Molecular Population Genetics
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Models of the Gene Must Inform Data-Mining Strategies in Genomics
entropy Review Models of the Gene Must Inform Data-Mining Strategies in Genomics Łukasz Huminiecki Department of Molecular Biology, Institute of Genetics and Animal Biotechnology, Polish Academy of Sciences, 00-901 Warsaw, Poland; [email protected] Received: 13 July 2020; Accepted: 22 August 2020; Published: 27 August 2020 Abstract: The gene is a fundamental concept of genetics, which emerged with the Mendelian paradigm of heredity at the beginning of the 20th century. However, the concept has since diversified. Somewhat different narratives and models of the gene developed in several sub-disciplines of genetics, that is in classical genetics, population genetics, molecular genetics, genomics, and, recently, also, in systems genetics. Here, I ask how the diversity of the concept impacts data-integration and data-mining strategies for bioinformatics, genomics, statistical genetics, and data science. I also consider theoretical background of the concept of the gene in the ideas of empiricism and experimentalism, as well as reductionist and anti-reductionist narratives on the concept. Finally, a few strategies of analysis from published examples of data-mining projects are discussed. Moreover, the examples are re-interpreted in the light of the theoretical material. I argue that the choice of an optimal level of abstraction for the gene is vital for a successful genome analysis. Keywords: gene concept; scientific method; experimentalism; reductionism; anti-reductionism; data-mining 1. Introduction The gene is one of the most fundamental concepts in genetics (It is as important to biology, as the atom is to physics or the molecule to chemistry.). The concept was born with the Mendelian paradigm of heredity, and fundamentally influenced genetics over 150 years [1]. -
CENTRE for POPULATION GENOMICS Strategic Plan 2021-2022 CENTRE for POPULATION GENOMICS Strategic Plan 2021-2022
CENTRE FOR POPULATION GENOMICS Strategic Plan 2021-2022 CENTRE FOR POPULATION GENOMICS Strategic Plan 2021-2022 • Plan-on-a-page page 3 • Vision page 4 • Purpose page 5 • Goals page 6 • Objectives page 7 • Goal 1 detail page 8 - 10 • Goal 2 detail page 11 - 13 • Goal 3 detail page 14 - 16 • Goal 4 detail page 17 - 19 • Principles page 20 • Operating model page 21 - 25 • Operational plan page 26 • Monitoring & evaluation plan page 27 – 28 • Team values page 29 CENTRE FOR POPULATION GENOMICS Strategic Plan-on-a-page 2021-2022 Vision: A world in which genomic information enables comprehensive disease prediction, accurate diagnosis and effective therapeutics for all people Purpose: To establish respectful partnerships with diverse communities, collect and analyse genomic data at transformative scale and drive genomic discovery and equitable genomic medicine in Australia Goals: Community Computational Genomic Scientific & medical partnerships infrastructure datasets knowledge Principles: Respect Diversity Openness Scalability Connectedness 3 CENTRE FOR POPULATION GENOMICS Strategic Plan 2021-2022 Vision: A world in which genomic information enables comprehensive disease prediction, accurate diagnosis and effective therapeutics for all people • The next decade will see a transformation of medicine and biology, driven in part by an explosion in our understanding of the connections between human genetic variation and individuals’ traits. This knowledge will allow the dissection of the biological basis of human traits, improve the prediction and diagnosis of disease, and accelerate the discovery and validation of new therapeutic targets. Key to these discoveries will be population genomics: the generation and analysis of massive-scale data sets of human genetic variation, combined with information on health and clinical outcomes. -
Advances in Genomics for Drug Development
G C A T T A C G G C A T genes Review Advances in Genomics for Drug Development Roberto Spreafico , Leah B. Soriaga, Johannes Grosse, Herbert W. Virgin and Amalio Telenti * Vir Biotechnology, Inc., San Francisco, CA 94158, USA; Rspreafi[email protected] (R.S.); [email protected] (L.B.S.); [email protected] (J.G.); [email protected] (H.W.V.) * Correspondence: [email protected] Received: 24 July 2020; Accepted: 13 August 2020; Published: 15 August 2020 Abstract: Drug development (target identification, advancing drug leads to candidates for preclinical and clinical studies) can be facilitated by genetic and genomic knowledge. Here, we review the contribution of population genomics to target identification, the value of bulk and single cell gene expression analysis for understanding the biological relevance of a drug target, and genome-wide CRISPR editing for the prioritization of drug targets. In genomics, we discuss the different scope of genome-wide association studies using genotyping arrays, versus exome and whole genome sequencing. In transcriptomics, we discuss the information from drug perturbation and the selection of biomarkers. For CRISPR screens, we discuss target discovery, mechanism of action and the concept of gene to drug mapping. Harnessing genetic