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Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 62,502 bioRxiv papers from 277,505 authors.

Most downloaded bioRxiv papers, since beginning of last month

60,783 results found. For more information, click each entry to expand.

1: An integrated brain-machine interface platform with thousands of channels
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Posted to bioRxiv 17 Jul 2019

An integrated brain-machine interface platform with thousands of channels
11,301 downloads neuroscience

Elon Musk, Neuralink

Brain-machine interfaces (BMIs) hold promise for the restoration of sensory and motor function and the treatment of neurological disorders, but clinical BMIs have not yet been widely adopted, in part because modest channel counts have limited their potential. In this white paper, we describe Neuralink’s first steps toward a scalable high-bandwidth BMI system. We have built arrays of small and flexible electrode “threads”, with as many as 3,072 electrodes per array distributed across 96 threads. We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute. Each thread can be individually inserted into the brain with micron precision for avoidance of surface vasculature and targeting specific brain regions. The electrode array is packaged into a small implantable device that contains custom chips for low-power on-board amplification and digitization: the package for 3,072 channels occupies less than (23 × 18.5 × 2) mm3. A single USB-C cable provides full-bandwidth data streaming from the device, recording from all channels simultaneously. This system has achieved a spiking yield of up to 70% in chronically implanted electrodes. Neuralink’s approach to BMI has unprecedented packaging density and scalability in a clinically relevant package.

2: Mammalian Y RNAs are modified at discrete guanosine residues with N-glycans
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Posted to bioRxiv 30 Sep 2019

Mammalian Y RNAs are modified at discrete guanosine residues with N-glycans
7,005 downloads molecular biology

Ryan A. Flynn, Benjamin A. H. Smith, Alex G Johnson, Kayvon Pedram, Benson M. George, Stacy A. Malaker, Karim Majzoub, Jan E. Carette, Carolyn R. Bertozzi

Glycans modify lipids and proteins to mediate inter- and intramolecular interactions across all domains of life. RNA, another multifaceted biopolymer, is not thought to be a major target of glycosylation. Here, we challenge this view with evidence that mammalian cells use RNA as a third scaffold for glycosylation in the secretory pathway. Using a battery of chemical and biochemical approaches, we find that a select group of small noncoding RNAs including Y RNAs are modified with complex, sialylated N-glycans (glycoRNAs). These glycoRNA are present in multiple cell types and mammalian species, both in cultured cells and in vivo. Finally, we find that RNA glycosylation depends on the canonical N-glycan biosynthetic machinery within the ER/Golgi luminal spaces. Collectively, these findings suggest the existence of a ubiquitous interface of RNA biology and glycobiology suggesting an expanded role for glycosylation beyond canonical lipid and protein scaffolds.

3: Muscle strength, size and composition following 12 months of gender-affirming treatment in transgender individuals: retained advantage for the transwomen
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Posted to bioRxiv 26 Sep 2019

Muscle strength, size and composition following 12 months of gender-affirming treatment in transgender individuals: retained advantage for the transwomen
6,950 downloads physiology

Anna Wiik, Tommy R Lundberg, Eric Rullman, Daniel P Andersson, Mats Holmberg, Mirko Mandic, Torkel B Brismar, Olof Dahlqvist Leinhard, Setareh Chanpen, John Flanagan, Stefan Arver, Thomas Gustafsson

