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VISPA: a Computational Pipeline for the Identification and Analysis Of
Calabria et al. Genome Medicine 2014, 6:67 http://genomemedicine.com/content/6/9/67 SOFTWARE Open Access VISPA: a computational pipeline for the identification and analysis of genomic vector integration sites Andrea Calabria1†, Simone Leo2,3†, Fabrizio Benedicenti1, Daniela Cesana1, Giulio Spinozzi1,4, Massimilano Orsini2, Stefania Merella5, Elia Stupka5, Gianluigi Zanetti2 and Eugenio Montini1* Abstract The analysis of the genomic distribution of viral vector genomic integration sites is a key step in hematopoietic stem cell-based gene therapy applications, allowing to assess both the safety and the efficacy of the treatment and to study the basic aspects of hematopoiesis and stem cell biology. Identifying vector integration sites requires ad-hoc bioinformatics tools with stringent requirements in terms of computational efficiency, flexibility, and usability. We developed VISPA (Vector Integration Site Parallel Analysis), a pipeline for automated integration site identification and annotation based on a distributed environment with a simple Galaxy web interface. VISPA was successfully used for the bioinformatics analysis of the follow-up of two lentiviral vector-based hematopoietic stem-cell gene therapy clinical trials. Our pipeline provides a reliable and efficient tool to assess the safety and efficacy of integrating vectors in clinical settings. Background the proximity of the vector’sintegrationsite(IS),a Viral vectors, due to their ability to permanently integrate phenomenon known as insertional mutagenesis (IM) in a target genome, are used to achieve the stable genetic [1,9-12]. The identification of ISs on leukemic cells from modification of therapeutically relevant cells and their GT patients and preclinical models allowed identifying progeny. In particular, γ-retroviral (γ-RVs) and lentiviral the causes of IM and tracking the evolution of the (LVs) vectors are the preferred choice for hematopoietic malignant clone over time [11-16]. -
Applied Category Theory for Genomics – an Initiative
Applied Category Theory for Genomics { An Initiative Yanying Wu1,2 1Centre for Neural Circuits and Behaviour, University of Oxford, UK 2Department of Physiology, Anatomy and Genetics, University of Oxford, UK 06 Sept, 2020 Abstract The ultimate secret of all lives on earth is hidden in their genomes { a totality of DNA sequences. We currently know the whole genome sequence of many organisms, while our understanding of the genome architecture on a systematic level remains rudimentary. Applied category theory opens a promising way to integrate the humongous amount of heterogeneous informations in genomics, to advance our knowledge regarding genome organization, and to provide us with a deep and holistic view of our own genomes. In this work we explain why applied category theory carries such a hope, and we move on to show how it could actually do so, albeit in baby steps. The manuscript intends to be readable to both mathematicians and biologists, therefore no prior knowledge is required from either side. arXiv:2009.02822v1 [q-bio.GN] 6 Sep 2020 1 Introduction DNA, the genetic material of all living beings on this planet, holds the secret of life. The complete set of DNA sequences in an organism constitutes its genome { the blueprint and instruction manual of that organism, be it a human or fly [1]. Therefore, genomics, which studies the contents and meaning of genomes, has been standing in the central stage of scientific research since its birth. The twentieth century witnessed three milestones of genomics research [1]. It began with the discovery of Mendel's laws of inheritance [2], sparked a climax in the middle with the reveal of DNA double helix structure [3], and ended with the accomplishment of a first draft of complete human genome sequences [4]. -
Distinctive Regulatory Architectures of Germline-Active and Somatic Genes in C