support increases the probability of drug developability and approval. Keywords: druggability; loss-of-function; CRISPR 1. Introduction For over 20 years, genomics has been used as a tool for accelerating drug development. Various conceptual approaches and techniques assist target identification, target prioritization and tractability, as well as the prediction of outcomes from pharmacological perturbations. These basic premises are now supported by a rapid expansion of population genomics initiatives (sequencing or genotyping of hundreds of thousands of individuals), in-depth understanding of disease and drug perturbation at the tissue and single-cell level as measured by transcriptome analysis, and by the capacity to screen for loss of function or activation of genes, genome-wide, using CRISPR technologies. -
Comparative Evolution: Latent Potentials for Anagenetic Advance (Adaptive Shifts/Constraints/Anagenesis) G
Proc. Natl. Acad. Sci. USA Vol. 85, pp. 5141-5145, July 1988 Evolution Comparative evolution: Latent potentials for anagenetic advance (adaptive shifts/constraints/anagenesis) G. LEDYARD STEBBINS* AND DANIEL L. HARTLtt *Department of Genetics, University of California, Davis, CA 95616; and tDepartment of Genetics, Washington University School of Medicine, Box 8031, 660 South Euclid Avenue, Saint Louis, MO 63110 Contributed by G. Ledyard Stebbins, April 4, 1988 ABSTRACT One of the principles that has emerged from genetic variation available for evolutionary changes (2), a experimental evolutionary studies of microorganisms is that major concern of modem evolutionists is explaining how the polymorphic alleles or new mutations can sometimes possess a vast amount of genetic variation that actually exists can be latent potential to respond to selection in different environ- maintained. Given the fact that in complex higher organisms ments, although the alleles may be functionally equivalent or most new mutations with visible effects on phenotype are disfavored under typical conditions. We suggest that such deleterious, many biologists, particularly Kimura (3), have responses to selection in microorganisms serve as experimental sought to solve the problem by proposing that much genetic models of evolutionary advances that occur over much longer variation is selectively neutral or nearly so, at least at the periods of time in higher organisms. We propose as a general molecular level. Amidst a background of what may be largely evolutionary principle that anagenic advances often come from neutral or nearly neutral genetic variation, adaptive evolution capitalizing on preexisting latent selection potentials in the nevertheless occurs. While much of natural selection at the presence of novel ecological opportunity. -
Whole-Genome Analysis of Polymorphism and Divergence in Drosophila Simulans David J
Washington University School of Medicine Digital Commons@Becker Open Access Publications 2007 Population genomics: Whole-genome analysis of polymorphism and divergence in Drosophila simulans David J. Begun University of California - Davis Alisha K. Holloway University of California - Davis Kristian Stevens University of California - Davis LaDeana W. Hillier Washington University School of Medicine in St. Louis Yu-Ping Poh University of California - Davis See next page for additional authors Follow this and additional works at: https://digitalcommons.wustl.edu/open_access_pubs Part of the Medicine and Health Sciences Commons Recommended Citation Begun, David J.; Holloway, Alisha K.; Stevens, Kristian; Hillier, LaDeana W.; Poh, Yu-Ping; Hahn, Matthew W.; Nista, Phillip M.; Jones, Corbin D.; Kern, Andrew D.; Dewey, Colin N.; Pachter, Lior; Myers, Eugene; and Langley, Charles H., ,"Population genomics: Whole-genome analysis of polymorphism and divergence in Drosophila simulans." PLoS Biology.,. e310. (2007). https://digitalcommons.wustl.edu/open_access_pubs/877 This Open Access Publication is brought to you for free and open access by Digital Commons@Becker. It has been accepted for inclusion in Open Access Publications by an authorized administrator of Digital Commons@Becker. For more information, please contact [email protected]. Authors David J. Begun, Alisha K. Holloway, Kristian Stevens, LaDeana W. Hillier, Yu-Ping Poh, Matthew W. Hahn, Phillip M. Nista, Corbin D. Jones, Andrew D. Kern, Colin N. Dewey, Lior Pachter, Eugene Myers, and Charles H. Langley This open access publication is available at Digital Commons@Becker: https://digitalcommons.wustl.edu/open_access_pubs/877 PLoS BIOLOGY Population Genomics: Whole-Genome Analysis of Polymorphism and Divergence in Drosophila simulans David J. -
Population and Comparative Genomics Inform Our Understanding of Bacterial Species Diversity in the Soil
