Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 62,725 bioRxiv papers from 278,303 authors.

Most downloaded bioRxiv papers, all time

in category genomics

4,230 results found. For more information, click each entry to expand.

61: LeafCutter: Annotation-free quantification of RNA splicing
more details view paper

Posted to bioRxiv 16 Mar 2016

LeafCutter: Annotation-free quantification of RNA splicing
7,070 downloads genomics

Yang I Li, David A. Knowles, Jack Humphrey, Alvaro N Barbeira, Scott P. Dickinson, Hae Kyung Im, Jonathan K Pritchard

The excision of introns from pre-mRNA is an essential step in mRNA processing. We developed LeafCutter to study sample and population variation in intron splicing. LeafCutter identifies variable intron splicing events from short-read RNA-seq data and finds alternative splicing events of high complexity. Our approach obviates the need for transcript annotations and circumvents the challenges in estimating relative isoform or exon usage in complex splicing events. LeafCutter can be used both for detecting differential splicing between sample groups, and for mapping splicing quantitative trait loci (sQTLs). Compared to contemporary methods, we find 1.4-2.1 times more sQTLs, many of which help us ascribe molecular effects to disease-associated variants. Strikingly, transcriptome-wide associations between LeafCutter intron quantifications and 40 complex traits increased the number of associated disease genes at 5% FDR by an average of 2.1-fold as compared to using gene expression levels alone. LeafCutter is fast, scalable, easy to use, and available at https://github.com/davidaknowles/leafcutter.

62: Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks.
more details view paper

Posted to bioRxiv 05 Oct 2015

Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks.
6,994 downloads genomics

David R Kelley, Jasper Snoek, John Rinn

The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanism. Here, we address this challenge using an approach based on a recent machine learning advance--deep convolutional neural networks (CNNs). We introduce an open source package Basset (https://github.com/davek44/Basset) to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNaseI-seq and demonstrate far greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for GWAS SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.

63: Cell freezing protocol optimized for ATAC-Seq on motor neurons derived from human induced pluripotent stem cells
more details view paper

Posted to bioRxiv 15 Jan 2016

Cell freezing protocol optimized for ATAC-Seq on motor neurons derived from human induced pluripotent stem cells
6,932 downloads genomics

Pamela Milani, Renan Escalante-Chong, Brandon C. Shelley, Natasha L. Patel-Murray, Xiaofeng Xin, Miriam Adam, Berhan Mandefro, Dhruv Sareen, Clive N. Svendsen, Ernest Fraenkel

In recent years, the assay for transposase-accessible chromatin using sequencing (ATAC-Seq) has become a fundamental tool of epigenomic research. However, it has proven difficult to perform this technique on frozen samples because freezing cells before extracting nuclei impairs nuclear integrity and alters chromatin structure. We describe a protocol for freezing cells that is compatible with ATAC-Seq, producing results that compare well with those generated from fresh cells. We found that while flash-frozen samples are not suitable for ATAC-Seq, the assay is successful with slow-cooled cryopreserved samples. Using this method, we were able to isolate high quality, intact nuclei, and we verified that epigenetic results from fresh and cryopreserved samples agree quantitatively. We developed our protocol on a disease-relevant cell type, namely motor neurons differentiated from induced pluripotent stem cells from a patient affected by spinal muscular atrophy.

64: LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies
more details view paper

Posted to bioRxiv 21 Feb 2014

LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies
6,922 downloads genomics

Brendan Bulik-Sullivan, Po-Ru Loh, Hilary Finucane, Stephan Ripke, Jian Yang, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Nick Patterson, Mark J. Daly, Alkes L. Price, Benjamin M Neale

Both polygenicity (i.e. many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield inflated distributions of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from bias and true signal from polygenicity. We have developed an approach that quantifies the contributions of each by examining the relationship between test statistics and linkage disequilibrium (LD). We term this approach LD Score regression. LD Score regression provides an upper bound on the contribution of confounding bias to the observed inflation in test statistics and can be used to estimate a more powerful correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of test statistic inflation in many GWAS of large sample size.

