Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 62,102 bioRxiv papers from 275,834 authors.

Most downloaded bioRxiv papers, since beginning of last month

49,349 results found. For more information, click each entry to expand.

101: Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons
more details view paper

Posted to bioRxiv 23 Sep 2019

Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons
525 downloads neuroscience

Audrey Sederberg, Ilya Nemenman

Advances in neural recording methods enable sampling from populations of thousands of neurons during the performance of behavioral tasks, raising the question of how recorded activity relates to the theoretical models of computations underlying performance. In the context of decision making in rodents, patterns of functional connectivity between choice-selective cortical neurons, as well as broadly distributed choice information in both excitatory and inhibitory populations, were recently reported [1]. The straightforward interpretation of these data suggests a mechanism relying on specific patterns of anatomical connectivity to achieve selective pools of inhibitory as well as excitatory neurons. We investigate an alternative mechanism for the emergence of these experimental observations using a computational approach. We find that a randomly connected network of excitatory and inhibitory neurons generates single-cell selectivity, patterns of pairwise correlations, and indistinguishable excitatory and inhibitory readout weight distributions, as observed in recorded neural populations. Further, we make the readily verifiable experimental predictions that, for this type of evidence accumulation task, there are no anatomically defined sub-populations of neurons representing choice, and that choice preference of a particular neuron changes with the details of the task. This work suggests that distributed stimulus selectivity and patterns of functional organization in population codes could be emergent properties of randomly connected networks.

102: Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets
more details view paper

Posted to bioRxiv 06 Sep 2019

Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets
523 downloads bioinformatics

Marc-Andre Schulz, Thomas Yeo, Joshua Vogelstein, Janaina Mourao-Miranada, Jakob Kather, Konrad Kording, B.T. Thomas Yeo, Danilo Bzdok

In recent years, deep learning has unlocked unprecedented success in various domains, especially in image, text, and speech processing. These breakthroughs may hold promise for neuroscience and especially for brain-imaging investigators who start to analyze thousands of participants. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at currently available sample sizes. We systematically profiled the performance of deep models, kernel models, and linear models as a function of sample size on UK Biobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improved when escalating from linear models to shallow-nonlinear models, and further improved when switching to deep-nonlinear models. The more observations were available for model training, the greater the performance gain we saw. In contrast, using structural or functional brain scans, simple linear models performed on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In fact, linear models kept improving as the sample size approached ~10,000 participants. Our results indicate that the increase in performance of linear models with additional data does not saturate at the limit of current feasibility. Yet, nonlinearities of common brain scans remain largely inaccessible to both kernel and deep learning methods at any examined scale.

103: Reptile-like physiology in Early Jurassic stem-mammals
more details view paper

Posted to bioRxiv 30 Sep 2019

Reptile-like physiology in Early Jurassic stem-mammals
523 downloads paleontology

Elis Newham, Pamela G Gill, Philippa Brewer, Michael J. Benton, Vincent Fernandez, Neil J Gostling, David Haberthür, Jukka Jernvall, Tuomas Kankanpää, Aki Kallonen, Charles Navarro, Alexandra Pacureanu, Berit Zeller-Plumhoff, Kelly Richards, Kate Robson-Brown, Philipp Schneider, Heikki Suhonen, Paul Tafforeau, Katherine Williams, Ian J Corfe

There is uncertainty regarding the timing and fossil species in which mammalian endothermy arose, with few studies of stem-mammals on key aspects of endothermy such as basal or maximum metabolic rates, or placing them in the context of living vertebrate metabolic ranges. Synchrotron X-ray imaging of incremental tooth cementum shows two Early Jurassic stem-mammals, Morganucodon and Kuehneotherium , had lifespans (a basal metabolic rate proxy) considerably longer than comparably sized living mammals, but similar to reptiles. Morganucodon also had femoral blood flow rates (a maximum metabolic rate proxy) intermediate between living mammals and reptiles. This shows maximum metabolic rates increased evolutionarily before basal rates, and that contrary to previous suggestions of a Triassic origin, Early Jurassic stem-mammals lacked the endothermic metabolism of living mammals. One Sentence Summary Surprisingly long lifespans and low femoral blood flow suggest reptile-like physiology in key Early Jurassic stem-mammals.

