expand.
759 downloads genomics
Ansuman T. Satpathy, Jeffrey M. Granja, Kathryn E Yost, Yanyan Qi, Francesca Meschi, Geoffrey P McDermott, Brett N Olsen, Maxwell R. Mumbach, Sarah E Pierce, M. Ryan Corces, Preyas Shah, Jason C. Bell, Darisha Jhutty, Corey M Nemec, Jean Wang, Li Wang, Yifeng Yin, Paul G Giresi, Anne Lynn S. Chang, Grace X.Y. Zheng, William J. Greenleaf, Howard Y. Chang
Understanding complex tissues requires single-cell deconstruction of gene regulation with precision and scale. Here we present a massively parallel droplet-based platform for mapping transposase-accessible chromatin in tens of thousands of single cells per sample (scATAC-seq). We obtain and analyze chromatin profiles of over 200,000 single cells in two primary human systems. In blood, scATAC-seq allows marker-free identification of cell type-specific cis- and trans-regulatory elements, mapping of disease-associated enhancer activity, and reconstruction of trajectories of differentiation from progenitors to diverse and rare immune cell types. In basal cell carcinoma, scATAC-seq reveals regulatory landscapes of malignant, stromal, and immune cell types in the tumor microenvironment. Moreover, scATAC-seq of serial tumor biopsies before and after PD-1 blockade allows identification of chromatin regulators and differentiation trajectories of therapy-responsive intratumoral T cell subsets, revealing a shared regulatory program driving CD8+ T cell exhaustion and CD4+ T follicular helper cell development. We anticipate that droplet-based single-cell chromatin accessibility will provide a broadly applicable means of identifying regulatory factors and elements that underlie cell type and function.
754 downloads neuroscience
Pattern recognition predictive models have become an important tool for analysis of neuroimaging data and answering important questions from clinical and cognitive neuroscience. Regardless of the application, the most commonly used method to quantify model performance is to calculate prediction accuracy, i.e. the proportion of correctly classified samples. While simple and intuitive, other performance measures are often more appropriate with respect to many common goals of neuroimaging pattern recognition studies. In this paper, we will review alternative performance measures and focus on their interpretation and practical aspects of model evaluation. Specifically, we will focus on 4 families of performance measures: 1) categorical performance measures such as accuracy, 2) rank based performance measures such as the area under the curve, 3) probabilistic performance measures based on quadratic error such as Brier score, and 4) probabilistic performance measures based on information criteria such as logarithmic score. We will examine their statistical properties in various settings using simulated data and real neuroimaging data derived from public datasets. Results showed that accuracy had the worst performance with respect to statistical power, detecting model improvement, selecting informative features and reliability of results. Therefore in most cases, it should not be used to make statistical inference about model performance. Accuracy should also be avoided for evaluating utility of clinical models, because it does not take into account clinically relevant information, such as relative cost of false-positive and false-negative misclassification or calibration of probabilistic predictions. We recommend alternative evaluation criteria with respect to the goals of a specific machine learning model.
740 downloads bioinformatics
Karen H Miga, Sergey Koren, Arang Rhie, Mitchell R. Vollger, Ariel Gershman, Andrey Bzikadze, Shelise Brooks, Edmund Howe, David Porubsky, Glennis A. Logsdon, Valerie A Schneider, Tamara Potapova, Jonathan Wood, William Chow, Joel Armstrong, Jeanne Fredrickson, Evgenia Pak, Kristof Tigyi, Milinn Kremitzki, Christopher Markovic, Valerie Maduro, Amalia Dutra, Gerard G Bouffard, Alexander M Chang, Nancy F Hansen, Françoisen Thibaud-Nissen, Anthony D Schmitt, Jon-Matthew Belton, Siddarth Selvaraj, Megan Y. Dennis, Daniela C Soto, Ruta Sahasrabudhe, Gulhan Kaya, Josh Quick, Nicholas J Loman, Nadine Holmes, Matthew Loose, Urvashi Surti, Rosa ana Risques, Tina A. Graves Lindsay, Robert Fulton, Ira Hall, Benedict Paten, Kerstin Howe, Winston Timp, Alice Young, James C. Mullikin, Pavel A Pevzner, Jennifer E. Gerton, Beth A Sullivan, Evan E Eichler, Adam M Phillippy
