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Rxivist combines biology preprints from bioRxiv and medRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 150,124 papers from 632,984 authors.

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in category genomics

7,451 results found. For more information, click each entry to expand.

1: SARS-CoV-2 RNA reverse-transcribed and integrated into the human genome
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Posted 13 Dec 2020

SARS-CoV-2 RNA reverse-transcribed and integrated into the human genome
129,499 downloads bioRxiv genomics

Liguo Zhang, Alexsia Richards, Andrew Khalil, Emile Wogram, Haiting Ma, Richard A. Young, Rudolf Jaenisch

Prolonged SARS-CoV-2 RNA shedding and recurrence of PCR-positive tests have been widely reported in patients after recovery, yet these patients most commonly are non-infectious. Here we investigated the possibility that SARS-CoV-2 RNAs can be reverse-transcribed and integrated into the human genome and that transcription of the integrated sequences might account for PCR-positive tests. In support of this hypothesis, we found chimeric transcripts consisting of viral fused to cellular sequences in published data sets of SARS-CoV-2 infected cultured cells and primary cells of patients, consistent with the transcription of viral sequences integrated into the genome. To experimentally corroborate the possibility of viral retro-integration, we describe evidence that SARS-CoV-2 RNAs can be reverse transcribed in human cells by reverse transcriptase (RT) from LINE-1 elements or by HIV-1 RT, and that these DNA sequences can be integrated into the cell genome and subsequently be transcribed. Human endogenous LINE-1 expression was induced upon SARS-CoV-2 infection or by cytokine exposure in cultured cells, suggesting a molecular mechanism for SARS-CoV-2 retro-integration in patients. This novel feature of SARS-CoV-2 infection may explain why patients can continue to produce viral RNA after recovery and suggests a new aspect of RNA virus replication.

2: The Genomic Formation of South and Central Asia
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Posted 31 Mar 2018

The Genomic Formation of South and Central Asia
102,274 downloads bioRxiv genomics

Vagheesh M. Narasimhan, Nick Patterson, Priya Moorjani, Iosif Lazaridis, Mark Lipson, Swapan Mallick, Nadin Rohland, Rebecca Bernardos, Alexander M Kim, Nathan Joel 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 M 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.

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

A guide to performing Polygenic Risk Score analyses
47,842 downloads bioRxiv genomics

Shing Wan Choi, Timothy Shin Heng 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.

4: Patterns of genetic differentiation and the footprints of historical migrations in the Iberian Peninsula
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Posted 12 Mar 2018

Patterns of genetic differentiation and the footprints of historical migrations in the Iberian Peninsula
46,306 downloads bioRxiv genomics

Clare Bycroft, Ceres Fernandez-Rozadilla, Clara Ruiz-Ponte, Inés Quintela-García, Angel Carracedo, Peter Donnelly, Simon Myers

Genetic differences within or between human populations (population structure) has been studied using a variety of approaches over many years. Recently there has been an increasing focus on studying genetic differentiation at fine geographic scales, such as within countries. Identifying such structure allows the study of recent population history, and identifies the potential for confounding in association studies, particularly when testing rare, often recently arisen variants. The Iberian Peninsula is linguistically diverse, has a complex demographic history, and is unique among European regions in having a centuries-long period of Muslim rule. Previous genetic studies of Spain have examined either a small fraction of the genome or only a few Spanish regions. Thus, the overall pattern of fine-scale population structure within Spain remains uncharacterised. Here we analyse genome-wide genotyping array data for 1,413 Spanish individuals sampled from all regions of Spain. We identify extensive fine-scale structure, down to unprecedented scales, smaller than 10 Km in some places. We observe a major axis of genetic differentiation that runs from east to west of the peninsula. In contrast, we observe remarkable genetic similarity in the north-south direction, and evidence of historical north-south population movement. Finally, without making particular prior assumptions about source populations, we show that modern Spanish people have regionally varying fractions of ancestry from a group most similar to modern north Moroccans. The north African ancestry results from an admixture event, which we date to 860 - 1120 CE, corresponding to the early half of Muslim rule. Our results indicate that it is possible to discern clear genetic impacts of the Muslim conquest and population movements associated with the subsequent Reconquista.