104: Improved CUT&RUN chromatin profiling and analysis tools
more details view paper

Posted to bioRxiv 06 Mar 2019

Improved CUT&RUN chromatin profiling and analysis tools
523 downloads molecular biology

Michael P. Meers, Terri Bryson, Steven Henikoff

We previously described a novel alternative to Chromatin Immunoprecipitation, Cleavage Under Targets & Release Using Nuclease (CUT&RUN), in which unfixed permeabilized cells are incubated with antibody, followed by binding of a Protein A-Micrococcal Nuclease (pA/MNase) fusion protein (1). Upon activation of tethered MNase, the bound complex is excised and released into the supernatant for DNA extraction and sequencing. Here we introduce four enhancements to CUT&RUN: 1) a hybrid Protein A-Protein G-MNase construct that expands antibody compatibility and simplifies purification; 2) a modified digestion protocol that inhibits premature release of the nuclease-bound complex; 3) a calibration strategy based on carry-over of E. coli DNA introduced with the fusion protein; and 4) a novel peak-calling strategy customized for the low-background profiles obtained using CUT&RUN. These new features, coupled with the previously described low-cost, high efficiency, high reproducibility and high- throughput capability of CUT&RUN make it the method of choice for routine epigenomic profiling.

105: A Quantitative Single-Cell Proteomics Approach to Characterize an Acute Myeloid Leukemia Hierarchy
more details view paper

Posted to bioRxiv 24 Aug 2019

A Quantitative Single-Cell Proteomics Approach to Characterize an Acute Myeloid Leukemia Hierarchy
522 downloads systems biology

Erwin M. Schoof, Nicolas Rapin, Simonas Savickas, Coline Gentil, Eric Lechman, James Seymour Haile, Ulrich auf dem Keller, John E Dick, Bo Porse

In recent years, cellular life science research has experienced a significant shift, moving away from conducting bulk cell interrogation towards single-cell analysis. It is only through single cell analysis that a complete understanding of cellular heterogeneity, and the interplay between various cell types that are fundamental to specific biological phenotypes, can be achieved. Single-cell assays at the protein level have been predominantly limited to targeted, antibody-based methods. However, here we present an experimental and computational pipeline, which establishes a comprehensive single-cell mass spectrometry-based proteomics workflow. By exploiting a leukemia culture system, containing functionally-defined leukemic stem cells, progenitors and terminally differentiated blasts, we demonstrate that our workflow is able to explore the cellular heterogeneity within this aberrant developmental hierarchy. We show our approach is capable to quantifying hundreds of proteins across hundreds of single cells using limited instrument time. Furthermore, we developed a computational pipeline (SCeptre), that effectively clusters the data and permits the extraction of cell-specific proteins and functional pathways. This proof-of-concept work lays the foundation for future global single-cell proteomics studies.

106: Brainstem Neurons that Command Left/Right Locomotor Asymmetries
more details view paper

Posted to bioRxiv 03 Sep 2019

Brainstem Neurons that Command Left/Right Locomotor Asymmetries
522 downloads neuroscience

Jared M. Cregg, Roberto Leiras, Alexia Montalant, Ian R Wickersham, Ole Kiehn

Descending command neurons instruct spinal networks to execute basic locomotor functions, such as which gait and what speed. The command functions for gait and speed are symmetric, implying that a separate unknown system directs asymmetric movements—the ability to move left or right. Here we report the discovery that Chx10 -lineage reticulospinal neurons act to control the direction of locomotor movements in mammals. Chx10 neurons exhibit ipsilateral projection, and can decrease spinal limb-based locomotor activity ipsilaterally. This circuit mechanism acts as the basis for left or right locomotor movements in freely moving animals: selective unilateral activation of Chx10 neurons causes ipsilateral movements whereas inhibition causes contralateral movements. Spontaneous forward locomotion is thus transformed into an ipsilateral movement by braking locomotion on the ipsilateral side. We identify sensorimotor brain regions that project onto Chx10 reticulospinal neurons, and demonstrate that their unilateral activation can impart left/right directional commands. Together these data identify the descending motor system which commands left/right locomotor asymmetries in mammals.