65: Pooled optical screens in human cells
more details view paper

Posted to bioRxiv 02 Aug 2018

Pooled optical screens in human cells
6,891 downloads genomics

David Feldman, Avtar Singh, Jonathan L Schmid-Burgk, Anja Mezger, Anthony J Garrity, Rebecca J Carlson, Feng Zhang, Paul C Blainey

Large-scale genetic screens play a key role in the systematic discovery of genes underlying cellular phenotypes. Pooling of genetic perturbations greatly increases screening throughput, but has so far been limited to screens of enrichments defined by cell fitness and flow cytometry, or to comparatively low-throughput single cell gene expression profiles. Although microscopy is a rich source of spatial and temporal information about mammalian cells, high-content imaging screens have been restricted to much less efficient arrayed formats. Here, we introduce an optical method to link perturbations and their phenotypic outcomes at the single-cell level in a pooled setting. Barcoded perturbations are read out by targeted in situ sequencing following image-based phenotyping. We apply this technology to screen a focused set of 952 genes across >3 million cells for involvement in NF-κB activation by imaging the translocation of RelA (p65) to the nucleus, recovering 20 known pathway components and 3 novel candidate positive regulators of IL-1β and TNFα-stimulated immune responses.

66: Generative adversarial networks simulate gene expression and predict perturbations in single cells
more details view paper

Posted to bioRxiv 08 Feb 2018

Generative adversarial networks simulate gene expression and predict perturbations in single cells
6,788 downloads genomics

Arsham Ghahramani, Fiona M Watt, Nicholas M. Luscombe

Recent advances have enabled gene expression profiling of single cells at lower cost. As more data is produced there is an increasing need to integrate diverse datasets and better analyse underutilised data to gain biological insights. However, analysis of single cell RNA-seq data is challenging due to biological and technical noise which not only varies between laboratories but also between batches. Here for the first time, we apply a new generative deep learning approach called Generative Adversarial Networks (GAN) to biological data. We apply GANs to epidermal, neural and hematopoietic scRNA-seq data spanning different labs and experimental protocols. We show that it is possible to integrate diverse scRNA-seq datasets and in doing so, our generative model is able to simulate realistic scRNA-seq data that covers the full diversity of cell types. In contrast to many machine-learning approaches, we are able to interpret internal parameters in a biologically meaningful manner. Using our generative model we are able to obtain a universal representation of epidermal differentiation and use this to predict the effect of cell state perturbations on gene expression at high time-resolution. We show that our trained neural networks identify biological state-determining genes and through analysis of these networks we can obtain inferred gene regulatory relationships. Finally, we use internal GAN learned features to perform dimensionality reduction. In combination these attributes provide a powerful framework to progress the analysis of scRNA-seq data beyond exploratory analysis of cell clusters and towards integration of multiple datasets regardless of origin.

67: CUT&Tag for efficient epigenomic profiling of small samples and single cells
more details view paper

Posted to bioRxiv 06 Mar 2019

CUT&Tag for efficient epigenomic profiling of small samples and single cells
6,754 downloads genomics

Hatice S. Kaya-Okur, Steven J. Wu, Christine A. Codomo, Erica S. Pledger, Terri D Bryson, Steven Henikoff, Kami Ahmad

Many chromatin features play critical roles in regulating gene expression. A complete understanding of gene regulation will require the mapping of specific chromatin features in small samples of cells at high resolution. Here we describe Cleavage Under Targets and Tagmentation (CUT&Tag), an enzyme-tethering strategy that provides efficient high-resolution sequencing libraries for profiling diverse chromatin components. In CUT&Tag, a chromatin protein is bound in situ by a specific antibody, which then tethers a protein A-Tn5 transposase fusion protein. Activation of the transposase efficiently generates fragment libraries with high resolution and exceptionally low background. All steps from live cells to sequencing-ready libraries can be performed in a single tube on the benchtop or a microwell in a high-throughput pipeline, and the entire procedure can be performed in one day. We demonstrate the utility of CUT&Tag by profiling histone modifications, RNA Polymerase II and transcription factors on low cell numbers and single cells.

