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By Arjun Krishnan Computational biologist
Mathias Uhlén, Björn M Hallström, Cecilia Lindskog, Adil Mardinoglu, Fredrik Pontén, Jens Nielsen
Quantifying the differential expression of genes in various human organs, tissues, and cell types is vital to understand human physiology and disease. Recently, several large-scale transcriptomics studies have analyzed the expression of protein-coding genes across tissues. These datasets provide a framework for defining the molecular constituents of the human body as well as for generating comprehensive lists of proteins expressed across tissues or in a tissue-restricted manner. Here, we review publicly available human transcriptome resources and discuss body-wide data from independent genome-wide transcriptome analyses of different tissues...
April 4, 2016: Molecular Systems Biology
Trygve E Bakken, Jeremy A Miller, Song-Lin Ding, Susan M Sunkin, Kimberly A Smith, Lydia Ng, Aaron Szafer, Rachel A Dalley, Joshua J Royall, Tracy Lemon, Sheila Shapouri, Kaylynn Aiona, James Arnold, Jeffrey L Bennett, Darren Bertagnolli, Kristopher Bickley, Andrew Boe, Krissy Brouner, Stephanie Butler, Emi Byrnes, Shiella Caldejon, Anita Carey, Shelby Cate, Mike Chapin, Jefferey Chen, Nick Dee, Tsega Desta, Tim A Dolbeare, Nadia Dotson, Amanda Ebbert, Erich Fulfs, Garrett Gee, Terri L Gilbert, Jeff Goldy, Lindsey Gourley, Ben Gregor, Guangyu Gu, Jon Hall, Zeb Haradon, David R Haynor, Nika Hejazinia, Anna Hoerder-Suabedissen, Robert Howard, Jay Jochim, Marty Kinnunen, Ali Kriedberg, Chihchau L Kuan, Christopher Lau, Chang-Kyu Lee, Felix Lee, Lon Luong, Naveed Mastan, Ryan May, Jose Melchor, Nerick Mosqueda, Erika Mott, Kiet Ngo, Julie Nyhus, Aaron Oldre, Eric Olson, Jody Parente, Patrick D Parker, Sheana Parry, Julie Pendergraft, Lydia Potekhina, Melissa Reding, Zackery L Riley, Tyson Roberts, Brandon Rogers, Kate Roll, David Rosen, David Sandman, Melaine Sarreal, Nadiya Shapovalova, Shu Shi, Nathan Sjoquist, Andy J Sodt, Robbie Townsend, Lissette Velasquez, Udi Wagley, Wayne B Wakeman, Cassandra White, Crissa Bennett, Jennifer Wu, Rob Young, Brian L Youngstrom, Paul Wohnoutka, Richard A Gibbs, Jeffrey Rogers, John G Hohmann, Michael J Hawrylycz, Robert F Hevner, Zoltán Molnár, John W Phillips, Chinh Dang, Allan R Jones, David G Amaral, Amy Bernard, Ed S Lein
The transcriptional underpinnings of brain development remain poorly understood, particularly in humans and closely related non-human primates. We describe a high-resolution transcriptional atlas of rhesus monkey (Macaca mulatta) brain development that combines dense temporal sampling of prenatal and postnatal periods with fine anatomical division of cortical and subcortical regions associated with human neuropsychiatric disease. Gene expression changes more rapidly before birth, both in progenitor cells and maturing neurons...
21, 2016: Nature
Andrew Dahl, Valentina Iotchkova, Amelie Baud, Åsa Johansson, Ulf Gyllensten, Nicole Soranzo, Richard Mott, Andreas Kranis, Jonathan Marchini
Genetic association studies have yielded a wealth of biological discoveries. However, these studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of the data sets. Joint genotype-phenotype analyses of complex, high-dimensional data sets represent an important way to move beyond simple genome-wide association studies (GWAS) with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. Here we address the central issue of missing phenotypes in studies with any level of relatedness between samples...
April 2016: Nature Genetics
Daniel Dominguez, Yi-Hsuan Tsai, Nicholas Gomez, Deepak Kumar Jha, Ian Davis, Zefeng Wang
Progression through the cell cycle is largely dependent on waves of periodic gene expression, and the regulatory networks for these transcriptome dynamics have emerged as critical points of vulnerability in various aspects of tumor biology. Through RNA-sequencing of human cells during two continuous cell cycles (>2.3 billion paired reads), we identified over 1 000 mRNAs, non-coding RNAs and pseudogenes with periodic expression. Periodic transcripts are enriched in functions related to DNA metabolism, mitosis, and DNA damage response, indicating these genes likely represent putative cell cycle regulators...
August 2016: Cell Research
Yuan Luo, Fei Wang, Peter Szolovits
Precision medicine initiatives come amid the rapid growth in quantity and variety of biomedical data, which exceeds the capacity of matrix-oriented data representations and many current analysis algorithms. Tensor factorizations extend the matrix view to multiple modalities and support dimensionality reduction methods that identify latent groups of data for meaningful summarization of both features and instances. In this opinion article, we analyze the modest literature on applying tensor factorization to various biomedical fields including genotyping and phenotyping...