5: Comprehensive integration of single cell data
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Posted 02 Nov 2018

Comprehensive integration of single cell data
45,062 downloads bioRxiv genomics

Tim Stuart, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M Mauck, Marlon Stoeckius, Peter Smibert, Rahul Satija

Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets. Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

6: Creating a universal SNP and small indel variant caller with deep neural networks
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Posted 14 Dec 2016

Creating a universal SNP and small indel variant caller with deep neural networks
39,603 downloads bioRxiv genomics

Ryan Poplin, Pi-Chuan Chang, David Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Dan Newburger, Jojo Dijamco, Nam Nguyen, Pegah T. Afshar, Sam S. Gross, Lizzie Dorfman, Cory McLean, Mark A. DePristo

Next-generation sequencing (NGS) is a rapidly evolving set of technologies that can be used to determine the sequence of an individual's genome by calling genetic variants present in an individual using billions of short, errorful sequence reads. Despite more than a decade of effort and thousands of dedicated researchers, the hand-crafted and parameterized statistical models used for variant calling still produce thousands of errors and missed variants in each genome. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships (likelihoods) between images of read pileups around putative variant sites and ground-truth genotype calls. This approach, called DeepVariant, outperforms existing tools, even winning the "highest performance" award for SNPs in a FDA-administered variant calling challenge. The learned model generalizes across genome builds and even to other species, allowing non-human sequencing projects to benefit from the wealth of human ground truth data. We further show that, unlike existing tools which perform well on only a specific technology, DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, from deep whole genomes from 10X Genomics to Ion Ampliseq exomes. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.

7: Introductions and early spread of SARS-CoV-2 in France
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Posted 24 Apr 2020

Introductions and early spread of SARS-CoV-2 in France
37,939 downloads bioRxiv genomics

Fabiana Gámbaro, Sylvie Behillil, Artem Baidaliuk, Flora Donati, Mélanie Albert, Andreea Alexandru, Maud Vanpeene, Méline Bizard, Angela Brisebarre, Marion Barbet, Fawzi Derrar, Sylvie van der Werf, Vincent Enouf, Etienne Simon-Loriere

Following the emergence of coronavirus disease (COVID-19) in Wuhan, China in December 2019, specific COVID-19 surveillance was launched in France on January 10, 2020. Two weeks later, the first three imported cases of COVID-19 into Europe were diagnosed in France. We sequenced 97 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from samples collected between January 24 and March 24, 2020 from infected patients in France. Phylogenetic analysis identified several early independent SARS-CoV-2 introductions without local transmission, highlighting the efficacy of the measures taken to prevent virus spread from symptomatic cases. In parallel, our genomic data reveals the later predominant circulation of a major clade in many French regions, and implies local circulation of the virus in undocumented infections prior to the wave of COVID-19 cases. This study emphasizes the importance of continuous and geographically broad genomic sequencing and calls for further efforts with inclusion of asymptomatic infections. ### Competing Interest Statement The authors have declared no competing interest.

8: The major genetic risk factor for severe COVID-19 is inherited from Neandertals
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Posted 03 Jul 2020

The major genetic risk factor for severe COVID-19 is inherited from Neandertals
36,868 downloads bioRxiv genomics

Hugo Zeberg, Svante Pääbo

A recent genetic association study (Ellinghaus et al. 2020) identified a gene cluster on chromosome 3 as a risk locus for respiratory failure in SARS-CoV-2. Recent data comprising 3,199 hospitalized COVID-19 patients and controls reproduce this and find that it is the major genetic risk factor for severe SARS-CoV-2 infection and hospitalization (COVID-19 Host Genetics Initiative). Here, we show that the risk is conferred by a genomic segment of ~50 kb that is inherited from Neandertals and occurs at a frequency of ~30% in south Asia and ~8% in Europe. ### Competing Interest Statement The authors have declared no competing interest.