Objectives: This study explored the effects of gender-affirming treatment, which includes inhibition of endogenous sex hormones and replacement with cross-sex hormones, on muscle function, size and composition in 11 transwomen (TW) and 12 transmen (TM). Methods: Isokinetic knee extensor and flexor muscle strength was assessed at baseline (T00), 4 weeks after gonadal suppression of endogenous hormones but before hormone replacement (T0), and 3 (T3) and 11 (T12) months after hormone replacement. In addition, at T00 and T12, we assessed lower-limb muscle volume using MRI, and cross-sectional area (CSA) and radiological density using CT. Results: Thigh muscle volume increased (15%) in TM, which was paralleled by increased quadriceps CSA (15%) and radiological density (6%). In TW, the corresponding parameters decreased by -5% (muscle volume) and -4% (CSA), while density remained unaltered. The TM increased strength over the assessment period, while the TW generally maintained or slightly increased in strength. Baseline muscle volume correlated highly with strength (R>0.75), yet the relative change in muscle volume and strength correlated only moderately (R=0.65 in TW and R=0.32 in TM). The absolute levels of muscle volume and knee extension strength after the intervention still favored the TW. Conclusion: Cross-sex hormone treatment markedly affects muscle strength, size and composition in transgender individuals. Despite the robust increases in muscle mass and strength in TM, the TW were still stronger and had more muscle mass following 12 months of treatment. These findings add new knowledge that could be relevant when evaluating transwomen's eligibility to compete in the women's category of athletic competitions.

4: Toxicity of JUUL Fluids and Aerosols Correlates Strongly with Nicotine and Some Flavor Chemical Concentrations
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Posted to bioRxiv 09 Dec 2018

Toxicity of JUUL Fluids and Aerosols Correlates Strongly with Nicotine and Some Flavor Chemical Concentrations
4,632 downloads pharmacology and toxicology

Esther Omaiye, Kevin J McWhirter, Wentai Luo, James F Pankow, Prue Talbot

While JUUL electronic cigarettes (ECs) have captured the majority of the EC market with a large fraction of their sales going to adolescents, little is known about their cytotoxicity and potential effects on health. The purpose of this study was to determine flavor chemical and nicotine concentrations in the eight currently marketed pre-filled JUUL EC cartridges (pods) and to evaluate the cytotoxicity of the different variants (e.g., Cool Mint and Creme Brulee) using in vitro assays. Nicotine and flavor chemicals were analyzed using gas chromatography/mass spectrometry in pod fluid before and after vaping and in the corresponding aerosols. 59 flavor chemicals were identified in JUUL pod fluids, and three were >1 mg/mL. Duplicate pods were similar in flavor chemical composition and concentration. Nicotine concentrations (average 60.9 mg/mL) were significantly higher than any EC products we have analyzed previously. Transfer efficiency of individual flavor chemicals that were >1mg/mL and nicotine from the pod fluid into aerosols was generally 35 - 80%. All pod fluids were cytotoxic at a 1:10 dilution (10%) in the MTT and neutral red uptake assays when tested with BEAS-2B lung epithelial cells. Most aerosols were cytotoxic in these assays at concentrations >1%. The cytotoxicity of aerosols was highly correlated with nicotine and ethyl maltol concentrations and moderately to weakly correlated with total flavor chemical concentration and menthol concentration. Our study demonstrates that: (1) some JUUL flavor pods have high concentrations of flavor chemicals that may make them attractive to youth, and (2) the concentrations of nicotine and some flavor chemicals (e.g. ethyl maltol) are high enough to be cytotoxic in acute in vitro assays, emphasizing the need to determine if JUUL products will lead to adverse health effects with chronic use.

5: The Genomic Formation of South and Central Asia
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Posted to bioRxiv 31 Mar 2018