After nearly two decades of improvements, the current human reference genome (GRCh38) is the most accurate and complete vertebrate genome ever produced. However, no one chromosome has been finished end to end, and hundreds of unresolved gaps persist ,. The remaining gaps include ribosomal rDNA arrays, large near-identical segmental duplications, and satellite DNA arrays. These regions harbor largely unexplored variation of unknown consequence, and their absence from the current reference genome can lead to experimental artifacts and hide true variants when re-sequencing additional human genomes. Here we present a de novo human genome assembly that surpasses the continuity of GRCh38 , along with the first gapless, telomere-to-telomere assembly of a human chromosome. This was enabled by high-coverage, ultra-long-read nanopore sequencing of the complete hydatidiform mole CHM13 genome, combined with complementary technologies for quality improvement and validation. Focusing our efforts on the human X chromosome , we reconstructed the ∼2.8 megabase centromeric satellite DNA array and closed all 29 remaining gaps in the current reference, including new sequence from the human pseudoautosomal regions and cancer-testis ampliconic gene families (CT-X and GAGE). This complete chromosome X, combined with the ultra-long nanopore data, also allowed us to map methylation patterns across complex tandem repeats and satellite arrays for the first time. These results demonstrate that finishing the human genome is now within reach and will enable ongoing efforts to complete the remaining human chromosomes. : #ref-1 : #ref-2 : #ref-3
732 downloads neuroscience
Johan Winnubst, Erhan Bas, Tiago A. Ferreira, Zhuhao Wu, Michael N Economo, Patrick Edson, Ben J. Arthur, Christopher Bruns, Konrad Rokicki, David Schauder, Donald J. Olbris, Sean D. Murphy, David G. Ackerman, Cameron Arshadi, Perry Baldwin, Regina Blake, Ahmad Elsayed, Mashtura Hasan, Daniel Ramirez, Bruno Dos Santos, Monet Weldon, Amina Zafar, Joshua T. Dudmann, Charles R Gerfen, Adam W Hantman, Wyatt Korff, Scott M. Sternson, Nelson Spruston, Karel Svoboda, Jayaram Chandrashekar
Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons comprise more than 75 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.
729 downloads neuroscience
Deep convolutional neural networks have emerged as the state of the art for predicting single-unit responses in a number of visual areas. While such models outperform classical linear-nonlinear and wavelet-based feature representations, we currently do not know what additional nonlinear computations they approximate. Divisive normalization (DN) has been suggested as one such nonlinear, canonical cortical computation, which has been found to be crucial for explaining nonlinear responses to combinations of simple stimuli such as gratings. However, it has neither been tested rigorously for its ability to account for spiking responses to natural images nor do we know to what extent it can close the gap to high-performing black-box models. Here, we developed an end-to-end trainable model of DN that learns the pool of normalizing neurons and the magnitude of their contribution directly from the data. We used this model to investigate DN in monkey primary visual cortex (V1) under stimulation with natural images. We found that this model outperformed linear-nonlinear and wavelet-based feature representations and came close to the performance of deep neural networks. Surprisingly, within the classical receptive field, oriented features were normalized preferentially by features with similar orientation preference rather than non-specifically as assumed by current models of DN. Thus, our work provides a new, quantitative and interpretable predictive model of V1 applicable to arbitrary images and refines our view on the mechanisms of gain control within the classical receptive field.
718 downloads neuroscience
Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a deep neural network using limited training data to precisely track user-defined features that matches human labeling accuracy. Here, with this paper we provide an updated toolbox that is self contained within a Python package that includes new features such as graphical user interfaces and active-learning based network refinement. Lastly, we provide a step-by-step guide for using DeepLabCut.