68: An open resource of structural variation for medical and population genetics
more details view paper

Posted to bioRxiv 14 Mar 2019

An open resource of structural variation for medical and population genetics
6,715 downloads genomics

Ryan L Collins, Harrison Brand, Konrad J. Karczewski, Xuefang Zhao, Jessica Alföldi, Amit V Khera, Laurent C Francioli, Laura D Gauthier, Harold Wang, Nicholas A Watts, Matthew Solomonson, Anne O’Donnell-Luria, Alexander Baumann, Ruchi Munshi, Chelsea Lowther, Mark Walker, Christopher Whelan, Yongqing Huang, Ted Brookings, Ted Sharpe, Matthew R Stone, Elise Valkanas, Jack Fu, Grace Tiao, Kristen M Laricchia, Christine Stevens, Namrata Gupta, Lauren Margolin, The Genome Aggregation Database (gnomAD) Production Team, The gnomAD Consortium, John A Spertus, Kent D Taylor, Henry J Lin, Stephen S Rich, Wendy Post, Yii-Der Ida Chen, Jerome I Rotter, Chad Nusbaum, Anthony Philippakis, Eric Lander, Stacey Gabriel, Benjamin M Neale, Sekar Kathiresan, Mark J. Daly, Eric Banks, Daniel G. MacArthur, Michael E. Talkowski

Structural variants (SVs) rearrange the linear and three-dimensional organization of the genome, which can have profound consequences in evolution, diversity, and disease. As national biobanks, human disease association studies, and clinical genetic testing are increasingly reliant on whole-genome sequencing, population references for small variants (i.e., SNVs & indels) in protein-coding genes, such as the Genome Aggregation Database (gnomAD), have become integral for the evaluation and interpretation of genomic variation. However, no comparable large-scale reference maps for SVs exist to date. Here, we constructed a reference atlas of SVs from deep whole-genome sequencing (WGS) of 14,891 individuals across diverse global populations (54% non-European) as a component of gnomAD. We discovered a rich landscape of 498,257 unique SVs, including 5,729 multi-breakpoint complex SVs across 13 mutational subclasses, and examples of localized chromosome shattering, like chromothripsis, in the general population. The mutation rates and densities of SVs were non-uniform across chromosomes and SV classes. We discovered strong correlations between constraint against predicted loss-of-function (pLoF) SNVs and rare SVs that both disrupt and duplicate protein-coding genes, suggesting that existing per-gene metrics of pLoF SNV constraint do not simply reflect haploinsufficiency, but appear to capture a gene's general sensitivity to dosage alterations. The average genome in gnomAD-SV harbored 8,202 SVs, and approximately eight genes altered by rare SVs. When incorporating these data with pLoF SNVs, we estimate that SVs comprise at least 25% of all rare pLoF events per genome. We observed large ( ≥1Mb), rare SVs in 3.1% of genomes (~1:32 individuals), and a clinically reportable pathogenic incidental finding from SVs in 0.24% of genomes (~1:417 individuals). We also estimated the prevalence of previously reported pathogenic recurrent CNVs associated with genomic disorders, which highlighted differences in frequencies across populations and confirmed that WGS-based analyses can readily recapitulate these clinically important variants. In total, gnomAD-SV includes at least one CNV covering 57% of the genome, while the remaining 43% is significantly enriched for CNVs found in tumors and individuals with developmental disorders. However, current sample sizes remain markedly underpowered to establish estimates of SV constraint on the level of individual genes or noncoding loci. The gnomAD-SV resources have been integrated into the gnomAD browser (https://gnomad.broadinstitute.org), where users can freely explore this dataset without restrictions on reuse, which will have broad utility in population genetics, disease association, and diagnostic screening.