The Genomic Formation of South and Central Asia
3,826 downloads genomics

Vagheesh M Narasimhan, Nick Patterson, Priya Moorjani, Iosif Lazaridis, Mark Lipson, Swapan Mallick, Nadin Rohland, Rebecca Bernardos, Alexander M Kim, Nathan Nakatsuka, Iñigo Olalde, Alfredo Coppa, James Mallory, Vyacheslav Moiseyev, Janet Monge, Luca M Olivieri, Nicole Adamski, Nasreen Broomandkhoshbacht, Francesca Candilio, Olivia Cheronet, Brendan J Culleton, Matthew Ferry, Daniel Fernandes, Beatriz Gamarra, Daniel Gaudio, Mateja Hajdinjak, Éadaoin Harney, Thomas K Harper, Denise Keating, Ann Marie Lawson, Megan Michel, Mario Novak, Jonas Oppenheimer, Niraj Rai, Kendra Sirak, Viviane Slon, Kristin Stewardson, Zhao Zhang, Gaziz Akhatov, Anatoly N Bagashev, Bauryzhan Baitanayev, Gian Luca Bonora, Tatiana Chikisheva, Anatoly Derevianko, Enshin Dmitry, Katerina Douka, Nadezhda Dubova, Andrey Epimakhov, Suzanne Freilich, Dorian Fuller, Alexander Goryachev, Andrey Gromov, Bryan Hanks, Margaret Judd, Erlan Kazizov, Aleksander Khokhlov, Egor Kitov, Elena Kupriyanova, Pavel Kuznetsov, Donata Luiselli, Farhod Maksudov, Christopher Meiklejohn, Deborah Merrett, Roberto Micheli, Oleg Mochalov, Zahir Muhammed, Samariddin Mustafokulov, Ayushi Nayak, Rykun M Petrovna, Davide Pettener, Richard Potts, Dmitry Razhev, Stefania Sarno, Kulyan Sikhymbaeva, Sergey M Slepchenko, Nadezhda Stepanova, Svetlana Svyatko, Sergey Vasilyev, Massimo Vidale, Dmitriy Voyakin, Antonina Yermolayeva, Alisa Zubova, Vasant S Shinde, Carles Lalueza-Fox, Matthias Meyer, David Anthony, Nicole Boivin, Kumarasamy Thangaraj, Douglas J. Kennett, Michael Frachetti, Ron Pinhasi, David Reich

The genetic formation of Central and South Asian populations has been unclear because of an absence of ancient DNA. To address this gap, we generated genome-wide data from 362 ancient individuals, including the first from eastern Iran, Turan (Uzbekistan, Turkmenistan, and Tajikistan), Bronze Age Kazakhstan, and South Asia. Our data reveal a complex set of genetic sources that ultimately combined to form the ancestry of South Asians today. We document a southward spread of genetic ancestry from the Eurasian Steppe, correlating with the archaeologically known expansion of pastoralist sites from the Steppe to Turan in the Middle Bronze Age (2300-1500 BCE). These Steppe communities mixed genetically with peoples of the Bactria Margiana Archaeological Complex (BMAC) whom they encountered in Turan (primarily descendants of earlier agriculturalists of Iran), but there is no evidence that the main BMAC population contributed genetically to later South Asians. Instead, Steppe communities integrated farther south throughout the 2nd millennium BCE, and we show that they mixed with a more southern population that we document at multiple sites as outlier individuals exhibiting a distinctive mixture of ancestry related to Iranian agriculturalists and South Asian hunter-gathers. We call this group Indus Periphery because they were found at sites in cultural contact with the Indus Valley Civilization (IVC) and along its northern fringe, and also because they were genetically similar to post-IVC groups in the Swat Valley of Pakistan. By co-analyzing ancient DNA and genomic data from diverse present-day South Asians, we show that Indus Periphery-related people are the single most important source of ancestry in South Asia — consistent with the idea that the Indus Periphery individuals are providing us with the first direct look at the ancestry of peoples of the IVC — and we develop a model for the formation of present-day South Asians in terms of the temporally and geographically proximate sources of Indus Periphery-related, Steppe, and local South Asian hunter-gatherer-related ancestry. Our results show how ancestry from the Steppe genetically linked Europe and South Asia in the Bronze Age, and identifies the populations that almost certainly were responsible for spreading Indo-European languages across much of Eurasia.

6: Prefrontal cortex as a meta-reinforcement learning system
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Posted to bioRxiv 06 Apr 2018

Prefrontal cortex as a meta-reinforcement learning system
3,086 downloads neuroscience

Jane X Wang, Zeb Kurth-Nelson, Dharshan Kumaran, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Demis Hassabis, Matthew Botvinick

Over the past twenty years, neuroscience research on reward-based learning has converged on a canonical model, under which the neurotransmitter dopamine 'stamps in' associations between situations, actions and rewards by modulating the strength of synaptic connections between neurons. However, a growing number of recent findings have placed this standard model under strain. In the present work, we draw on recent advances in artificial intelligence to introduce a new theory of reward-based learning. Here, the dopamine system trains another part of the brain, the prefrontal cortex, to operate as its own free-standing learning system. This new perspective accommodates the findings that motivated the standard model, but also deals gracefully with a wider range of observations, providing a fresh foundation for future research.