9: Common methods for fecal sample storage in field studies yield consistent signatures of individual identity in microbiome sequencing data
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Posted 04 Feb 2016

Common methods for fecal sample storage in field studies yield consistent signatures of individual identity in microbiome sequencing data
33,781 downloads bioRxiv genomics

Ran Blekhman, Karen Tang, Elizabeth A. Archie, Luis B Barreiro, Zachary P Johnson, Mark E Wilson, Jordan Kohn, Michael L Yuan, Laurence Gesquiere, Laura E. Grieneisen, Jenny Tung

Field studies of wild vertebrates are frequently associated with extensive collections of banked fecal samples, which are often collected from known individuals and sometimes also sampled longitudinally across time. Such collections represent unique resources for understanding ecological, behavioral, and phylogenetic effects on the gut microbiome, especially for species of particular conservation concern. However, we do not understand whether sample storage methods confound the ability to investigate interindividual variation in gut microbiome profiles. This uncertainty arises in part because comparisons across storage methods to date generally include only a few (≤5) individuals, or analyze pooled samples. Here, we used n=52 samples from 13 rhesus macaque individuals to compare immediate freezing, the gold standard of preservation, to three methods commonly used in vertebrate field studies: storage in ethanol, lyophilization following ethanol storage, and storage in RNAlater. We found that the signature of individual identity consistently outweighed storage effects: alpha diversity and beta diversity measures were significantly correlated across methods, and while samples often clustered by donor, they never clustered by storage method. Provided that all analyzed samples are stored the same way, banked fecal samples therefore appear highly suitable for investigating variation in gut microbiota. Our results open the door to a much-expanded perspective on variation in the gut microbiome across species and ecological contexts.

10: The complete sequence of a human genome
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Posted 27 May 2021

The complete sequence of a human genome
31,502 downloads bioRxiv genomics

Sergey Nurk, Sergey Koren, Arang Rhie, Mikko Rautiainen, Andrey V. Bzikadze, Alla Mikheenko, Mitchell R. Vollger, Nicolas Altemose, Lev Uralsky, Ariel Gershman, Sergey Aganezov, Savannah J. Hoyt, Mark Diekhans, Glennis A. Logsdon, Michael Alonge, Stylianos E. Antonarakis, Matthew Borchers, Gerard G Bouffard, Shelise Y. Brooks, Gina V. Caldas, Haoyu Cheng, Chen-Shan Chin, William Chow, Leonardo G. de Lima, Philip C. Dishuck, Richard Durbin, Tatiana Dvorkina, Ian T. Fiddes, Giulio Formenti, Robert S Fulton, Arkarachai Fungtammasan, Erik Garrison, Patrick G. S. Grady, Tina A Graves-Lindsay, Ira M. Hall, Nancy F Hansen, Gabrielle A. Hartley, Marina Haukness, Kerstin Howe, Michael W Hunkapiller, Chirag Jain, Miten Jain, Erich D Jarvis, Peter Kerpedjiev, Melanie Kirsche, Mikhail Kolmogorov, Jonas Korlach, Milinn Kremitzki, Heng Li, Valerie V. Maduro, Tobias Marschall, Ann M McCartney, Jennifer McDaniel, Danny E Miller, James C Mullikin, Eugene W Myers, Nathan D. Olson, Benedict Paten, Paul Peluso, Pavel A. Pevzner, David Porubsky, Tamara Potapova, Evgeny I. Rogaev, Jeffrey Rosenfeld, Steven Salzberg, Valerie A Schneider, Fritz J. Sedlazeck, Kishwar Shafin, Colin J. Shew, Alaina Shumate, Yumi Sims, Arian F. A. Smit, Daniela C Soto, Ivan Sovic, Jessica M. Storer, Aaron Streets, Beth A. Sullivan, Francoise Thibaud-Nissen, James Torrance, Justin Wagner, Brian P Walenz, Aaron Wenger, Jonathan M D Wood, Chunlin Xiao, Stephanie M. Yan, Alice C. Young, Samantha Zarate, Urvashi Surti, Rajiv C. McCoy, Megan Y. Dennis, Ivan A. Alexandrov, Jennifer L. Gerton, Rachel J. O'Neill, Winston Timp, Justin M. Zook, Michael C Schatz, Evan E. Eichler, Karen H Miga, Adam M. Phillippy