Chapter 11 Population and Comparative Genomics Inform Our Understanding of Bacterial Species Diversity in the Soil Margaret A. Riley 11.1 Introduction The diversity of soil bacteria is simply staggering. According to Torsvik and colleagues (Torsvik et al. 1990; Torsvik and Ovreas 2002), species diversity in soil samples is so high that our most powerful methods of estimation provide only the crudest measure of its magnitude. Nonetheless, many such estimates exist, and they suggest that a single gram of soil may contain over 10 billion microbial cells and more than 1,800 bacterial species (Torsvik and Ovreas 2002; Gans et al. 2005; Zhang et al. 2008). An equally compelling estimate is provided by Dykhuizen (Dykhuizen and Dean 2004), who examined levels of genetic diversity in soil bacteria and predicted that 30 g of forest soil contains over half a million species! As our methods of empirically estimating bacterial diversity improve, so to do our methods of mathematically refining these numbers. For example, some analyti- cal methods now take into account the fact that there are very few common soil species and untold numbers of rare species (Youssef and Elshahed 2009; Schloss and Handelsman 2006). These refinements push our estimates of bacterial diversity to over a million species per gram of soil (Gans et al. 2005). To put these numbers into perspective, similar studies of the human gastrointestinal tract predict a mere 400 distinct bacterial phylotypes (Rajilic-Stojanovic et al. 2007). This staggering level of species diversity is even more remarkable when one considers that the number of prokaryotes reported in the National Center for Biotechnology Informa- tion molecular database is only about 15,111 (Sayers et al. -
A Review on the Population Genomics of Transposable Elements
G C A T T A C G G C A T genes Review On the Population Dynamics of Junk: A Review on the Population Genomics of Transposable Elements Yann Bourgeois and Stéphane Boissinot * New York University Abu Dhabi, P.O. 129188 Saadiyat Island, Abu Dhabi, UAE; [email protected] * Correspondence: [email protected] Received: 4 April 2019; Accepted: 21 May 2019; Published: 30 May 2019 Abstract: Transposable elements (TEs) play an important role in shaping genomic organization and structure, and may cause dramatic changes in phenotypes. Despite the genetic load they may impose on their host and their importance in microevolutionary processes such as adaptation and speciation, the number of population genetics studies focused on TEs has been rather limited so far compared to single nucleotide polymorphisms (SNPs). Here, we review the current knowledge about the dynamics of transposable elements at recent evolutionary time scales, and discuss the mechanisms that condition their abundance and frequency. We first discuss non-adaptive mechanisms such as purifying selection and the variable rates of transposition and elimination, and then focus on positive and balancing selection, to finally conclude on the potential role of TEs in causing genomic incompatibilities and eventually speciation. We also suggest possible ways to better model TEs dynamics in a population genomics context by incorporating recent advances in TEs into the rich information provided by SNPs about the demography, selection, and intrinsic properties of genomes. Keywords: transposable elements; population genetics; selection; drift; coevolution 1. Introduction Transposable elements (TEs) are repetitive DNA sequences that are ubiquitous in the living world and have the ability to replicate and multiply within genomes. -
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. -
Genomic Sequencing of SARS-Cov-2: a Guide to Implementation for Maximum Impact on Public Health
Genomic sequencing of SARS-CoV-2 A guide to implementation for maximum impact on public health 8 January 2021 Genomic sequencing of SARS-CoV-2 A guide to implementation for maximum impact on public health 8 January 2021 Genomic sequencing of SARS-CoV-2: a guide to implementation for maximum impact on public health ISBN 978-92-4-001844-0 (electronic version) ISBN 978-92-4-001845-7 (print version) © World Health Organization 2021 Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Under the terms of this licence, you may copy, redistribute and adapt the work for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this work, there should be no suggestion that WHO endorses any specific organization, products or services. The use of the WHO logo is not permitted. If you adapt the work, then you must license your work under the same or equivalent Creative Commons licence. If you create a translation of this work, you should add the following disclaimer along with the suggested citation: “This translation was not created by the World Health Organization (WHO). WHO is not responsible for the content or accuracy of this translation. The original English edition shall be the binding and authentic edition”. Any mediation relating to disputes arising under the licence shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization (http://www.wipo.int/amc/en/mediation/rules/). -
Molecular Evolution