69: Minor allele frequency thresholds strongly affect population structure inference with genomic datasets
more details view paper

Posted to bioRxiv 14 Sep 2017

Minor allele frequency thresholds strongly affect population structure inference with genomic datasets
6,684 downloads genomics

Ethan B. Linck, C.J. Battey

Across the genome, the effects of different evolutionary processes and historical events can result in different classes of genetic variants (or alleles) characterized by their relative frequency in a given population. As a result, population genetic inference can be strongly affected by biases in laboratory and bioinformatics treatments that affect the site frequency spectrum, or SFS. Yet despite the widespread use of reduced-representation genomic datasets with nonmodel organisms, the potential consequences of these biases for downstream analyses remain poorly examined. Here, we assess the influence of minor allele frequency (MAF) thresholds implemented during variant detection on inference of population structure. We use simulated and empirical datasets to evaluate the effect of MAF thresholds on the ability to discriminate among populations and quantify admixture with both model-based and non-model-based clustering methods. We find model-based inference of population structure is highly sensitive to choice of MAF, and may be confounded by either including singletons or excluding all rare alleles. In contrast, non-model-based clustering is largely robust to MAF choice. Our results suggest that model-based inference of population structure can fail due to either natural demographic processes or assembly artifacts, with broad consequences for phylogeographic and population genetic studies using NGS data. We propose a simple hypothesis to explain this behavior and recommend a set of best practices for researchers seeking to describe population structure using reduced-representation libraries.

70: MULTI-seq: Scalable sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
more details view paper

Posted to bioRxiv 08 Aug 2018

MULTI-seq: Scalable sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
6,678 downloads genomics

Christopher S McGinnis, David M Patterson, Juliane Winkler, Marco Y Hein, Vasudha Srivastava, Daniel N Conrad, Lyndsay M Murrow, Jonathan S Weissman, Zena Werb, Eric D. Chow, Zev J Gartner

We describe MULTI-seq: A rapid, modular, and universal scRNA-seq sample multiplexing strategy using lipid-tagged indices. MULTI-seq reagents can barcode any cell type from any species with an accessible plasma membrane. The method is compatible with enzymatic tissue dissociation, and also preserves viability and endogenous gene expression patterns. We leverage these features to multiplex the analysis of multiple solid tissues comprising human and mouse cells isolated from patient-derived xenograft mouse models. We also utilize MULTI-seq's modular design to perform a 96-plex perturbation experiment with human mammary epithelial cells. MULTI-seq also enables robust doublet identification, which improves data quality and increases scRNA-seq cell throughput by minimizing the negative effects of Poisson loading. We anticipate that the sample throughput and reagent savings enabled by MULTI-seq will expand the purview of scRNA-seq and democratize the application of these technologies within the scientific community.

71: Detecting DNA Methylation using the Oxford Nanopore Technologies MinION sequencer
more details view paper

Posted to bioRxiv 04 Apr 2016

Detecting DNA Methylation using the Oxford Nanopore Technologies MinION sequencer
6,654 downloads genomics

Jared T Simpson, Rachael Workman, P. C. Zuzarte, Matei David, L. J. Dursi, Winston Timp

Nanopore sequencing instruments measure the change in electric current caused by DNA transiting through the pore. In experimental and prototype nanopore sequencing devices it has been shown that the electrolytic current signals are sensitive to base modifications, such as 5-methylcytosine. Here we quantify the strength of this effect for the Oxford Nanopore Technologies MinION sequencer. Using synthetically methylated DNA we are able to train a hidden Markov model to distinguish 5-methylcytosine from unmethylated cytosine in DNA. We demonstrate by sequencing natural human DNA, without any special library preparation, that global patterns of methylation can be detected from low-coverage sequencing and that the methylation status of CpG islands can be reliably predicted from single MinION reads. Our trained model and prediction software is open source and freely available to the community under the MIT license.