706 downloads genetics
Ashot Margaryan, Daniel Lawson, Martin Sikora, Fernando Racimo, Simon Rasmussen, Ida Moltke, Lara Cassidy, Emil Jørsboe, Andrés Ingason, Mikkel Pedersen, Thorfinn Korneliussen, Helene Wilhelmson, Magdalena Buś, Peter de Barros Damgaard, Rui Martiniano, Gabriel Renaud, Claude Bhérer, J. Víctor Moreno-Mayar, Anna Fotakis, Marie Allen, Martyna Molak, Enrico Cappellini, Gabriele Scorrano, Alexandra Buzhilova, Allison Fox, Anders Albrechtsen, Berit Schütz, Birgitte Skar, Caroline Arcini, Ceri Falys, Charlotte Hedenstierna Jonson, Dariusz Błaszczyk, Denis Pezhemsky, Gordon Turner-Walker, Hildur Gestsdóttir, Inge Lundstrøm, Ingrid Gustin, Ingrid Mainland, Inna Potekhina, Italo Muntoni, Jade Cheng, Jesper Stenderup, Jilong Ma, Julie Gibson, Jüri Peets, Jörgen Gustafsson, Katrine Iversen, Linzi Simpson, Lisa Strand, Louise Loe, Maeve Sikora, Marek Florek, Maria Vretemark, Mark Redknap, Monika Bajka, Tamara Pushkina, Morten Søvsø, Natalia Grigoreva, Tom Christensen, Ole Kastholm, Otto Uldum, Pasquale Favia, Per Holck, Raili Allmäe, Sabine Sten, Símun Arge, Sturla Ellingvåg, Vayacheslav Moiseyev, Wiesław Bogdanowicz, Yvonne Magnusson, Ludovic Orlando, Daniel Bradley, Marie Louise Jørkov, Jette Arneborg, Niels Lynnerup, Neil Price, M. Thomas Gilbert, Morten Allentoft, Jan Bill, Søren Sindbæk, Lotte Hedeager, Kristian Kristiansen, Rasmus Nielsen, Thomas Werge, Eske Willerslev
The Viking maritime expansion from Scandinavia (Denmark, Norway, and Sweden) marks one of the swiftest and most far-flung cultural transformations in global history. During this time (c. 750 to 1050 CE), the Vikings reached most of western Eurasia, Greenland, and North America, and left a cultural legacy that persists till today. To understand the genetic structure and influence of the Viking expansion, we sequenced the genomes of 442 ancient humans from across Europe and Greenland ranging from the Bronze Age (c. 2400 BC) to the early Modern period (c. 1600 CE), with particular emphasis on the Viking Age. We find that the period preceding the Viking Age was accompanied by foreign gene flow into Scandinavia from the south and east: spreading from Denmark and eastern Sweden to the rest of Scandinavia. Despite the close linguistic similarities of modern Scandinavian languages, we observe genetic structure within Scandinavia, suggesting that regional population differences were already present 1,000 years ago. We find evidence for a majority of Danish Viking presence in England, Swedish Viking presence in the Baltic, and Norwegian Viking presence in Ireland, Iceland, and Greenland. Additionally, we see substantial foreign European ancestry entering Scandinavia during the Viking Age. We also find that several of the members of the only archaeologically well-attested Viking expedition were close family members. By comparing Viking Scandinavian genomes with present-day Scandinavian genomes, we find that pigmentation-associated loci have undergone strong population differentiation during the last millennia. Finally, we are able to trace the allele frequency dynamics of positively selected loci with unprecedented detail, including the lactase persistence allele and various alleles associated with the immune response. We conclude that the Viking diaspora was characterized by substantial foreign engagement: distinct Viking populations influenced the genomic makeup of different regions of Europe, while Scandinavia also experienced increased contact with the rest of the continent.
693 downloads developmental biology
One of the earliest and most significant events in embryonic development is zygotic genome activation (ZGA). In several species, bulk transcription begins at the mid-blastula transition (MBT) when, after a certain number of cleavages, the embryo attains a particular nuclear-to-cytoplasmic (N/C) ratio, maternal repressors become sufficiently diluted, and the cell cycle slows down. Here we resolve the frog ZGA in time and space by profiling RNA polymerase II (RNAPII) engagement and its transcriptional readout. We detect a gradual increase in both the quantity and the length of RNAPII elongation before the MBT, revealing that >1,000 zygotic genes disregard the N/C timer for their activation, and that the sizes of newly transcribed genes are not necessarily constrained by cell cycle duration. We also find that Wnt, Nodal and BMP signaling together generate most of the spatio-temporal dynamics of regional ZGA, directing the formation of orthogonal body axes and proportionate germ layers.