7: LeafByte: A mobile application that measures leaf area and herbivory quickly and accurately
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Posted to bioRxiv 23 Sep 2019

LeafByte: A mobile application that measures leaf area and herbivory quickly and accurately
3,043 downloads ecology

Zoe L Getman-Pickering, Adam Campbell, Nicholas Aflitto, Todd Ugine, Ari Grele, Julie Davis

1. In both basic and applied studies, quantification of herbivory on foliage is a key metric in characterizing plant-herbivore interactions, which underpin many ecological, evolutionary, and agricultural processes. Current methods of quantifying herbivory are slow or inaccurate. We present LeafByte, a free iOS application for measuring leaf area and herbivory. LeafByte can save data automatically, read and record barcodes, handle both light and dark colored plant tissue, and be used non-destructively. 2. We evaluate its accuracy and efficiency relative to existing herbivory assessment tools. 3. LeafByte has the same accuracy as ImageJ, the field standard, but is 50% faster. Other tools, such as BioLeaf and grid quantification, are quick and accurate, but limited in the information they can provide. Visual estimation is quickest, but it only provides a coarse measure of leaf damage and tends to overestimate herbivory. 4. LeafByte is a quick and accurate means of measuring leaf area and herbivory, making it a useful tool for research in fields such as ecology, entomology, agronomy, and plant science.

8: Correlative three-dimensional super-resolution and block face electron microscopy of whole vitreously frozen cells
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Posted to bioRxiv 18 Sep 2019

Correlative three-dimensional super-resolution and block face electron microscopy of whole vitreously frozen cells
2,910 downloads cell biology

David P Hoffman, Gleb Shtengel, C. Shan Xu, Kirby R. Campbell, Melanie Freeman, Lei Wang, Daniel E Milkie, H. Amalia Pasolli, Nirmala Iyer, John Bogovic, Daniel R. Stabley, Abbas Shirinifard, Song Pang, David Peale, Kathy Schaefer, Wim Pomp, Chi-Lun Chang, Jennifer Lippincott-Schwartz, Tom Kirchhausen, David Joseph Solecki, Eric Betzig, Harald F. Hess

Living cells function through the spatial compartmentalization of thousands of distinct proteins serving a multitude of diverse biochemical needs. Correlative super-resolution (SR) fluorescence and electron microscopy (EM) has emerged as a pathway to directly view nanoscale protein relationships to the underlying global ultrastructure, but has traditionally suffered from tradeoffs of structure preservation, fluorescence retention, resolution, and field of view. We developed a platform for three-dimensional correlative cryogenic SR and focused ion beam milled block-face EM across entire vitreously frozen cells that addresses these issues by preserving native ultrastructure and enabling independent SR and EM workflow optimization. Application to a variety of biological systems revealed a number of unexpected protein-ultrastructure relationships and underscored the value of a comprehensive multimodal view of ultrastructural variability across whole cells.

9: An association between sexes of successive siblings in the data from Demographic and Health Survey program
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Posted to bioRxiv 12 Nov 2015

An association between sexes of successive siblings in the data from Demographic and Health Survey program
2,733 downloads physiology

Mikhail Monakhov

The prediction of future child's sex is a question of keen public interest. The probability of having a child of either sex is close to 50%, although multiple factors may slightly change this value. Some demographic studies suggested that sex determination can be influenced by previous pregnancies, although this hypothesis was not commonly accepted. This paper explores the correlations between siblings' sexes using data from the Demographic and Health Survey program. In the sample of about 2,214,601 women (7,985,855 children), the frequencies of sibships with multiple siblings of the same sex were significantly higher than can be expected by chance. A formal modelling demonstrated that sexes of the children were dependent on three kinds of sex ratio variation: a variation between families (Lexian), a variation within a family (Poisson) and a variation contingent upon the sex of preceding sibling (Markovian). There was a positive correlation between the sexes of successive siblings (coefficient = 0.067, p < 0.001), i.e. a child was more likely to be of the same sex as its preceding sibling. This correlation could be caused by secondary sex ratio adjustment in utero since the effect was decreasing with the length of birth-to-birth interval, and the birth-to-birth interval was longer for siblings with unlike sex.