Downloaded from genome.cshlp.org on October 7, 2021 - Published by Cold Spring Harbor Laboratory Press Research Distinctive regulatory architectures of germline-active and somatic genes in C. elegans Jacques Serizay, Yan Dong, Jürgen Jänes, Michael Chesney, Chiara Cerrato, and Julie Ahringer The Gurdon Institute and Department of Genetics, University of Cambridge, CB2 1QN Cambridge, United Kingdom RNA profiling has provided increasingly detailed knowledge of gene expression patterns, yet the different regulatory ar- chitectures that drive them are not well understood. To address this, we profiled and compared transcriptional and regu- latory element activities across five tissues of Caenorhabditis elegans, covering ∼90% of cells. We find that the majority of promoters and enhancers have tissue-specific accessibility, and we discover regulatory grammars associated with ubiquitous, germline, and somatic tissue–specific gene expression patterns. In addition, we find that germline-active and soma-specific promoters have distinct features. Germline-active promoters have well-positioned +1 and −1 nucleosomes associated with a periodic 10-bp WW signal (W = A/T). Somatic tissue–specific promoters lack positioned nucleosomes and this signal, have wide nucleosome-depleted regions, and are more enriched for core promoter elements, which largely differ between tissues. We observe the 10-bp periodic WW signal at ubiquitous promoters in other animals, suggesting it is an ancient conserved signal. Our results show fundamental differences in regulatory architectures of germline and somatic tissue–specific genes, uncover regulatory rules for generating diverse gene expression patterns, and provide a tissue-specific resource for future studies. [Supplemental material is available for this article.] Cell type–specific transcription regulation underlies production tissues are achieved and whether expression is governed by dis- of the myriad of different cells generated during development. -
Personal and Population Genomics of Human Regulatory Variation
Downloaded from genome.cshlp.org on September 24, 2021 - Published by Cold Spring Harbor Laboratory Press Research Personal and population genomics of human regulatory variation Benjamin Vernot, Andrew B. Stergachis, Matthew T. Maurano, Jeff Vierstra, Shane Neph, Robert E. Thurman, John A. Stamatoyannopoulos,1 and Joshua M. Akey1 Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA The characteristics and evolutionary forces acting on regulatory variation in humans remains elusive because of the difficulty in defining functionally important noncoding DNA. Here, we combine genome-scale maps of regulatory DNA marked by DNase I hypersensitive sites (DHSs) from 138 cell and tissue types with whole-genome sequences of 53 geo- graphically diverse individuals in order to better delimit the patterns of regulatory variation in humans. We estimate that individuals likely harbor many more functionally important variants in regulatory DNA compared with protein-coding regions, although they are likely to have, on average, smaller effect sizes. Moreover, we demonstrate that there is sig- nificant heterogeneity in the level of functional constraint in regulatory DNA among different cell types. We also find marked variability in functional constraint among transcription factor motifs in regulatory DNA, with sequence motifs for major developmental regulators, such as HOX proteins, exhibiting levels of constraint comparable to protein-coding regions. Finally, we perform a genome-wide scan of recent positive selection and identify hundreds of novel substrates of adaptive regulatory evolution that are enriched for biologically interesting pathways such as melanogenesis and adipo- cytokine signaling. These data and results provide new insights into patterns of regulatory variation in individuals and populations and demonstrate that a large proportion of functionally important variation lies beyond the exome. -
What Is Biomath?