691 downloads neuroscience
One of the main ways we interact with the world is using our hands. In macaques, the circuit formed by the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. We hypothesized that a recurrent neural network mimicking the multi-area structure of the anatomical circuit and trained to transform visual features into the muscle fiber velocity required to grasp objects would recapitulate neural data in the macaque grasping circuit. While a number of network architectures produced the required kinematics, modular networks with visual input and activity that was encouraged to be biologically realistic best matched neural data and the inter-area differences present in the biological circuit. Network dynamics could be explained by simple rules that also allowed the correct prediction of kinematics and neural responses to novel objects, providing a potential mechanism for flexibly generating grasping movements.
687 downloads developmental biology
Size trade-offs of visual versus olfactory organs is a pervasive feature of animal evolution. Comparing Drosophila species, we find that larger eyes correlate with smaller antennae, where olfactory organs reside, and narrower faces. We demonstrate that this trade-off arises through differential subdivision of the head primordium into visual versus non-visual fields. Specification of the visual field requires a highly-conserved eye development gene called eyeless in flies and Pax6 in humans. We discover that changes in the temporal regulation of eyeless expression during development is a conserved mechanism for sensory trade-offs within and between Drosophila species. We identify a natural single nucleotide polymorphism in the cis-regulatory region of eyeless that is sufficient to alter its temporal regulation and eye size. Because Pax6 is a conserved regulator of sensory placode subdivision, we propose that alterations in the mutual repression between sensory territories is a conserved mechanism for sensory trade-offs in animals.
674 downloads bioinformatics
The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not been established, yet. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ∼ 3,000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.
663 downloads bioinformatics
Single cell RNA sequencing (scRNA-seq) is widely used for profiling transcriptomes of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently-used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data by the two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analysis. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data better. For 10X-based data, we observed higher noise for mRNA in the low expression level. Despite the poly(A) enrichment, approximately 10-30% of all detected transcripts by both platforms were from non-coding genes, with lncRNA accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can better detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected different sets of differentially expressed genes between cell clusters, indicating the complementary nature of these technologies. Our comprehensive benchmark analysis offers the basis for selecting the optimal scRNA-seq strategy based on the objectives of each study.
653 downloads neuroscience
The correct subcellular distribution of protein complexes establishes the complex morphology of neurons and is fundamental to their functioning. Thus, determining the dynamic distribution of proteins is essential to understand neuronal processes. Fluorescence imaging, in particular super-resolution microscopy, has become invaluable to investigate subcellular protein distribution. However, these approaches suffer from the limited ability to efficiently and reliably label endogenous proteins. We developed ORANGE: an Open Resource for the Application of Neuronal Genome Editing, that mediates targeted genomic integration of fluorescent tags in neurons. This toolbox includes a knock-in library for in-depth investigation of endogenous protein distribution, and a detailed protocol explaining how knock-in can be developed for novel targets. In combination with super-resolution microscopy, ORANGE revealed the dynamic nanoscale organization of endogenous neuronal signaling molecules, synaptic scaffolding proteins, and neurotransmitter receptors. Thus, ORANGE enables quantitation of expression and distribution for virtually any protein in neurons at high resolution and will significantly further our understanding of neuronal cell biology.
644 downloads synthetic biology
Engineering mammalian cells to carry out sophisticated and customizable genetic programs requires a toolkit of multiple orthogonal and well-characterized transcription factors (TFs). To address this need, we developed the COmposable Mammalian Elements of Transcription (COMET): an ensemble of TFs and promoters that enable the design and tuning of gene expression to an extent not previously possible. COMET currently comprises 44 activating and 12 inhibitory zinc-finger TFs and 83 cognate promoters, combined in a framework that readily accommodates new parts. This system can tune gene expression over three orders of magnitude, provides chemically inducible control of TF activity, and enables single-layer Boolean logic. We also develop a mathematical model that provides mechanistic insights into COMET performance characteristics. Altogether, COMET enables the design and construction of customizable genetic programs in mammalian cells.