107: An Improved Crystal Violet Assay for Biofilm Quantification in 96-Well Microtitre Plate
more details view paper

Posted to bioRxiv 13 Jan 2017

An Improved Crystal Violet Assay for Biofilm Quantification in 96-Well Microtitre Plate
521 downloads microbiology

Sudhir K Shukla, T. Subba Rao

Microplates are essential tools for biofilm research since it allows high throughput screening of biofilm forming strains or in the assay of anti-biofilm drugs. However, 96 well microtitre plate based assays share the issue of 'edge effect'. The primary cause of the 'edge effect' phenomenon is evaporation. As edge effect causes a significant increase in plate rejection rate by introducing experimental error, we improvised the classical crystal violet assay to reduce water loss from the peripheral wells. The improvised method showed a significant reduction in edge effect and minimised error in crystal violet assay

108: The art of using t-SNE for single-cell transcriptomics
more details view paper

Posted to bioRxiv 25 Oct 2018

The art of using t-SNE for single-cell transcriptomics
520 downloads bioinformatics

Dmitry Kobak, Philipp Berens

Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells. Common data analysis pipelines include a dimensionality reduction step for visualising the data in two dimensions, most frequently performed using t-distributed stochastic neighbour embedding (t-SNE). It excels at revealing local structure in high-dimensional data, but naive applications often suffer from severe shortcomings, e.g. the global structure of the data is not represented accurately. Here we describe how to circumvent such pitfalls, and develop a protocol for creating more faithful t-SNE visualisations. It includes PCA initialisation, a high learning rate, and multi-scale similarity kernels; for very large data sets, we additionally use exaggeration and downsampling-based initialisation. We use published single-cell RNA-seq data sets to demonstrate that this protocol yields superior results compared to the naive application of t-SNE.

109: High-throughput isolation and sorting of gut microbes reduce biases of traditional cultivation strategies
more details view paper

Posted to bioRxiv 06 Sep 2019

High-throughput isolation and sorting of gut microbes reduce biases of traditional cultivation strategies
518 downloads microbiology

William J. Watterson, Melikhan Tanyeri, Andrea R. Watson, Candace M. Cham, Yue Shan, Eugene B. Chang, A. Murat Eren, Savas Tay

Traditional cultivation approaches in microbiology are labor-intensive, low-throughput, and often yield biased sampling of taxa due to ecological and evolutionary factors. New strategies are needed to enable ample representation of rare taxa and slow-growers that are outcompeted by fast-growing organisms. We developed a microfluidic platform that anaerobically isolates and cultivates microbial cells in millions of picoliter droplets and automatically sorts droplets based on colony density. We applied our strategy to mouse and human gut microbiomes and used 16S ribosomal RNA gene amplicons to characterize taxonomic composition of cells grown using different media. We found up to 4-fold increase in richness and larger representation of rare taxa among cells grown in droplets compared to conventional culture plates. Automated sorting of droplets for slow-growing colonies further enhanced the relative abundance of rare populations. Our method improves the cultivation and analysis of diverse microbiomes to gain deeper insights into microbial functioning and lifestyles.

110: Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front
more details view paper

Posted to bioRxiv 24 Aug 2019

Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front
517 downloads immunology

Christian M. Schürch, Salil S. Bhate, Graham L. Barlow, Darci J. Phillips, Luca Noti, Inti Zlobec, Pauline Chu, Sarah Black, Janos Demeter, David R. McIlwain, Nikolay Samusik, Yury Goltsev, Garry P Nolan

Antitumoral immunity requires organized, spatially nuanced interactions between components of the immune tumor microenvironment (iTME). Understanding this coordinated behavior in effective versus ineffective tumor control will advance immunotherapies. We optimized CO-Detection by indEXing (CODEX) for paraffin-embedded tissue microarrays, enabling profiling of 140 tissue regions from 35 advanced-stage colorectal cancer (CRC) patients with 56 protein markers simultaneously. We identified nine conserved, distinct cellular neighborhoods (CNs) - a collection of components characteristic of the CRC iTME. Enrichment of PD- 1+CD4+ T cells only within a granulocyte CN positively correlated with survival in a high-risk patient subset. Coupling of tumor and immune CNs, fragmentation of T cell and macrophage CNs, and disruption of inter-CN communication was associated with inferior outcomes. This study provides a framework for interrogating complex biological processes, such as antitumoral immunity, demonstrating an example of how tumors can disrupt immune functionality through interference in the concerted action of cells and spatial domains.