10: Isolation of an archaeon at the prokaryote-eukaryote interface
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Posted to bioRxiv 06 Aug 2019

Isolation of an archaeon at the prokaryote-eukaryote interface
2,416 downloads microbiology

Hiroyuki Imachi, Masaru K Nobu, Nozomi Nakahara, Yuki Morono, Miyuki Ogawara, Yoshihiro Takaki, Yoshinori Takano, Katsuyuki Uematsu, Tetsuro Ikuta, Motoo Ito, Yohei Matsui, Masayuki Miyazaki, Kazuyoshi Murata, Yumi Saito, Sanae Sakai, Chihong Song, Eiji Tasumi, Yuko Yamanaka, Takashi Yamaguchi, Yoichi Kamagata, Hideyuki Tamaki, Ken Takai

The origin of eukaryotes remains enigmatic. Current data suggests that eukaryotes may have risen from an archaeal lineage known as "Asgard archaea". Despite the eukaryote-like genomic features found in these archaea, the evolutionary transition from archaea to eukaryotes remains unclear due to the lack of cultured representatives and corresponding physiological insight. Here we report the decade-long isolation of a Lokiarchaeota-related Asgard archaeon from deep marine sediment. The archaeon, " Candidatus Prometheoarchaeum syntrophicum strain MK-D1", is an anaerobic, extremely slow-growing, small cocci (~550 nm), that degrades amino acids through syntrophy. Although eukaryote-like intracellular complexities have been proposed for Asgard archaea, the isolate has no visible organella-like structure. Ca . P. syntrophicum instead displays morphological complexity - unique long, and often, branching protrusions. Based on cultivation and genomics, we propose an "Entangle-Engulf-Enslave (E3) model" for eukaryogenesis through archaea-alphaproteobacteria symbiosis mediated by the physical complexities and metabolic dependency of the hosting archaeon.

11: Report of Partial findings from the National Toxicology Program Carcinogenesis Studies of Cell Phone Radiofrequency Radiation in Hsd: Sprague Dawley® SD rats (Whole Body Exposure)
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Posted to bioRxiv 26 May 2016

Report of Partial findings from the National Toxicology Program Carcinogenesis Studies of Cell Phone Radiofrequency Radiation in Hsd: Sprague Dawley® SD rats (Whole Body Exposure)
2,302 downloads cancer biology

Michael Wyde, Mark Cesta, Chad Blystone, Susan Elmore, Paul Foster, Michelle Hooth, Grace Kissling, David Malarkey, Robert Sills, Matthew Stout, Nigel Walker, Kristine Witt, Mary Wolfe, John Bucher