641 downloads immunology
Prakash Ramachandran, Ross Dobie, John R Wilson-Kanamori, Elena F Dora, Beth EP Henderson, Richard S Taylor, Kylie P Matchett, Jordan R Portman, Mirjana Efremova, Roser Vento-Tormo, Nguyet T Luu, Christopher J Weston, Philip N Newsome, Ewen M. Harrison, Damian James Mole, Steve J Wigmore, John P Iredale, Frank Tacke, Jeffrey W Pollard, Chris Ponting, John Marioni, Sarah A Teichmann, Neil C. Henderson
Currently there are no effective antifibrotic therapies for liver cirrhosis, a major killer worldwide. To obtain a cellular resolution of directly-relevant pathogenesis and to inform therapeutic design, we profile the transcriptomes of over 100,000 primary human single cells, yielding molecular definitions for the major non-parenchymal cell types present in healthy and cirrhotic human liver. We uncover a novel scar-associated TREM2+CD9+ macrophage subpopulation with a fibrogenic phenotype, that has a distinct differentiation trajectory from circulating monocytes. In the endothelial compartment, we show that newly-defined ACKR1+ and PLVAP+ endothelial cells expand in cirrhosis and are topographically located in the fibrotic septae. Multi-lineage ligand-receptor modelling of specific interactions between the novel scar-associated macrophages, endothelial cells and collagen-producing myofibroblasts in the fibrotic niche, reveals intra-scar activity of several major pathways which promote hepatic fibrosis. Our work dissects unanticipated aspects of the cellular and molecular basis of human organ fibrosis at a single-cell level, and provides the conceptual framework required to discover rational therapeutic targets in liver cirrhosis.
638 downloads immunology
Zhaoyuan Liu, Yaqi Gu, Svetoslav Chakarov, Camille Bleriot, Xin Chen, Amanda Shin, Weijie Huang, Regine J. Dress, Charles-Antoine Dutertre, Andreas Schlitzer, Jinmiao Chen, Honglin Wang, Zhiduo Liu, Bing Su, Florent Ginhoux
Most tissue-resident macrophage (RTM) populations are seeded by waves of embryonic hematopoiesis and are self-maintained independently of a bone-marrow contribution during adulthood. A proportion of RTMs, however, is constantly replaced by blood monocytes and their functions compared to embryonic RTM remains unclear. The kinetics and extent of the contribution of circulating monocytes to RTM replacement during homeostasis, inflammation and disease is highly debated. Here, we identified Ms4a3 as a specific marker expressed by granulocyte-monocyte progenitors (GMPs) and subsequently generated Ms4a3TdT reporter and Ms4a3Cre-RosaTdT fate mapper models to follow monocytes and their progenies. Our Ms4a3Cre-RosaTdT model traced efficiently blood monocytes (97%) and granulocytes (100%), but no lymphocytes or tissue dendritic cells. Using this model, we precisely quantified the contribution of monocytes to the RTM pool during homeostasis and inflammation. The unambiguous identification of monocyte-derived cells will permit future studies of their function under any condition.
632 downloads scientific communication and education
Universities are increasingly evaluated, both internally and externally on the basis of their outputs. Often these are converted to simple, and frequently contested, rankings based on quantitative analysis of those outputs. These rankings can have substantial implications for student and staff recruitment, research income and perceived prestige of a university. Both internal and external analyses usually rely on a single data source to define the set of outputs assigned to a specific university. Although some differences between such databases are documented, few studies have explored them at the institutional scale and examined the implications of these differences for the metrics and rankings that are derived from them. We address this gap by performing detailed bibliographic comparisons between three key databases: Web of Science (WoS), Scopus and, the recently relaunched Microsoft Academic (MSA). We analyse the differences between outputs with DOIs identified from each source for a sample of 155 universities and supplement this with a detailed manual analysis of the differences for fifteen universities. We find significant differences between the sources at the university level. Sources differ in the publication year of specific objects, the completeness of metadata, as well as in their coverage of disciplines, outlets, and publication type. We construct two simple rankings based on citation counts and open access status of the outputs for these universities and show dramatic changes in position based on the choice of bibliographic data sources. Those universities that experience the largest changes are frequently those from non-English speaking countries and those that are outside the top positions in international university rankings. Overall MSA has greater coverage than Scopus or WoS, but has less complete affiliation metadata. We suggest that robust evaluation measures need to consider the effect of choice of data sources and recommend an approach where data from multiple sources is integrated to provide a more robust dataset.