In 2001, Celera Genomics and the International Human Genome Sequencing Consortium published their initial drafts of the human genome, which revolutionized the field of genomics. While these drafts and the updates that followed effectively covered the euchromatic fraction of the genome, the heterochromatin and many other complex regions were left unfinished or erroneous. Addressing this remaining 8% of the genome, the Telomere-to-Telomere (T2T) Consortium has finished the first truly complete 3.055 billion base pair (bp) sequence of a human genome, representing the largest improvement to the human reference genome since its initial release. The new T2T-CHM13 reference includes gapless assemblies for all 22 autosomes plus chromosome X, corrects numerous errors, and introduces nearly 200 million bp of novel sequence containing 2,226 paralogous gene copies, 115 of which are predicted to be protein coding. The newly completed regions include all centromeric satellite arrays and the short arms of all five acrocentric chromosomes, unlocking these complex regions of the genome to variational and functional studies for the first time.

11: The mutational constraint spectrum quantified from variation in 141,456 humans
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Posted 28 Jan 2019

The mutational constraint spectrum quantified from variation in 141,456 humans
24,822 downloads bioRxiv genomics

Konrad Karczewski, Laurent C Francioli, Grace Tiao, Beryl Cummings, Jessica Alföldi, Qingbo S Wang, Ryan L. Collins, Kristen M Laricchia, andrea ganna, Daniel P. Birnbaum, Laura D Gauthier, Harrison Brand, Matthew Solomonson, Nicholas A Watts, Daniel Rhodes, Moriel Singer-Berk, Eleina M England, Eleanor G Seaby, Jack A. Kosmicki, Raymond K Walters, Katherine Tashman, Yossi Farjoun, Eric Banks, Timothy Poterba, Arcturus Wang, Cotton Seed, Nicola Whiffin, Jessica X. Chong, Kaitlin E Samocha, Emma Pierce-Hoffman, Zachary Zappala, Anne H. O’Donnell-Luria, Eric Vallabh Minikel, Ben Weisburd, Monkol Lek, James S Ware, Christopher Vittal, Irina M Armean, Louis Bergelson, Kristian Cibulskis, Kristen M Connolly, Miguel Covarrubias, Stacey Donnelly, Steven Ferriera, Stacey Gabriel, Jeff Gentry, Namrata Gupta, Thibault Jeandet, Diane Kaplan, Christopher Llanwarne, Ruchi Munshi, Sam Novod, Nikelle Petrillo, David Roazen, Valentin Ruano-Rubio, Andrea Saltzman, Molly Schleicher, Jose Soto, Kathleen Tibbetts, Charlotte Tolonen, Gordon Wade, Michael E. Talkowski, Genome Aggregation Database (gnomAD) Consortium, Benjamin M Neale, Mark J. Daly, Daniel G MacArthur

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes critical for an organism’s function will be depleted for such variants in natural populations, while non-essential genes will tolerate their accumulation. However, predicted loss-of-function (pLoF) variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes[1][1]. Here, we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence pLoF variants in this cohort after filtering for sequencing and annotation artifacts. Using an improved human mutation rate model, we classify human protein-coding genes along a spectrum representing tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases. ### Competing Interest Statement [1]: #ref-1

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

Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
22,234 downloads bioRxiv 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.