The U.S. National Toxicology Program (NTP) has carried out extensive rodent toxicology and carcinogenesis studies of radiofrequency radiation (RFR) at frequencies and modulations used in the U.S. telecommunications industry. This report presents partial findings from these studies. The occurrences of two tumor types in male Harlan Sprague Dawley rats exposed to RFR, malignant gliomas in the brain and schwannomas of the heart, were considered of particular interest and are the subject of this report. The findings in this report were reviewed by expert peer reviewers selected by the NTP and National Institutes of Health (NIH). These reviews and responses to comments are included as appendices to this report, and revisions to the current document have incorporated and addressed these comments. When the studies are completed, they will undergo additional peer review before publication in full as part of the NTP's Toxicology and Carcinogenesis Technical Reports Series. No portion of this work has been submitted for publication in a scientific journal. Supplemental information in the form of four additional manuscripts has or will soon be submitted for publication. These manuscripts describe in detail the designs and performance of the RFR exposure system, the dosimetry of RFR exposures in rats and mice, the results to a series of pilot studies establishing the ability of the animals to thermoregulate during RFR exposures, and studies of DNA damage. (1) Capstick M, Kuster N, Kuhn S, Berdinas-Torres V, Wilson P, Ladbury J, Koepke G, McCormick D, Gauger J, and Melnick R. A radio frequency radiation reverberation chamber exposure system for rodents; (2) Yijian G, Capstick M, McCormick D, Gauger J, Horn T, Wilson P, Melnick RL, and Kuster N. Life time dosimetric assessment for mice and rats exposed to cell phone radiation; (3) Wyde ME, Horn TL, Capstick M, Ladbury J, Koepke G, Wilson P, Stout MD, Kuster N, Melnick R, Bucher JR, and McCormick D. Pilot studies of the National Toxicology Program's cell phone radiofrequency radiation reverberation chamber exposure system; (4) Smith-Roe SL, Wyde ME, Stout MD, Winters J, Hobbs CA, Shepard KG, Green A, Kissling GE, Tice RR, Bucher JR, and Witt KL. Evaluation of the genotoxicity of cell phone radiofrequency radiation in male and female rats and mice following subchronic exposure.

12: The GTEx Consortium atlas of genetic regulatory effects across human tissues
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Posted to bioRxiv 03 Oct 2019

The GTEx Consortium atlas of genetic regulatory effects across human tissues
2,250 downloads genetics

Francois Aguet, Alvaro N Barbeira, Rodrigo Bonazzola, Andrew Brown, Stephane E Castel, Brian Jo, Silva Kasela, Sarah Kim-Hellmuth, Yanyu Liang, Meritxell Oliva, Princy E Parsana, Elise Flynn, Laure Fresard, Eric R Gaamzon, Andrew R Hamel, Yuan He, Farhad Hormozdiari, Pejman Mohammadi, Manuel Muñoz-Aguirre, YoSon Park, Ashis Saha, Ayellet V Segrć, Benjamin J. Strober, Xiaoquan Wen, Valentin Wucher, Sayantan Das, Diego Garrido-Martín, Nicole R Gay, Robert E Handsaker, Paul J. Hoffman, Seva Kashin, Alan Kwong, Xiao Li, Daniel MacArthur, John M Rouhana, Matthew Stephens, Ellen Todres, Ana Viñuela, Gao Wang, Yuxin Zou, The GTEx Consortium, Christopher D Brown, Nancy Cox, Emmanouil Dermitzakis, Barbara E Engelhardt, Gad Getz, Roderic Guigo, Stephen B. Montgomery, Barbara E. Stranger, Hae Kyung Im, Alexis Battle, Kristin Ardlie, Tuuli Lappalainen

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

13: Molecular Atlas Of The Adult Mouse Brain
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Posted to bioRxiv 27 Sep 2019

Molecular Atlas Of The Adult Mouse Brain
1,978 downloads neuroscience

Cantin Ortiz, Jose Fernandez Navarro, Aleksandra Jurek, Antje Martin, Joakim Lundeberg, Konstantinos Meletis

Brain maps are essential for integrating information and interpreting the structure-function relationship of circuits and behavior. We aimed to generate a systematic classification of the adult mouse brain organization based on unbiased extraction of spatially-defining features. Applying whole-brain spatial transcriptomics, we captured the gene expression signatures to define the spatial organization of molecularly discrete subregions. We found that the molecular code contained sufficiently detailed information to directly deduce the complex spatial organization of the brain. This unsupervised molecular classification revealed new area- and layer-specific subregions, for example in isocortex and hippocampus, and a new division of striatum. The whole-brain molecular atlas further supports the identification of the spatial origin of single neurons using their gene expression profile, and forms the foundation to define a minimal gene set (a brain palette) that is sufficient to spatially annotate the adult brain. In summary, we have established a new molecular atlas to formally define the identity of brain regions, and a molecular code for mapping and targeting of discrete neuroanatomical domains.