72: Nanopore native RNA sequencing of a human poly(A) transcriptome
more details view paper

Posted to bioRxiv 09 Nov 2018

Nanopore native RNA sequencing of a human poly(A) transcriptome
6,586 downloads genomics

Rachael E. Workman, Alison D Tang, Paul S. Tang, Miten Jain, John R Tyson, Philip C Zuzarte, Timothy Gilpatrick, Roham Razaghi, Joshua Quick, Norah Sadowski, Nadine Holmes, Jaqueline Goes de Jesus, Karen L. Jones, Terrance P Snutch, Nicholas J Loman, Benedict Paten, Matthew Loose, Jared T Simpson, Hugh E Olsen, Angela N Brooks, Mark Akeson, Winston Timp

High throughput RNA sequencing technologies have dramatically advanced our understanding of transcriptome complexity and regulation. However, these cDNA-based methods lose information contained in biological RNA because the copied reads are short or because modifications are not carried forward in cDNA. Here we address these limitations using a native poly(A) RNA sequencing strategy developed by Oxford Nanopore Technologies (ONT). Our study focused on poly(A) RNA isolated from the human cell line GM12878, from which we sequenced approximately 9.9 million individual aligned strands. These native RNA sequence reads had an N50 length of 1334 bases, and a maximum length of 22,000 bases. A total of 78,199 high-confidence isoforms were identified by combining long nanopore reads with short higher accuracy Illumina reads. Among these isoforms, over 50% are not present in GENCODE v24. We describe strategies for assessing 3'poly(A) tail length, base modifications and transcript haplotypes using this single molecule technology. Together, these nanopore-based techniques are poised to deliver new insights into RNA biology.

73: Systematic comparative analysis of single cell RNA-sequencing methods
more details view paper

Posted to bioRxiv 09 May 2019

Systematic comparative analysis of single cell RNA-sequencing methods
6,543 downloads genomics

Jiarui Ding, Xian Adiconis, Sean K Simmons, Monika S. Kowalczyk, Cynthia C. Hession, Nemanja D. Marjanovic, Travis K Hughes, Marc H Wadsworth, Tyler Burks, Lan T. Nguyen, John Y. H. Kwon, Boaz Barak, William Ge, Amanda J. Kedaigle, Shaina Carroll, Shuqiang Li, Nir Hacohen, Orit Rozenblatt-Rosen, Alex K Shalek, Alexandra-Chloé Villani, Aviv Regev, Joshua Z Levin

A multitude of single-cell RNA sequencing methods have been developed in recent years, with dramatic advances in scale and power, and enabling major discoveries and large scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single cell and/or single nucleus profiling from three types of samples -- cell lines, peripheral blood mononuclear cells and brain tissue -- generating 36 libraries in six separate experiments in a single center. To analyze these datasets, we developed and applied scumi, a flexible computational pipeline that can be used for any scRNA-seq method. We evaluated the methods for both basic performance and for their ability to recover known biological information in the samples. Our study will help guide experiments with the methods in this study as well as serve as a benchmark for future studies and for computational algorithm development.

74: DroNc-Seq: Deciphering cell types in human archived brain tissues by massively-parallel single nucleus RNA-seq
more details view paper

Posted to bioRxiv 09 Mar 2017

DroNc-Seq: Deciphering cell types in human archived brain tissues by massively-parallel single nucleus RNA-seq
6,490 downloads genomics

Naomi Habib, Anindita Basu, Inbal Avraham-Davidi, Tyler Burks, Sourav R Choudhury, François Aguet, Ellen Gelfand, Kristin Ardlie, David A Weitz, Orit Rozenblatt-Rosen, Feng Zhang, Aviv Regev

Single nucleus RNA-Seq (sNuc-Seq) profiles RNA from tissues that are preserved or cannot be dissociated, but does not provide the throughput required to analyse many cells from complex tissues. Here, we develop DroNc-Seq, massively parallel sNuc-Seq with droplet technology. We profile 29,543 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient and unbiased classification of cell types, paving the way for charting systematic cell atlases.