What is Biomath? Summary Biomathematics covers a wide range of activities at the interface between the mathematical and biological sciences. Depending on who you talk to, it might have a different name, including mathematical biology, systems biology, quantitative biology or theoretical biology. The focus of our biomathematics program is on building and using mathematical models to describe and analyze biological systems. Often this involves the analysis of biological data, which typically means using statistical approaches together with the models. In many cases, our models are mechanistic, meaning that their components represent biological processes, although many of us also use models that are more statistical in nature. More In-Depth Biomathematics is the use of mathematical models to help understand phenomena in biology. Modern experimental biology is very good at taking biological systems apart (at all levels of organization, from genome to global nutrient cycling), into components simple enough that their structure and function can be studied in isolation. Dynamic models are a way to put the pieces back together, with equations that represent the system’s components, processes, and the structure of their interactions. Mathematical models are important tools in basic scientific research in many areas of biology, including physiology, cellular biology, developmental biology, ecology, evolution, toxicology, epidemiology, immunology, natural resource management, and conservation biology. The results obtained from analysis and simulation -
Genomic Approaches for Understanding the Genetics of Complex Disease
Downloaded from genome.cshlp.org on September 25, 2021 - Published by Cold Spring Harbor Laboratory Press Perspective Genomic approaches for understanding the genetics of complex disease William L. Lowe Jr.1 and Timothy E. Reddy2,3 1Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA; 2Department of Biostatistics and Bioinformatics, Duke University Medical School, Durham, North Carolina 27708, USA; 3Center for Genomic and Computational Biology, Duke University Medical School, Durham, North Carolina 27708, USA There are thousands of known associations between genetic variants and complex human phenotypes, and the rate of novel discoveries is rapidly increasing. Translating those associations into knowledge of disease mechanisms remains a fundamental challenge because the associated variants are overwhelmingly in noncoding regions of the genome where we have few guiding principles to predict their function. Intersecting the compendium of identified genetic associations with maps of regulatory activity across the human genome has revealed that phenotype-associated variants are highly enriched in candidate regula- tory elements. Allele-specific analyses of gene regulation can further prioritize variants that likely have a functional effect on disease mechanisms; and emerging high-throughput assays to quantify the activity of candidate regulatory elements are a promising next step in that direction. Together, these technologies have created the ability to systematically and empirically test hypotheses about the function of noncoding variants and haplotypes at the scale needed for comprehensive and system- atic follow-up of genetic association studies. Major coordinated efforts to quantify regulatory mechanisms across genetically diverse populations in increasingly realistic cell models would be highly beneficial to realize that potential. -
Ten Simple Rules to Consider Regarding Preprint Submission
EDITORIAL Ten simple rules to consider regarding preprint submission Philip E. Bourne1*, Jessica K. Polka2, Ronald D. Vale3, Robert Kiley4 1 Office of the Director, The National Institutes of Health, Bethesda, Maryland, United States of America, 2 Whitehead Institute, Cambridge, Massachusetts, United States of America, 3 Department of Cellular and Molecular Pharmacology and the Howard Hughes Medical Institute, University of California San Francisco, San Francisco, California, United States of America, 4 Wellcome Library, The Wellcome Trust, London, United Kingdom * [email protected] For the purposes of these rules, a preprint is defined as a complete written description of a body of scientific work that has yet to be published in a journal. Typically, a preprint is a research article, editorial, review, etc. that is ready to be submitted to a journal for peer review or is under review. It could also be a commentary, a report of negative results, a large data set and its description, and more. Finally, it could also be a paper that has been peer reviewed and either is awaiting formal publication by a journal or was rejected, but the authors are willing to make the content public. In short, a preprint is a research output that has not completed a typi- cal publication pipeline but is of value to the community and deserving of being easily discov- ered and accessed. We also note that the term preprint is an anomaly, since there may not be a a1111111111 print version at all. The rules that follow relate to all these preprint types unless otherwise a1111111111 noted. -