111: Telomere-to-telomere assembly of a complete human X chromosome
more details view paper

Posted to bioRxiv 16 Aug 2019

Telomere-to-telomere assembly of a complete human X chromosome
515 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 [1][1],[2][2]. 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 [2][2], 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 [3][3], 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. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3

112: A CyclinB2-Cas9 fusion promotes the homology-directed repair of double-strand breaks
more details view paper

Posted to bioRxiv 20 Feb 2019

A CyclinB2-Cas9 fusion promotes the homology-directed repair of double-strand breaks
512 downloads molecular biology

Manuel M Vicente, Afonso Mendes, Margarida Cruz, Jose R Vicente, Vasco M Barreto

The discovery of clustered regularly interspaced palindromic repeats (CRISPR), a defense system against viruses found in bacteria, launched a new era in gene targeting. The key feature of this technique is the guiding of the endonuclease Cas9 by single guide RNAs (sgRNA) to specific sequences, where a DNA lesion is introduced to trigger DNA repair. The CRISPR/Cas9 system may be extremely relevant for gene therapy, but the technique needs improvement to become a safe and fully effective tool. The Cas9-induced double-strand break (DSB) is repaired by one of two pathways, the error-prone Non-homologous end joining (NHEJ) or the high-fidelity Homology Direct Repair (HDR). Shifting the repair of the DSB to HDR is challenging, given the efficiency of NHEJ. Here we describe an engineered protein approach to increase knock-in efficiency by promoting the relative increase in Cas9 activity in G2, the phase of the cell cycle where HDR is more active. Cas9 was fused to the degradation domain of proteins known to be degraded in G1. The activity of two chimeric proteins, Geminin-Cas9 and CyclinB2-Cas9, is demonstrated, as well as their cell-cycle-dependent degradation. The chimeras shifted the repair of the DSBs to the HDR repair pathway compared to the commonly used Cas9. The application of cell cycle specific degradation tags could pave the way for more efficient and secure gene editing applications of the CRISPR/Cas9 system.

113: Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism
more details view paper

Posted to bioRxiv 30 Nov 2018

Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism
512 downloads genetics

F. Kyle Satterstrom, Jack A. Kosmicki, Jiebiao Wang, Michael S. Breen, Silvia De Rubeis, Joon-Yong An, Minshi Peng, Ryan Collins, Jakob Grove, Lambertus Klei, Christine Stevens, Jennifer Reichert, Maureen S. Mulhern, Mykyta Artomov, Sherif Gerges, Brooke Sheppard, Xinyi Xu, Aparna Bhaduri, Utku Norman, Harrison Brand, Grace Schwartz, Rachel Nguyen, Elizabeth E. Guerrero, Caroline Dias, Branko Aleksic, Richard Anney, Mafalda Barbosa, Somer Bishop, Alfredo Brusco, Jonas Bybjerg-Grauholm, Angel Carracedo, Marcus C.Y. Chan, Andreas G. Chiocchetti, Brian H. Y. Chung, Hilary Coon, Michael L. Cuccaro, Aurora Currò, Bernardo Dalla Bernardina, Ryan Doan, Enrico Domenici, Shan Dong, Chiara Fallerini, Montserrat Fernández-Prieto, Giovanni Battista Ferrero, Christine M. Freitag, Menachem Fromer, J. Jay Gargus, Daniel Geschwind, Elisa Giorgio, Javier González-Peñas, Stephen Guter, Danielle Halpern, Emily Hansen-Kiss, Xin He, Gail E. Herman, Irva Hertz-Picciotto, David M Hougaard, Christina M Hultman, Iuliana Ionita-Laza, Suma Jacob, Jesslyn Jamison, Astanand Jugessur, Miia Kaartinen, Gun Peggy Knudsen, Alexander Kolevzon, Itaru Kushima, So Lun Lee, Terho Lehtimäki, Elaine T Lim, Carla Lintas, W. Ian Lipkin, Diego Lopergolo, Fátima Lopes, Yunin Ludena, Patricia Maciel, Per Magnus, Behrang Mahjani, Nell Maltman, Dara S Manoach, Gal Meiri, Idan Menashe, Judith Miller, Nancy Minshew, Eduarda Montenegro M. de Souza, Danielle Moreira, Eric M Morrow, Ole Mors, Preben Bo Mortensen, Matthew Mosconi, Pierandrea Muglia, Benjamin Neale, Merete Nordentoft, Norio Ozaki, Aarno Palotie, Mara Parellada, Maria Rita Passos-Bueno, Margaret Pericak-Vance, Antonio Persico, Isaac Pessah, Kaija Puura, Abraham Reichenberg, Alessandra Renieri, Evelise Riberi, Elise B Robinson, Kaitlin E. Samocha, Sven Sandin, Susan L Santangelo, Gerry Schellenberg, Stephen W Scherer, Sabine Schlitt, Rebecca Schmidt, Lauren Schmitt, Isabela Maya W. Silva, Tarjinder Singh, Paige M. Siper, Moyra Smith, Gabriela Soares, Camilla Stoltenberg, Pål Suren, Ezra Susser, John Sweeney, Peter Szatmari, Lara Tang, Flora Tassone, Karoline Teufel, Elisabetta Trabetti, Maria del Pilar Trelles, Christopher Walsh, Lauren A. Weiss, Thomas Werge, Donna Werling, Emilie M. Wigdor, Emma Wilkinson, Jeremy A Willsey, Tim Yu, Mullin H.C. Yu, Ryan Yuen, Elaine Zachi, iPSYCH consortium, Catalina Betancur, Edwin H. Cook, Louise Gallagher, Michael Gill, James S Sutcliffe, Audrey Thurm, Michael E. Zwick, Anders D. Børglum, Matthew W State, A. Ercument Cicek, Michael E. Talkowski, David J. Cutler, Bernie Devlin, Stephan Sanders, Kathryn Roeder, Mark J. Daly, Joseph D. Buxbaum