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Molecular Evolution An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Outline • Evolutionary Tree Reconstruction • “Out of Africa” hypothesis • Did we evolve from Neanderthals? • Distance Based Phylogeny • Neighbor Joining Algorithm • Additive Phylogeny • Least Squares Distance Phylogeny • UPGMA • Character Based Phylogeny • Small Parsimony Problem • Fitch and Sankoff Algorithms • Large Parsimony Problem • Evolution of Wings • HIV Evolution • Evolution of Human Repeats An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Early Evolutionary Studies • Anatomical features were the dominant criteria used to derive evolutionary relationships between species since Darwin till early 1960s • The evolutionary relationships derived from these relatively subjective observations were often inconclusive. Some of them were later proved incorrect An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Evolution and DNA Analysis: the Giant Panda Riddle • For roughly 100 years scientists were unable to figure out which family the giant panda belongs to • Giant pandas look like bears but have features that are unusual for bears and typical for raccoons, e.g., they do not hibernate • In 1985, Steven O’Brien and colleagues solved the giant panda classification problem using DNA sequences and algorithms An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Evolutionary Tree of Bears and Raccoons An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Evolutionary Trees: DNA-based Approach • 40 years ago: Emile Zuckerkandl and Linus Pauling brought reconstructing evolutionary relationships with DNA into the spotlight • In the first few years after Zuckerkandl and Pauling proposed using DNA for evolutionary studies, the possibility of reconstructing evolutionary trees by DNA analysis was hotly debated • Now it is a dominant approach to study evolution. -
Epigenetics Is Normal Science, but Don't Call It Lamarckian
Extended Abstract Volume 6:3, 2020 DOI: 6.2472/imedipce.2020.6.002 Journal of Clinical Epigenetics ISSN: 2472-1158 Open Access Epigenetics is Normal Science, But Don’t Call It Lamarckian David Penny New Zealand Extended Abstract they are general (but not necessarily universal) because they are found in Excavates, which many people consider are ancestral to other eukaryotes [6, 7]. Similarly, an important point is that Epigenetic mechanisms are increasingly being accepted as an integral methylation, phosphorylation, etc. of the DNA and of the histones is a part of normal science. Earlier genetic studies appear to have part of normal evolution (at least in eukaryotes that have the assumed that the only changes were ‘mutations’ as a result of a nucleosome structure) [8]. One of the most interesting aspects is change in the DNA sequence. If any such changes altered the amino methylation (and hydroxymethylation) of cytosine, and this is found in acid sequences then it was effectively a mutation, and if it occurred in many deep branching eukaryotes [9]. Typically there is methylation of a germline cell (including in unicellular organisms) then it could be cytosine molecules, especially in CpG (cytosine followed by guanine) passed on to subsequent generations. However, it is increasingly regions. The role of small RNAs in helping regulate the levels of apparent that other changes are also important, for example, changes protein expression is discussed in [10]. The main point here is that to methylation patterns, and to the histone proteins. Epigenetics was protein expression levels are very variable in all cells – it is not just sometimes consider ‘additional’ to classical genetics, but it is still fully classical genetics that is important; there are many epigenetic dependent on DNA, RNA and protein sequences, and at least since [1] mechanisms affecting the differences in protein expression. -
Using Classical Genetics Simulator (CGS) to Teach Students the Basics of Genetic Research
Tested Studies for Laboratory Teaching Proceedings of the Association for Biology Laboratory Education Vol. 33, 85–94, 2012 Using Classical Genetics Simulator (CGS) to Teach Students the Basics of Genetic Research Jean Heitz1, Mark Wolansky2, and Ben Adamczyk3 1University of Wisconsin, Zoology Department, 250 N. Mills Street, Madison WI 53706 USA 2University of Alberta, Department of Biological Science, Edmonton AB, CAN T6G 2E9 3Genedata, MA, A-1 Cranberry HI, Lexington MA 02421 USA ([email protected];[email protected];[email protected]) Some experiments are not well suited to large introductory labs because of space, time and funding constraints. Among these are classical Mendelian genetics investigations. Cyber labs can overcome these constraints. Classi- cal Genetics Simulator (CGS) gives students the opportunity to perform controlled crosses with model organisms much like a geneticist would do. CGS provides populations of Drosophila, Arabidopsis or mice with unknown patterns of inheritance and gives students the tools to design and perform experiments to discover these patterns. Students require an understanding of the what, why and how of solving genetic problems to successfully complete our cyber genetics lab activities. Keywords: Mendelian genetics, simulator, classical genetics simulator (CGS), linkage, monohybrid, dihybrid Introduction In large introductory biology classes some labs are dif- How can students get practice doing what geneticists ficult to do because of space, time and funding constraints. actually do? Among these are classical Mendelian genetics investiga- An obvious way for students to get practice doing what tions. As a result, we have turned to cyber labs. Working geneticists do is to conduct actual crosses.