1 the Challenges Facing Genomic Informatics
1 The Challenges Facing Genomic Informatics Temple F. Smith What are these areas of intense research labeled bioinformatics and functional genomics? If we take literally much of the recently published "news and views," it seems that the often stated claim that the last century was the century of physics, whereas the twenty-first will be the century of biology, rests significantly on these new research areas. We might therefore ask: What is new about them? After all, compu- tational or mathematical biology has been around for a long time. Surely much of bioinformatics, particularly that associated with evolution and genetic analyses, does not appear very new. In fact, the related work of researchers like R. A. Fisher, J. B. S. Haldane, and SewellWright dates nearly to the beginning of the 1900s. The modem analytical approaches to genetics, evolution, and ecology rest directly on their and similar work. Even genetic mapping easily dates to the 1930s, with the work of T. S. Painter and his students of Drosophila (still earlier if you include T. H. Morgan's work on X-linked markers in the fly). Thus a short historical review might provide a useful perspective on this anticipated century of biology and allow us to view the future from a firmer foundation. First of all, it should be helpful to recognize that it was very early in the so-called century of physics that modem biology began, with a paper read by Hermann Muller at a 1921meeting in Toronto. Muller, a student of Morgan's, stated that although of submicroscopic size, the gene was clearly a physical particle of complex structure, not just a working construct! Muller noted that the gene is unique from its product, and that it is normally duplicated unchanged, but once mutated, the new form is in turn duplicated faithfully. -
The Economic Impact and Functional Applications of Human Genetics and Genomics
The Economic Impact and Functional Applications of Human Genetics and Genomics Commissioned by the American Society of Human Genetics Produced by TEConomy Partners, LLC. Report Authors: Simon Tripp and Martin Grueber May 2021 TEConomy Partners, LLC (TEConomy) endeavors at all times to produce work of the highest quality, consistent with our contract commitments. However, because of the research and/or experimental nature of this work, the client undertakes the sole responsibility for the consequence of any use or misuse of, or inability to use, any information or result obtained from TEConomy, and TEConomy, its partners, or employees have no legal liability for the accuracy, adequacy, or efficacy thereof. Acknowledgements ASHG and the project authors wish to thank the following organizations for their generous support of this study. Invitae Corporation, San Francisco, CA Regeneron Pharmaceuticals, Inc., Tarrytown, NY The project authors express their sincere appreciation to the following indi- viduals who provided their advice and input to this project. ASHG Government and Public Advocacy Committee Lynn B. Jorde, PhD ASHG Government and Public Advocacy Committee (GPAC) Chair, President (2011) Professor and Chair of Human Genetics George and Dolores Eccles Institute of Human Genetics University of Utah School of Medicine Katrina Goddard, PhD ASHG GPAC Incoming Chair, Board of Directors (2018-2020) Distinguished Investigator, Associate Director, Science Programs Kaiser Permanente Northwest Melinda Aldrich, PhD, MPH Associate Professor, Department of Medicine, Division of Genetic Medicine Vanderbilt University Medical Center Wendy Chung, MD, PhD Professor of Pediatrics in Medicine and Director, Clinical Cancer Genetics Columbia University Mira Irons, MD Chief Health and Science Officer American Medical Association Peng Jin, PhD Professor and Chair, Department of Human Genetics Emory University Allison McCague, PhD Science Policy Analyst, Policy and Program Analysis Branch National Human Genome Research Institute Rebecca Meyer-Schuman, MS Human Genetics Ph.D. -
INTELLIGENT MEDICINE the Wings of Global Health
INTELLIGENT MEDICINE The Wings of Global Health AUTHOR INFORMATION PACK TABLE OF CONTENTS XXX . • Description p.1 • Editorial Board p.2 • Guide for Authors p.6 ISSN: 2667-1026 DESCRIPTION . Intelligent Medicine is an open access, peer-reviewed journal sponsored and owned by the Chinese Medical Association and designated to publish high-quality research and application in the field of medical-industrial crossover concerning the internet technology, artificial intelligence (AI), data science, medical information, and intelligent devices in the clinical medicine, biomedicine, and public health. Intelligent Medicine appreciates the innovation, pioneering, science, and application, encourages the unique perspectives and suggestions. The topics focus on the computer and data science enabled intelligent medicine, including while not limited to the