We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n=35,584 total samples, 11,986 with ASD). Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate ≤ 0.1. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 53 show higher frequencies in individuals ascertained for ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most of the risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In human cortex single-cell gene expression data, expression of risk genes is enriched in both excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.

114: Evidence for bias of genetic ancestry in resting state functional MRI
more details view paper

Posted to bioRxiv 11 Oct 2018

Evidence for bias of genetic ancestry in resting state functional MRI
510 downloads neuroscience

Andre Altmann, Janaina Mourao-Miranda

Resting state functional magnetic resonance imaging (rs-fMRI) is a popular imaging modality for mapping the functional connectivity of the brain. Rs-fMRI is, just like other neuroimaging modalities, subject to a series of technical and subject level biases that change the inferred connectivity pattern. In this work we predicted genetic ancestry from rs-fMRI connectivity data at very high performance (area under the ROC curve of 0.93). Thereby, we demonstrated that genetic ancestry is encoded in the functional connectivity pattern of the brain at rest. Consequently, genetic ancestry constitutes a bias that should be accounted for in the analysis of rs-fMRI data.

115: Removing reference bias in ancient DNA data analysis by mapping to a sequence variation graph
more details view paper

Posted to bioRxiv 26 Sep 2019

Removing reference bias in ancient DNA data analysis by mapping to a sequence variation graph
506 downloads genomics

Rui Martiniano, Erik Garrison, Eppie R. Jones, Andrea Manica, Richard Durbin

During the last decade, the analysis of ancient DNA (aDNA) sequence has become a powerful tool for the study of past human populations. However, the degraded nature of aDNA means that aDNA sequencing reads are short, single-ended and frequently mutated by post-mortem chemical modifications. All these features decrease read mapping accuracy and increase reference bias, in which reads containing non-reference alleles are less likely to be mapped than those containing reference alleles. Recently, alternative approaches for read mapping and genetic variation analysis have been developed that replace the linear reference by a variation graph which includes all the alternative variants at each genetic locus. Here, we evaluate the use of variation graph software vg to avoid reference bias for ancient DNA. We used vg to align multiple previously published aDNA samples to a variation graph containing 1000 Genome Project variants, and compared these with the same data aligned with bwa to the human linear reference genome. We show that use of vg leads to a much more balanced allelic representation at polymorphic sites and better variant detection in comparison with bwa, especially in the presence of post-mortem changes, effectively removing reference bias. A recently published approach that filters bwa alignments using modified reads also removes bias, but has lower sensitivity than vg. Our findings demonstrate that aligning aDNA sequences to variation graphs allows recovering a higher fraction of non-reference variation and effectively mitigates the impact of reference bias in population genetics analyses using aDNA, while retaining mapping sensitivity.

116: A functional cortical network for sensorimotor sequence generation
more details view paper

Posted to bioRxiv 27 Sep 2019

A functional cortical network for sensorimotor sequence generation
506 downloads neuroscience

Duo Xu, Yuxi Chen, Angel M Delgado, Natasha C Hughes, Linghua Zhang, Mingyuan Dong, Daniel H. O'Connor

The brain generates complex sequences of movements that can be flexibly reconfigured in real-time based on sensory feedback, but how this occurs is not fully understood. We developed a novel "sequence licking" task in which mice directed their tongue to a target that moved through a series of locations. Mice could rapidly reconfigure the sequence online based on tactile feedback. Closed-loop optogenetics and electrophysiology revealed that tongue/jaw regions of somatosensory (S1TJ) and motor (M1TJ) cortex encoded and controlled tongue kinematics at the level of individual licks. Tongue premotor (anterolateral motor, ALM) cortex encoded intended tongue angle in a smooth manner that spanned individual licks and even whole sequences, and progress toward the reward that marked successful sequence execution. ALM activity regulated sequence initiation, but multiple cortical areas collectively controlled termination of licking. Our results define a functional cortical network for hierarchical control of sensory- and reward-guided orofacial sequence generation.