March 19, 2016: Briefings in Bioinformatics
Ayal B Gussow, Slavé Petrovski, Quanli Wang, Andrew S Allen, David B Goldstein
Ranking human genes based on their tolerance to functional genetic variation can greatly facilitate patient genome interpretation. It is well established, however, that different parts of proteins can have different functions, suggesting that it will ultimately be more informative to focus attention on functionally distinct portions of genes. Here we evaluate the intolerance of genic sub-regions using two biological sub-region classifications. We show that the intolerance scores of these sub-regions significantly correlate with reported pathogenic mutations...
January 18, 2016: Genome Biology
Charlotte Soneson, Katarina L Matthes, Malgorzata Nowicka, Charity W Law, Mark D Robinson
BACKGROUND: RNA-seq has been a boon to the quantitative analysis of transcriptomes. A notable application is the detection of changes in transcript usage between experimental conditions. For example, discovery of pathological alternative splicing may allow the development of new treatments or better management of patients. From an analysis perspective, there are several ways to approach RNA-seq data to unravel differential transcript usage, such as annotation-based exon-level counting, differential analysis of the percentage spliced in, or quantitative analysis of assembled transcripts...
January 26, 2016: Genome Biology
Ana Conesa, Pedro Madrigal, Sonia Tarazona, David Gomez-Cabrero, Alejandra Cervera, Andrew McPherson, Michał Wojciech Szcześniak, Daniel J Gaffney, Laura L Elo, Xuegong Zhang, Ali Mortazavi
RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques...
January 26, 2016: Genome Biology
Aleksandra E Kornienko, Christoph P Dotter, Philipp M Guenzl, Heinz Gisslinger, Bettina Gisslinger, Ciara Cleary, Robert Kralovics, Florian M Pauler, Denise P Barlow
BACKGROUND: Long non-coding RNAs (lncRNAs) are increasingly implicated as gene regulators and may ultimately be more numerous than protein-coding genes in the human genome. Despite large numbers of reported lncRNAs, reference annotations are likely incomplete due to their lower and tighter tissue-specific expression compared to mRNAs. An unexplored factor potentially confounding lncRNA identification is inter-individual expression variability. Here, we characterize lncRNA natural expression variability in human primary granulocytes...
January 29, 2016: Genome Biology
Stefan Canzar, Sandro Andreotti, David Weese, Knut Reinert, Gunnar W Klau
We present CIDANE, a novel framework for genome-based transcript reconstruction and quantification from RNA-seq reads. CIDANE assembles transcripts efficiently with significantly higher sensitivity and precision than existing tools. Its algorithmic core not only reconstructs transcripts ab initio, but also allows the use of the growing annotation of known splice sites, transcription start and end sites, or full-length transcripts, which are available for most model organisms. CIDANE supports the integrated analysis of RNA-seq and additional gene-boundary data and recovers splice junctions that are invisible to other methods...
January 30, 2016: Genome Biology
Lesley T MacNeil, Carles Pons, H Efsun Arda, Gabrielle E Giese, Chad L Myers, Albertha J M Walhout
A wealth of physical interaction data between transcription factors (TFs) and DNA has been generated, but these interactions often do not have apparent regulatory consequences. Thus, equating physical interaction data with gene regulatory networks (GRNs) is problematic. Here, we comprehensively assay TF activity, rather than binding, to construct a network of gene regulatory interactions in the C. elegans intestine. By manually observing the in vivo tissue-specific knockdown of 921 TFs on a panel of 19 fluorescent transcriptional reporters, we identified a GRN of 411 interactions between 19 promoters and 177 TFs...
August 26, 2015: Cell Systems
Y William Yu, Noah M Daniels, David Christian Danko, Bonnie Berger
Many data sets exhibit well-defined structure that can be exploited to design faster search tools, but it is not always clear when such acceleration is possible. Here we introduce a framework for similarity search based on characterizing a data set's entropy and fractal dimension. We prove that searching scales in time with metric entropy (number of covering hyperspheres), if the fractal dimension of the data set is low, and scales in space with the sum of metric entropy and information-theoretic entropy (randomness of the data)...
August 26, 2015: Cell Systems
Dexter Pratt, Jing Chen, David Welker, Ricardo Rivas, Rudolf Pillich, Vladimir Rynkov, Keiichiro Ono, Carol Miello, Lyndon Hicks, Sandor Szalma, Aleksandar Stojmirovic, Radu Dobrin, Michael Braxenthaler, Jan Kuentzer, Barry Demchak, Trey Ideker
Networks are a powerful and flexible methodology for expressing biological knowledge for computation and communication. Network-encoded information can include systematic screens for molecular interactions, biological relationships curated from literature, and outputs from analysis of Big Data. NDEx, the Network Data Exchange (, is an online commons where scientists can upload, share, and publicly distribute networks. Networks in NDEx receive globally unique accession IDs and can be stored for private use, shared in pre-publication collaboration, or released for public access...