631 downloads neuroscience
Parkinson's disease (PD) is characterized by the accumulation of misfolded alpha-synuclein (α-syn) into intraneuronal inclusions named Lewy bodies (LB). Although it is widely believed that α-syn plays a central role in the pathogenesis of PD and synucleinopathies, the processes that govern α-syn fibrillization and LB formation in the brain remain poorly understood. In this work, we sought to reverse engineer LBs and dissect the spatiotemporal events involved in their biogenesis at the genetic, molecular, biochemical, structural, and cellular levels. Toward this goal, we took advantage of a seeding-based model of α-syn fibril formation in primary neurons and further developed this model to generate the first neuronal model that reproduces the key events leading to LB formation; including seeding, fibrillization, and the formation of LB-like inclusions that recapitulate many of the biochemical, structural, and organizational features of LBs found in post-mortem human PD brain tissues. Next, we applied an integrative approach combining confocal and correlative light-electron microscopy (CLEM) imaging methods with biochemical profiling of α-syn species and temporal proteomic and transcriptomic analyses to dissect the molecular events associated with LB formation and maturation and to elucidate their contributions to neuronal dysfunctions and neurodegeneration in PD and synucleinopathies. The results from these studies demonstrate that LB formation involves a complex interplay between α-syn fibrillization, post-translational modifications, and interactions between α-syn aggregates and membranous organelles, including mitochondria and the autophagosome and endolysosome. Furthermore, we demonstrate that the process of LB formation and maturation, rather than simply fibril formation, is the major driver of neurodegeneration through disruption of cellular functions and inducing mitochondria damage and deficits, as well as synaptic dysfunctions. Having a neuronal model that allows for unlinking of the key processes involved in LB formation is crucial for elucidating the molecular and cellular determinants of each process and their contributions to neuronal dysfunction and degeneration in PD and synucleinopathies. Such a model is essential to efforts to identify and investigate the mode of action and toxicity of drug candidates targeting α-syn aggregation and LB formation.
625 downloads neuroscience
Individual animals vary in their behaviors. This is true even when they share the same genotype and were reared in the same environment. Clusters of covarying behaviors constitute behavioral syndromes, and an individual's position along such axes of covariation is a representation of their personality. Despite these conceptual frameworks, the structure of behavioral covariation within a genotype is essentially uncharacterized and its mechanistic origins unknown. Passing hundreds of isogenic Drosophila individuals through an experimental pipeline that captured hundreds of behavioral measures, we found correlations only between sparse pairs of behaviors. Thus, the space of behavioral variation has many independent dimensions. Manipulating the physiology of the brain, and specific neural populations, altered specific correlations. We also observed that variation in gene expression can predict an individual's position on some behavior axes. This work represents the first steps in understanding the biological mechanisms determining the structure of behavioral variation within a genotype.
618 downloads bioinformatics
Nicola De Maio, Liam P. Shaw, Alasdair Hubbard, Sophie George, Nick Sanderson, Jeremy Swann, Ryan Wick, Manal AbuOun, Emma Stubberfield, Sarah J Hoosdally, Derrick W Crook, Timothy E. A. Peto, Anna E Sheppard, Mark J. Bailey, Daniel S Read, Muna F. Anjum, A Sarah Walker, Nicole Stoesser, The REHAB consortium
Illumina sequencing allows rapid, cheap and accurate whole genome bacterial analyses, but short reads (<300 bp) do not usually enable complete genome assembly. Long read sequencing greatly assists with resolving complex bacterial genomes, particularly when combined with short-read Illumina data (hybrid assembly); however, it is not clear how different long-read sequencing methods impact on assembly accuracy. Relative automation of the assembly process is also crucial to facilitating high-throughput complete bacterial genome reconstruction, avoiding multiple bespoke filtering and data manipulation steps. In this study, we compared hybrid assemblies for 20 bacterial isolates, including two reference strains, using Illumina sequencing and long reads from either Oxford Nanopore Technologies (ONT) or from SMRT Pacific Biosciences (PacBio) sequencing platforms. We chose isolates from the Enterobacteriaceae family, as these frequently have highly plastic, repetitive genetic structures and complete genome reconstruction for these species is relevant for a precise understanding of the epidemiology of antimicrobial resistance. We de novo assembled genomes using the hybrid assembler Unicycler and compared different read processing strategies. Both strategies facilitate high-quality genome reconstruction. Combining ONT and Illumina reads fully resolved most genomes without additional manual steps, and at a lower cost per isolate in our setting. Automated hybrid assembly is a powerful tool for complete and accurate bacterial genome assembly.
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