clinical decision making, computer-assisted surgery, telemedicine, drug development, image analysis and computation, and health management. The journal sets academic columns according to the different disciplines and hotspots. Article types include Research Article: These articles are expected to be original, innovative, and significant, including medical and algorithmic research. The real-world medical research rules and clinical assessment of the usefulness and reliability are recommended for those medical research. The full text is about 6,000 words, with structured abstract of 300 words including Background, Methods, Results, and Conclusion. Editorial: Written by the Editor-in-Chief, Associate Editors, editorial board members, or prestigious invited scientists and policy makers on a broad range of topics from science to policy. Review: Extensive reviews of the recent progress in specific areas of science, involving historical reviews, recent advances made by scientists internationally, and perspective for future development; the full text is about 5,000~6,000 words. -
Developing and Implementing an Institute-Wide Data Sharing Policy Stephanie OM Dyke and Tim JP Hubbard*
Dyke and Hubbard Genome Medicine 2011, 3:60 http://genomemedicine.com/content/3/9/60 CORRESPONDENCE Developing and implementing an institute-wide data sharing policy Stephanie OM Dyke and Tim JP Hubbard* Abstract HapMap Project [7], also decided to follow HGP prac- tices and to share data publicly as a resource for the The Wellcome Trust Sanger Institute has a strong research community before academic publications des- reputation for prepublication data sharing as a result crib ing analyses of the data sets had been prepared of its policy of rapid release of genome sequence (referred to as prepublication data sharing). data and particularly through its contribution to the Following the success of the first phase of the HGP [8] Human Genome Project. The practicalities of broad and of these other projects, the principles of rapid data data sharing remain largely uncharted, especially to release were reaffirmed and endorsed more widely at a cover the wide range of data types currently produced meeting of genomics funders, scientists, public archives by genomic studies and to adequately address and publishers in Fort Lauderdale in 2003 [9]. Meanwhile, ethical issues. This paper describes the processes the Organisation for Economic Co-operation and and challenges involved in implementing a data Develop ment (OECD) Committee on Scientific and sharing policy on an institute-wide scale. This includes Tech nology Policy had established a working group on questions of governance, practical aspects of applying issues of access to research information [10,11], which principles to diverse experimental contexts, building led to a Declaration on access to research data from enabling systems and infrastructure, incentives and public funding [12], and later to a set of OECD guidelines collaborative issues. -
Cancer Genome Interpreter Annotates the Biological and Clinical Relevance of Tumor Alterations David Tamborero1,2, Carlota Rubio-Perez1, Jordi Deu-Pons1,2, Michael P
Tamborero et al. Genome Medicine (2018) 10:25 https://doi.org/10.1186/s13073-018-0531-8 DATABASE Open Access Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations David Tamborero1,2, Carlota Rubio-Perez1, Jordi Deu-Pons1,2, Michael P. Schroeder1,3, Ana Vivancos4, Ana Rovira5,6, Ignasi Tusquets5,6,7, Joan Albanell5,6,8, Jordi Rodon4, Josep Tabernero4, Carmen de Torres9, Rodrigo Dienstmann4, Abel Gonzalez-Perez1,2 and Nuria Lopez-Bigas1,2,10* Abstract While tumor genome sequencing has become widely available in clinical and research settings, the interpretation of tumor somatic variants remains an important bottleneck. Here we present the Cancer Genome Interpreter, a versatile platform that automates the interpretation of newly sequenced cancer genomes, annotating the potential of alterations detected in tumors to act as drivers and their possible effect on treatment response. The results are organized in different levels of evidence according to current knowledge, which we envision can support a broad range of oncology use cases. The resource is publicly available at http://www.cancergenomeinterpreter.org. Background to systematically identify genes involved in tumorigen- The accumulation of so-called “driver” genomic alter- esis through the detection of signals of positive selection ations confers on cells tumorigenic capabilities [1]. in their alteration patterns across tumors of some two Thousands of tumor genomes are sequenced around the dozen malignancies [3–6]. However, many of the som- world every year for both research and clinical purposes. atic variants detected in tumors, even those in cancer In some cases the whole genome is sequenced while in genes, still have uncertain significance and thus it is not others the focus is on the exome or a panel of selected clear whether or not they are relevant for tumorigenesis.