14: A guide to performing Polygenic Risk Score analyses
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Posted to bioRxiv 14 Sep 2018

A guide to performing Polygenic Risk Score analyses
1,942 downloads genomics

Shing Wan Choi, Timothy Mak, Paul F O'Reilly

The application of polygenic risk scores (PRS) has become routine in genetic epidemiological studies. Among a range of applications, PRS are commonly used to assess shared aetiology among different phenotypes and to evaluate the predictive power of genetic data, while they are also now being exploited as part of study design, in which experiments are performed on individuals, or their biological samples (eg. tissues, cells), at the tails of the PRS distribution and contrasted. As GWAS sample sizes increase and PRS become more powerful, they are also set to play a key role in personalised medicine. Despite their growing application and importance, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here we provide detailed guidelines for performing polygenic risk score analyses relevant to different methods for their calculation, outlining standard quality control steps and offering recommendations for best-practice. We also discuss different methods for the calculation of PRS, common misconceptions regarding the interpretation of results and future challenges.

15: Sex Chromosome Dosage Effects On Gene Expression In Humans
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Posted to bioRxiv 14 May 2017

Sex Chromosome Dosage Effects On Gene Expression In Humans
1,893 downloads genomics

Armin Raznahan, Neelroop Parikshak, Vijayendran Chandran, Jonathan Blumenthal, Liv Clasen, Aaron Alexander-Bloch, Andrew Zinn, Danny Wangsa, Jasen Wise, Declan Murphy, Patrick Bolton, Thomas Ried, Judith Ross, Jay Giedd, Daniel Geschwind

A fundamental question in the biology of sex-differences has eluded direct study in humans: how does sex chromosome dosage (SCD) shape genome function? To address this, we developed a systematic map of SCD effects on gene function by analyzing genome-wide expression data in humans with diverse sex chromosome aneuploidies (XO, XXX, XXY, XYY, XXYY). For sex chromosomes, we demonstrate a pattern of obligate dosage sensitivity amongst evolutionarily preserved X-Y homologs, and update prevailing theoretical models for SCD compensation by detecting X-linked genes whose expression increases with decreasing X- and/or Y-chromosome dosage. We further show that SCD-sensitive sex chromosome genes regulate specific co-expression networks of SCD-sensitive autosomal genes with critical cellular functions and a demonstrable potential to mediate previously documented SCD effects on disease. Our findings detail wide-ranging effects of SCD on genome function with implications for human phenotypic variation.

16: GeneWalk identifies relevant gene functions for a biological context using network representation learning
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Posted to bioRxiv 05 Sep 2019

GeneWalk identifies relevant gene functions for a biological context using network representation learning
1,843 downloads bioinformatics

Robert Ietswaart, Benjamin M Gyori, John A Bachman, Peter K Sorger, L. Stirling Churchman

The primary bottleneck in high-throughput genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Existing methods such as Gene Ontology (GO) enrichment analysis provide insight at the gene set level. For individual genes, GO annotations are static and biological context can only be added by manual literature searches. Here, we introduce GeneWalk (github.com/churchmanlab/genewalk), a method that identifies individual genes and their relevant functions under a particular experimental condition. After automatic assembly of an experiment-specific gene regulatory network, GeneWalk quantifies the similarity between vector representations of each gene and its GO annotations through representation learning, yielding annotation significance scores that reflect their functional relevance for the experimental context. We demonstrate the use of GeneWalk analysis of RNA-seq and nascent transcriptome (NET-seq) data from human cells and mouse brains, validating the methodology. By performing gene- and condition-specific functional analysis that converts a list of genes into data-driven hypotheses, GeneWalk accelerates the interpretation of high-throughput genetics experiments.