75: Adapterama I: Universal stubs and primers for 384 unique dual-indexed or 147,456 combinatorially-indexed Illumina libraries (iTru & iNext)
more details view paper

Posted to bioRxiv 15 Jun 2016

Adapterama I: Universal stubs and primers for 384 unique dual-indexed or 147,456 combinatorially-indexed Illumina libraries (iTru & iNext)
6,479 downloads genomics

Travis C. Glenn, Roger A. Nilsen, Troy J. Kieran, Jon G. Sanders, Natalia J. Bayona-Vásquez, John W. Finger, Todd W. Pierson, Kerin E. Bentley, Sandra L. Hoffberg, Swarnali Louha, Francisco J. García-De León, Miguel Angel Del Río-Portilla, Kurt D. Reed, Jennifer L. Anderson, Jennifer K. Meece, Samuel E. Aggrey, Romdhane Rekaya, Magdy Alabady, Myriam Bélanger, Kevin Winker, Brant C Faircloth

Next-generation DNA sequencing (NGS) offers many benefits, but major factors limiting NGS include reducing costs of: 1) start-up (i.e., doing NGS for the first time); 2) buy-in (i.e., getting the smallest possible amount of data from a run); and 3) sample preparation. Reducing sample preparation costs is commonly addressed, but start-up and buy-in costs are rarely addressed. We present dual-indexing systems to address all three of these issues. By breaking the library construction process into universal, re-usable, combinatorial components, we reduce all costs, while increasing the number of samples and the variety of library types that can be combined within runs. We accomplish this by extending the Illumina TruSeq dual-indexing approach to 768 (384 + 384) indexed primers that produce 384 unique dual-indexes or 147,456 (384 x 384) unique combinations. We maintain eight nucleotide indexes, with many that are compatible with Illumina index sequences. We synthesized these indexing primers, purifying them with only standard desalting and placing small aliquots in replicate plates. In qPCR validation tests, 206 of 208 primers tested passed (99% success). We then created hundreds of libraries in various scenarios. Our approach reduces start-up and per-sample costs by requiring only one universal adapter that works with indexed PCR primers to uniquely identify samples. Our approach reduces buy-in costs because: 1) relatively few oligonucleotides are needed to produce a large number of indexed libraries; and 2) the large number of possible primers allows researchers to use unique primer sets for different projects, which facilitates pooling of samples during sequencing. Our libraries make use of standard Illumina sequencing primers and index sequence length and are demultiplexed with standard Illumina software, thereby minimizing customization headaches. In subsequent Adapterama papers, we use these same primers with different adapter stubs to construct amplicon and restriction-site associated DNA libraries, but their use can be expanded to any type of library sequenced on Illumina platforms.

76: A vision for ubiquitous sequencing
more details view paper

Posted to bioRxiv 07 May 2015

A vision for ubiquitous sequencing
6,255 downloads genomics

Yaniv Erlich

Genomics has recently celebrated reaching the $1000 genome milestone, making affordable DNA sequencing a reality. With this goal successfully completed, the next goal of the sequencing revolution can be sequencing sensors - miniaturized sequencing devices that are manufactured for real time applications and deployed in large quantities at low costs. The first part of this manuscript envisions applications that will benefit from moving the sequencers to the samples in a range of domains. In the second part, the manuscript outlines the critical barriers that need to be addressed in order to reach the goal of ubiquitous sequencing sensors.

77: The Drosophila Embryo at Single Cell Transcriptome Resolution
more details view paper

Posted to bioRxiv 17 Mar 2017

The Drosophila Embryo at Single Cell Transcriptome Resolution
6,207 downloads genomics

Nikos Karaiskos, Philipp Wahle, Jonathan Alles, Anastasiya Boltengagen, Salah Ayoub, Claudia Kipar, Christine Kocks, Nikolaus Rajewsky, Robert P Zinzen