13: Analysis of protein-coding genetic variation in 60,706 humans
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Posted 30 Oct 2015

Analysis of protein-coding genetic variation in 60,706 humans
22,088 downloads bioRxiv genomics

Exome Aggregation Consortium, Monkol Lek, Konrad Karczewski, Eric Vallabh Minikel, Kaitlin E Samocha, Eric Banks, Timothy Fennell, Anne H. O’Donnell-Luria, James S Ware, Andrew J. Hill, Beryl Cummings, Taru Tukiainen, Daniel P. Birnbaum, Jack A. Kosmicki, Laramie E Duncan, Karol Estrada, Fengmei Zhao, James Zou, Emma Pierce-Hoffman, Joanne Berghout, David N. Cooper, Nicole Deflaux, Mark DePristo, Ron Do, Jason Flannick, Menachem Fromer, Laura Gauthier, Jackie Goldstein, Namrata Gupta, Daniel Howrigan, Adam Kiezun, Mitja I. Kurki, Ami Levy Moonshine, Pradeep Natarajan, Lorena Orozco, Gina M. Peloso, Ryan Poplin, Manuel A. Rivas, Valentin Ruano-Rubio, Samuel A Rose, Douglas Ruderfer, Khalid Shakir, Peter D. Stenson, Christine Stevens, Brett P Thomas, Grace Tiao, Maria T Tusie-Luna, Ben Weisburd, Hong-Hee Won, Dongmei Yu, David M Altshuler, Diego Ardissino, Michael Boehnke, John Danesh, Stacey Donnelly, Roberto Elosua, Jose C. Florez, Stacey B Gabriel, Gad Getz, Stephen J Glatt, Christina M Hultman, Sekar Kathiresan, Markku Laakso, Steven McCarroll, Mark I McCarthy, Dermot McGovern, Ruth McPherson, Benjamin Neale, Aarno Palotie, Shaun M Purcell, Danish Saleheen, Jeremiah Scharf, Pamela Sklar, Patrick F Sullivan, Jaakko Tuomilehto, Ming T. Tsuang, Hugh C Watkins, James G Wilson, Mark J. Daly, Daniel G MacArthur

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). The resulting catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We show that this catalogue can be used to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; we identify 3,230 genes with near-complete depletion of truncating variants, 72% of which have no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human knockout variants in protein-coding genes.

14: When to use Quantile Normalization?
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Posted 04 Dec 2014

When to use Quantile Normalization?
20,691 downloads bioRxiv genomics

Stephanie C Hicks, Rafael A. Irizarry

Normalization and preprocessing are essential steps for the analysis of high-throughput data including next-generation sequencing and microarrays. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation from noisy data. These methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Transforming the data to remove these differences has the potential to remove interesting biologically driven global variation and therefore may not be appropriate depending on the type and source of variation. Currently, it is up to the subject matter experts, for example biologists, to determine if the stated assumptions are appropriate or not. Here, we propose a data-driven method to test for the assumptions of global normalization methods. We demonstrate the utility of our method (quantro), by applying it to multiple gene expression and DNA methylation and show examples of when global normalization methods are not appropriate. We also perform a Monte Carlo simulation study to illustrate how our method generally outperforms the current approach. An R-package implementing our method is available on Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/quantro.html).

15: Quantitative analysis of population-scale family trees using millions of relatives
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Posted 07 Feb 2017

Quantitative analysis of population-scale family trees using millions of relatives
19,999 downloads bioRxiv genomics

Joanna Kaplanis, Assaf Gordon, Mary Wahl, Michael Gershovits, Barak Markus, Mona Sheikh, Melissa Gymrek, Gaurav Bhatia, Daniel G MacArthur, Alkes L. Price, Yaniv Erlich

Family trees have vast applications in multiple fields from genetics to anthropology and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. Here, we collected 86 million profiles from publicly-available online data from genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of longevity by inspecting millions of relative pairs and to provide insights to population genetics theories on the dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data.