117: Dopamine waves as a mechanism for spatiotemporal credit assignment
more details view paper

Posted to bioRxiv 13 Aug 2019

Dopamine waves as a mechanism for spatiotemporal credit assignment
504 downloads neuroscience

Arif A. Hamid, Michael J Frank, Christopher I Moore

Significant evidence supports the view that dopamine shapes reward-learning by encoding prediction errors. However, it is unknown whether dopamine decision-signals are tailored to the functional specialization of target regions. Here, we report a novel set of wave-like spatiotemporal activity-patterns in dopamine axons across the dorsal striatum. These waves switch between different activational motifs and organize dopamine transients into localized clusters within functionally related striatal subregions. These specific motifs are associated with distinct task contexts: At reward delivery, dopamine signals rapidly resynchronize into propagating waves with opponent directions depending on instrumental task contingencies. Moreover, dopamine dynamics during reward pursuit signal the extent to which mice have instrumental control, and interact with reward waves to predict future behavioral adjustments. Our results are consistent with a computational architecture in which striatal dopamine signals are sculpted by inference about instrumental controllability, and provide evidence for a spatiotemporally 'vectorized' role of dopamine in credit assignment.

118: Performance of neural network basecalling tools for Oxford Nanopore sequencing
more details view paper

Posted to bioRxiv 07 Feb 2019

Performance of neural network basecalling tools for Oxford Nanopore sequencing
498 downloads bioinformatics

Ryan R Wick, Louise M Judd, Kathryn Holt

Basecalling, the computational process of translating raw electrical signal to nucleotide sequence, is of critical importance to the sequencing platforms produced by Oxford Nanopore Technologies (ONT). Here we examine the performance of different basecalling tools, looking at accuracy at the level of bases within individual reads and at majority-rules consensus basecalls in an assembly. We also investigate some additional aspects of basecalling: training using a taxon-specific dataset, using a larger neural network model and improving consensus basecalls in an assembly by additional signal-level analysis with Nanopolish. Training basecallers on taxon-specific data results in a significant boost in consensus accuracy, mostly due to the reduction of errors in methylation motifs. A larger neural network is able to improve both read and consensus accuracy, but at a cost to speed. Improving consensus sequences ('polishing') with Nanopolish somewhat negates the accuracy differences in basecallers, but prepolish accuracy does have an effect on post-polish accuracy. Basecalling accuracy has seen significant improvements over the last two years. The current version of ONT's Guppy basecaller performs well overall, with good accuracy and fast performance. If higher accuracy is required, users should consider producing a custom model using a larger neural network and/or training data from the same species.

119: Optimal design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis
more details view paper

Posted to bioRxiv 12 Sep 2019

Optimal design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis
497 downloads bioinformatics

Igor Mandric, Tommer Schwarz, Arunabha Majumdar, Richard Perez, Meena Subramaniam, Chun Jimmie Ye, Bogdan Pasaniuc, Eran Halperin

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to simultaneously measure cellular composition and state which is impossible with bulk profiling approaches. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power.

120: Evaluation of UMAP as an alternative to t-SNE for single-cell data
more details view paper

Posted to bioRxiv 10 Apr 2018

Evaluation of UMAP as an alternative to t-SNE for single-cell data
497 downloads bioinformatics

Etienne Becht, Charles-Antoine Dutertre, Immanuel W. H. Kwok, Lai Guan Ng, Florent Ginhoux, Evan W Newell

Uniform Manifold Approximation and Projection (UMAP) is a recently-published non-linear dimensionality reduction technique. Another such algorithm, t-SNE, has been the default method for such task in the past years. Herein we comment on the usefulness of UMAP high-dimensional cytometry and single-cell RNA sequencing, notably highlighting faster runtime and consistency, meaningful organization of cell clusters and preservation of continuums in UMAP compared to t-SNE.

Previous page 1 . . . 4 5 6 7 8 9 10 . . . 2468 Next page

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News