17: Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq
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Posted to bioRxiv 09 Sep 2019

Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq
1,823 downloads genomics

Valentine Svensson, Eduardo da Veiga Beltrame, Lior Pachter

The allocation of a sequencing budget when designing single cell RNA-seq experiments requires consideration of the tradeoff between number of cells sequenced and the read depth per cell. One approach to the problem is to perform a power analysis for a univariate objective such as differential expression. However, many of the goals of single-cell analysis requires consideration of the multivariate structure of gene expression, such as clustering. We introduce an approach to quantifying the impact of sequencing depth and cell number on the estimation of a multivariate generative model for gene expression that is based on error analysis in the framework of a variational autoencoder. We find that at shallow depths, the marginal benefit of deeper sequencing per cell significantly outweighs the benefit of increased cell numbers. Above about 15,000 reads per cell the benefit of increased sequencing depth is minor. Code for the workflow reproducing the results of the paper is available at https://github.com/pachterlab/SBP_2019/.

18: Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
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Posted to bioRxiv 14 Mar 2019

Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
1,701 downloads genomics

Christoph Hafemeister, Rahul Satija

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from 'regularized negative binomial regression', where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation, and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform (https://github.com/ChristophH/sctransform), with a direct interface to our single-cell toolkit Seurat.

19: The Tolman-Eichenbaum Machine: Unifying space and relational memory through generalisation in the hippocampal formation
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Posted to bioRxiv 16 Sep 2019

The Tolman-Eichenbaum Machine: Unifying space and relational memory through generalisation in the hippocampal formation
1,668 downloads neuroscience

James C.R. Whittington, Timothy H. Muller, Shirley Mark, Guifen Chen, Caswell Barry, Neil Burgess, Timothy E.J. Behrens

The hippocampal-entorhinal system is important for spatial and relational memory tasks. We formally link these domains; provide a mechanistic understanding of the hippocampal role in generalisation; and offer unifying principles underlying many entorhinal and hippocampal cell-types. We propose medial entorhinal cells form a basis describing structural knowledge, and hippocampal cells link this basis with sensory representations. Adopting these principles, we introduce the Tolman-Eichenbaum machine (TEM). After learning, TEM entorhinal cells include grid, band, border and object-vector cells. Hippocampal cells include place and landmark cells, remapping between environments. Crucially, TEM also predicts empirically recorded representations in complex non-spatial tasks. TEM predicts hippocampal remapping is not random as previously believed. Rather structural knowledge is preserved across environments. We confirm this in simultaneously recorded place and grid cells. One Sentence Summary Simple principles of representation and generalisation unify spatial and non-spatial accounts of hippocampus and explain many cell representations.

20: Pan-cancer classifications of tumor histological images using deep learning
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Posted to bioRxiv 26 Jul 2019

Pan-cancer classifications of tumor histological images using deep learning
1,639 downloads bioinformatics

Javad Noorbakhsh, Saman Farahmand, Mohammad Soltanieh-ha, Sandeep Namburi, Kourosh Zarringhalam, Jeff Chuang

Histopathological images are essential for the diagnosis of cancer type and selection of optimal treatment. However, the current clinical process of manual inspection of images is time consuming and prone to intra- and inter-observer variability. Here we show that key aspects of cancer image analysis can be performed by deep convolutional neural networks (CNNs) across a wide spectrum of cancer types. In particular, we implement CNN architectures based on Google Inception v3 transfer learning to analyze 27815 H&E slides from 23 cohorts in The Cancer Genome Atlas in studies of tumor/normal status, cancer subtype, and mutation status. For 19 solid cancer types we are able to classify tumor/normal status of whole slide images with extremely high AUCs (0.995±0.008). We are also able to classify cancer subtypes within 10 tissue types with AUC values well above random expectations (micro-average 0.87±0.1). We then perform a cross-classification analysis of tumor/normal status across tumor types. We find that classifiers trained on one type are often effective in distinguishing tumor from normal in other cancer types, with the relationships among classifiers matching known cancer tissue relationships. For the more challenging problem of mutational status, we are able to classify TP53 mutations in three cancer types with AUCs from 0.65-0.80 using a fully-trained CNN, and with similar cross-classification accuracy across tissues. These studies demonstrate the power of CNNs for not only classifying histopathological images in diverse cancer types, but also for revealing shared biology between tumors. We have made software available at: https://github.com/javadnoorb/HistCNN

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