October 28, 2015: Cell Systems
Malachi Griffith, Christopher A Miller, Obi L Griffith, Kilannin Krysiak, Zachary L Skidmore, Avinash Ramu, Jason R Walker, Ha X Dang, Lee Trani, David E Larson, Ryan T Demeter, Michael C Wendl, Joshua F McMichael, Rachel E Austin, Vincent Magrini, Sean D McGrath, Amy Ly, Shashikant Kulkarni, Matthew G Cordes, Catrina C Fronick, Robert S Fulton, Christopher A Maher, Li Ding, Jeffery M Klco, Elaine R Mardis, Timothy J Ley, Richard K Wilson
Tumors are typically sequenced to depths of 75-100× (exome) or 30-50× (whole genome). We demonstrate that current sequencing paradigms are inadequate for tumors that are impure, aneuploid or clonally heterogeneous. To reassess optimal sequencing strategies, we performed ultra-deep (up to ~312×) whole genome sequencing (WGS) and exome capture (up to ~433×) of a primary acute myeloid leukemia, its subsequent relapse, and a matched normal skin sample. We tested multiple alignment and variant calling algorithms and validated ~200,000 putative SNVs by sequencing them to depths of ~1,000×...
September 23, 2015: Cell Systems
James C Chen, Jane E Cerise, Ali Jabbari, Raphael Clynes, Angela M Christiano
Network-based molecular modeling of physiological behaviors has proven invaluable in the study of complex diseases such as cancer, but these approaches remain largely untested in contexts involving interacting tissues such as autoimmunity. Here, using Alopecia Areata (AA) as a model, we have adapted regulatory network analysis to specifically isolate physiological behaviors in the skin that contribute to the recruitment of immune cells in autoimmune disease. We use context-specific regulatory networks to deconvolve and identify skin-specific regulatory modules with IKZF1 and DLX4 as master regulators (MRs)...
November 25, 2015: Cell Systems
Arthur Liberzon, Chet Birger, Helga Thorvaldsdóttir, Mahmoud Ghandi, Jill P Mesirov, Pablo Tamayo
The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB...
December 23, 2015: Cell Systems
Owen J L Rackham, Jaber Firas, Hai Fang, Matt E Oates, Melissa L Holmes, Anja S Knaupp, Harukazu Suzuki, Christian M Nefzger, Carsten O Daub, Jay W Shin, Enrico Petretto, Alistair R R Forrest, Yoshihide Hayashizaki, Jose M Polo, Julian Gough
Transdifferentiation, the process of converting from one cell type to another without going through a pluripotent state, has great promise for regenerative medicine. The identification of key transcription factors for reprogramming is currently limited by the cost of exhaustive experimental testing of plausible sets of factors, an approach that is inefficient and unscalable. Here we present a predictive system (Mogrify) that combines gene expression data with regulatory network information to predict the reprogramming factors necessary to induce cell conversion...
March 2016: Nature Genetics
Virginia Savova, Sung Chun, Mashaal Sohail, Ruth B McCole, Robert Witwicki, Lisa Gai, Tobias L Lenz, C-ting Wu, Shamil R Sunyaev, Alexander A Gimelbrant
An unexpectedly large number of human autosomal genes are subject to monoallelic expression (MAE). Our analysis of 4,227 such genes uncovers surprisingly high genetic variation across human populations. This increased diversity is unlikely to reflect relaxed purifying selection. Remarkably, MAE genes exhibit an elevated recombination rate and an increased density of hypermutable sequence contexts. However, these factors do not fully account for the increased diversity. We find that the elevated nucleotide diversity of MAE genes is also associated with greater allelic age: variants in these genes tend to be older and are enriched in polymorphisms shared by Neanderthals and chimpanzees...
March 2016: Nature Genetics
Gardar Sveinbjornsson, Anders Albrechtsen, Florian Zink, Sigurjón A Gudjonsson, Asmundur Oddson, Gísli Másson, Hilma Holm, Augustine Kong, Unnur Thorsteinsdottir, Patrick Sulem, Daniel F Gudbjartsson, Kari Stefansson
The consensus approach to genome-wide association studies (GWAS) has been to assign equal prior probability of association to all sequence variants tested. However, some sequence variants, such as loss-of-function and missense variants, are more likely than others to affect protein function and are therefore more likely to be causative. Using data from whole-genome sequencing of 2,636 Icelanders and the association results for 96 quantitative and 123 binary phenotypes, we estimated the enrichment of association signals by sequence annotation...
March 2016: Nature Genetics
Alexander Gusev, Arthur Ko, Huwenbo Shi, Gaurav Bhatia, Wonil Chung, Brenda W J H Penninx, Rick Jansen, Eco J C de Geus, Dorret I Boomsma, Fred A Wright, Patrick F Sullivan, Elina Nikkola, Marcus Alvarez, Mete Civelek, Aldons J Lusis, Terho Lehtimäki, Emma Raitoharju, Mika Kähönen, Ilkka Seppälä, Olli T Raitakari, Johanna Kuusisto, Markku Laakso, Alkes L Price, Päivi Pajukanta, Bogdan Pasaniuc
Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations...
March 2016: Nature Genetics
2016-02-14 15:35:17
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