16: Integrated analysis of multimodal single-cell data
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Posted 12 Oct 2020

Integrated analysis of multimodal single-cell data
19,925 downloads bioRxiv genomics

Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen, William M Mauck, Shiwei Zheng, Andrew Butler, Maddie J. Lee, Aaron J. Wilk, Charlotte Darby, Michael Zagar, Paul Hoffman, Marlon Stoeckius, Efthymia Papalexi, Eleni Mimitou, Jaison Jain, Avi Srivastava, Tim Stuart, Lamar B. Fleming, Bertrand Yeung, Angela J. Rogers, M Juliana McElrath, Catherine A. Blish, Raphael Gottardo, Peter Smibert, Rahul Satija

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce "weighted-nearest neighbor analysis", an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. Availability: Installation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat ### Competing Interest Statement In the past three years, RS has worked as a consultant for Bristol-Myers Squibb, Regeneron, and Kallyope, and served as an SAB member for ImmunAI, Apollo Life Sciences GmbH, Nanostring and the NYC Pandemic Response Lab. R.G. has received consulting income from Juno Therapeutics, Takeda, Infotech Soft, Celgene, Merck and has received research support from Janssen Pharmaceuticals and Juno Therapeutics, and declares ownership in CellSpace Biosciences. PS is a co-inventor of a patent related to this work. BZY is an employee at BioLegend Inc., which is the exclusive licensee of the New York Genome Center patent application related to this work.

17: A molecular cell atlas of the human lung from single cell RNA sequencing
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Posted 27 Aug 2019

A molecular cell atlas of the human lung from single cell RNA sequencing
19,775 downloads bioRxiv genomics

Kyle Joseph Travaglini, Ahmad N. Nabhan, Lolita Penland, Rahul Sinha, Astrid Gillich, Rene V Sit, Stephen Chang, Stephanie D Conley, Yasuo Mori, Jun Seita, Gerald J. Berry, Joseph B Shrager, Ross J Metzger, Christin S Kuo, Norma Neff, Irving L. Weissman, Stephen R Quake, Mark A Krasnow

Although single cell RNA sequencing studies have begun providing compendia of cell expression profiles, it has proven more difficult to systematically identify and localize all molecular cell types in individual organs to create a full molecular cell atlas. Here we describe droplet- and plate-based single cell RNA sequencing applied to ∼75,000 human lung and blood cells, combined with a multi-pronged cell annotation approach, which have allowed us to define the gene expression profiles and anatomical locations of 58 cell populations in the human lung, including 41 of 45 previously known cell types or subtypes and 14 new ones. This comprehensive molecular atlas elucidates the biochemical functions of lung cell types and the cell-selective transcription factors and optimal markers for making and monitoring them; defines the cell targets of circulating hormones and predicts local signaling interactions including sources and targets of chemokines in immune cell trafficking and expression changes on lung homing; and identifies the cell types directly affected by lung disease genes and respiratory viruses. Comparison to mouse identified 17 molecular types that appear to have been gained or lost during lung evolution and others whose expression profiles have been substantially altered, revealing extensive plasticity of cell types and cell-type-specific gene expression during organ evolution including expression switches between cell types. This atlas provides the molecular foundation for investigating how lung cell identities, functions, and interactions are achieved in development and tissue engineering and altered in disease and evolution.