Drosophila is a premier model system for understanding the molecular mechanisms of development. By the onset of morphogenesis, ~6000 cells express distinct gene combinations according to embryonic position. Despite extensive mRNA in situ screens, combinatorial gene expression within individual cells is largely unknown. Therefore, it is difficult to comprehensively identify the coding and non-coding transcripts that drive patterning and to decipher the molecular basis of cellular identity. Here, we single-cell sequence precisely staged embryos, measuring >3100 genes per cell. We produce a ‘transcriptomic blueprint’ of development - a virtual embryo where 3D locations of sequenced cells are confidently identified. Our Drosophila-Virtual-Expression-eXplorer performs virtual in situ hybridizations and computes expression gradients (http://dvex.org). Using DVEX, we predict spatial expression and discover patterned lncRNAs. DEVX is sensitive enough to detect subtle evolutionary changes in expression patterns between Drosophila species. We believe DVEX is a prototype for powerful single cell studies in complex tissues.

78: The first near-complete assembly of the hexaploid bread wheat genome, Triticum aestivum
more details view paper

Posted to bioRxiv 03 Jul 2017

The first near-complete assembly of the hexaploid bread wheat genome, Triticum aestivum
6,173 downloads genomics

Aleksey V. Zimin, Daniela Puiu, Richard Hall, Sarah Kingan, Bernardo J. Clavijo, Steven Salzberg

Common bread wheat, Triticum aestivum, has one of the most complex genomes known to science, with 6 copies of each chromosome, enormous numbers of near-identical sequences scattered throughout, and an overall size of more than 15 billion bases. Multiple past attempts to assemble the genome have failed. Here we report the first successful assembly of T. aestivum, using deep sequencing coverage from a combination of short Illumina reads and very long Pacific Biosciences reads. The final assembly contains 15,344,693,583 bases and has a weighted average (N50) contig size of of 232,659 bases. This represents by far the most complete and contiguous assembly of the wheat genome to date, providing a strong foundation for future genetic studies of this important food crop. We also report how we used the recently published genome of Aegilops tauschii, the diploid ancestor of the wheat D genome, to identify 4,179,762,575 bp of T. aestivum that correspond to its D genome components.

79: Pooled CRISPR screening with single-cell transcriptome read-out
more details view paper

Posted to bioRxiv 27 Oct 2016

Pooled CRISPR screening with single-cell transcriptome read-out
6,162 downloads genomics

Paul Datlinger, Christian Schmidl, André F Rendeiro, Peter Traxler, Johanna Klughammer, Linda Schuster, Christoph Bock

CRISPR-based genetic screens have revolutionized the search for new gene functions and biological mechanisms. However, widely used pooled screens are limited to simple read-outs of cell proliferation or the production of a selectable marker protein. Arrayed screens allow for more complex molecular read-outs such as transcriptome profiling, but they provide much lower throughput. Here we demonstrate CRISPR genome editing together with single-cell RNA sequencing as a new screening paradigm that combines key advantages of pooled and arrayed screens. This approach allowed us to link guide-RNA expression to the associated transcriptome responses in thousands of single cells using a straightforward and broadly applicable screening workflow.

80: MTAG: Multi-Trait Analysis of GWAS
more details view paper

Posted to bioRxiv 20 Mar 2017

MTAG: Multi-Trait Analysis of GWAS
6,074 downloads genomics

Patrick Turley, Raymond K Walters, Omeed Maghzian, Aysu Okbay, James J Lee, Mark Alan Fontana, Tuan Anh Nguyen-Viet, Robbee Wedow, Meghan Zacher, Nicholas A. Furlotte, 23andMe Research Team, Social Science Genetic Association Consortium, Patrik Magnusson, Sven Oskarsson, Magnus Johannesson, Peter M. Visscher, David Laibson, David Cesarini, Benjamin M Neale, Daniel J Benjamin

We introduce Multi-Trait Analysis of GWAS (MTAG), a method for joint analysis of summary statistics from GWASs of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (Neff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). Compared to 32, 9, and 13 genome-wide significant loci in the single-trait GWASs (most of which are themselves novel), MTAG increases the number of loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

Previous page 1 2 3 4 5 6 7 8 . . . 212 Next page

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