18: Complemented palindrome small RNAs first discovered from SARS coronavirus
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Posted 07 Sep 2017

Complemented palindrome small RNAs first discovered from SARS coronavirus
19,616 downloads bioRxiv genomics

Chang Liu, Ze Chen, Wenyuan Shen, Deshui Yu, Siyu Li, Yue Hu, Haishuo Ji, Wenjun Bu, Qingsong Wang, Gao Shan

In this study, we reported for the first time the existence of complemented palindrome small RNAs (cpsRNAs) and proposed cpsRNAs and palindrome small RNAs (psRNAs) as a novel class of small RNAs. The first discovered cpsRNA UCUUUAACAAGCUUGUUAAAGA from SARS coronavirus named SARS-CoV-cpsR-22 contained 22 nucleotides perfectly matching its reverse complementary sequence. Further sequence analysis supported that SARS-CoV-cpsR-22 originated from bat betacoronavirus. The results of RNAi experiments showed that one 19-nt segment of SARS-CoV-cpsR-22 significantly induced cell apoptosis. These results suggested that SARS-CoV-cpsR-22 could play a role in SARS-CoV infection or pathogenicity. The discovery of psRNAs and cpsRNAs paved the way to find new markers for pathogen detection and reveal the mechanisms in the infection or pathogenicity from a different point of view. The discovery of psRNAs and cpsRNAs also broaden the understanding of palindrome motifs in animal of plant genomes.

19: Vibrio natriegens, a new genomic powerhouse
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Posted 12 Jun 2016

Vibrio natriegens, a new genomic powerhouse
19,169 downloads bioRxiv genomics

Henry H Lee, Nili Ostrov, Brandon G Wong, Michaela A Gold, Ahmad S Khalil, George Church

Recombinant DNA technology has revolutionized biomedical research with continual innovations advancing the speed and throughput of molecular biology. Nearly all these tools, however, are reliant on Escherichia coli as a host organism, and its lengthy growth rate increasingly dominates experimental time. Here we report the development of Vibrio natriegens, a free-living bacteria with the fastest generation time known, into a genetically tractable host organism. We systematically characterize its growth properties to establish basic laboratory culturing conditions. We provide the first complete Vibrio natriegens genome, consisting of two chromosomes of 3,248,023 bp and 1,927,310 bp that together encode 4,578 open reading frames. We reveal genetic tools and techniques for working with Vibrio natriegens. These foundational resources will usher in an era of advanced genomics to accelerate biological, biotechnological, and medical discoveries.

20: Comparative analysis of single-cell RNA sequencing methods
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Posted 05 Jan 2016

Comparative analysis of single-cell RNA sequencing methods
17,681 downloads bioRxiv genomics

Christoph Ziegenhain, Beate Vieth, Swati Parekh, Björn Reinius, Martha Smets, Heinrich Leonhardt, Ines Hellmann, Wolfgang Enard

Background: Single-cell RNA sequencing (scRNA‑seq) offers exciting possibilities to address biological and medical questions, but a systematic comparison of recently developed protocols is still lacking. Results: We generated data from 447 mouse embryonic stem cells using Drop‑seq, SCRB‑seq, Smart‑seq (on Fluidigm C1) and Smart‑seq2 and analyzed existing data from 35 mouse embryonic stem cells prepared with CEL‑seq. We find that Smart‑seq2 is the most sensitive method as it detects the most genes per cell and across cells. However, it shows more amplification noise than CEL‑seq, Drop‑seq and SCRB‑seq as it cannot use unique molecular identifiers (UMIs). We use simulations to model how the observed combinations of sensitivity and amplification noise affects detection of differentially expressed genes and find that SCRB‑seq reaches 80% power with the fewest number of cells. When considering cost-efficiency at different sequencing depths at 80% power, we find that Drop‑seq is preferable when quantifying transcriptomes of a large numbers of cells with low sequencing depth, SCRB‑seq is preferable when quantifying transcriptomes of fewer cells and Smart‑seq2 is preferable when annotating and/or quantifying transcriptomes of fewer cells as long one can use in-house produced transposase. Conclusions: Our analyses allows an informed choice among five prominent scRNA‑seq protocols and provides a solid framework for benchmarking future improvements in scRNA‑seq methodologies.

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