Interactive Tutorial 1: SNP Database Resources
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Genetic and Environmental Exposures Constrain Epigenetic Drift Over the Human Life Course
Downloaded from genome.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press Research Genetic and environmental exposures constrain epigenetic drift over the human life course Sonia Shah,1,9 Allan F. McRae,1,9 Riccardo E. Marioni,1,2,3,9 Sarah E. Harris,2,3 Jude Gibson,4 Anjali K. Henders,5 Paul Redmond,6 Simon R. Cox,3,6 Alison Pattie,6 Janie Corley,6 Lee Murphy,4 Nicholas G. Martin,5 Grant W. Montgomery,5 John M. Starr,3,7 Naomi R. Wray,1,10 Ian J. Deary,3,6,10 and Peter M. Visscher1,3,8,10 1Queensland Brain Institute, The University of Queensland, Brisbane, 4072, Queensland, Australia; 2Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom; 3Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom; 4Wellcome Trust Clinical Research Facility, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, United Kingdom; 5QIMR Berghofer Medical Research Institute, Brisbane, 4029, Queensland, Australia; 6Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom; 7Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom; 8University of Queensland Diamantina Institute, Translational Research Institute, The University of Queensland, Brisbane, 4072, Queensland, Australia Epigenetic mechanisms such as DNA methylation (DNAm) are essential for regulation of gene expression. DNAm is dy- namic, influenced by both environmental and genetic factors. Epigenetic drift is the divergence of the epigenome as a function of age due to stochastic changes in methylation. -
Small Variants Frequently Asked Questions (FAQ) Updated September 2011
Small Variants Frequently Asked Questions (FAQ) Updated September 2011 Summary Information for each Genome .......................................................................................................... 3 How does Complete Genomics map reads and call variations? ........................................................................... 3 How do I assess the quality of a genome produced by Complete Genomics?................................................ 4 What is the difference between “Gross mapping yield” and “Both arms mapped yield” in the summary file? ............................................................................................................................................................................. 5 What are the definitions for Fully Called, Partially Called, Half-Called and No-Called?............................ 5 In the summary-[ASM-ID].tsv file, how is the number of homozygous SNPs calculated? ......................... 5 In the summary-[ASM-ID].tsv file, how is the number of heterozygous SNPs calculated? ....................... 5 In the summary-[ASM-ID].tsv file, how is the total number of SNPs calculated? .......................................... 5 In the summary-[ASM-ID].tsv file, what regions of the genome are included in the “exome”? .............. 6 In the summary-[ASM-ID].tsv file, how is the number of SNPs in the exome calculated? ......................... 6 In the summary-[ASM-ID].tsv file, how are variations in potentially redundant regions of the genome counted? ..................................................................................................................................................................... -
A Pilot Analysis of DNA Methylation in Candidate Genes in Brain
cells Article Epigenetic Study in Parkinson’s Disease: A Pilot Analysis of DNA Methylation in Candidate Genes in Brain Luis Navarro-Sánchez 1 , Beatriz Águeda-Gómez 1, Silvia Aparicio 1 and Jordi Pérez-Tur 1,2,3,* 1 Unitat de Genètica Molecular, Instituto de Biomedicina de Valencia, CSIC, 46010 València, Spain; [email protected] (L.N.-S.); [email protected] (B.Á.-G.); [email protected] (S.A.) 2 Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), 46010 València, Spain 3 Unidad Mixta de Genética y Neurología, Instituto de Investigación Sanitaria La Fe, 46026 València, Spain * Correspondence: [email protected]; Tel.: +34-96-339-1755 Received: 25 July 2018; Accepted: 21 September 2018; Published: 26 September 2018 Abstract: Efforts have been made to understand the pathophysiology of Parkinson’s disease (PD). A significant number of studies have focused on genetics, despite the fact that the described pathogenic mutations have been observed only in around 10% of patients; this observation supports the fact that PD is a multifactorial disorder. Lately, differences in miRNA expression, histone modification, and DNA methylation levels have been described, highlighting the importance of epigenetic factors in PD etiology. Taking all this into consideration, we hypothesized that an alteration in the level of methylation in PD-related genes could be related to disease pathogenesis, possibly due to alterations in gene expression. After analysing promoter regions of five PD-related genes in three brain regions by pyrosequencing, we observed some differences in DNA methylation levels (hypo and hypermethylation) in substantia nigra in some CpG dinucleotides that, possibly through an alteration in Sp1 binding, could alter their expression. -
Expressed Sequence Tag Analysis of the Response of Apple
Physiologia Plantarum 133: 298–317. 2008 Copyright ª Physiologia Plantarum 2008, ISSN 0031-9317 Expressed sequence tag analysis of the response of apple (Malus x domestica ‘Royal Gala’) to low temperature and water deficit Michael Wisniewskia,*, Carole Bassetta,*, John Norellia, Dumitru Macarisina, Timothy Artlipa, Ksenija Gasicb and Schuyler Korbanb aUnited States Department of Agriculture – Agricultural Research Service (USDA-ARS), Appalachian Fruit Research Station, 2217 Wiltshire Road, Kearneysville, WV 25430, USA bDepartment of Natural Resources and Environmental Sciences, University of Illinois, 310 ERML, 1201 W. Gregory Drive, Urbana, IL 61801, USA Correspondence Leaf, bark, xylem and root tissues were used to make nine cDNA libraries from *Corresponding author, non-stressed (control) ‘Royal Gala’ apple trees, and from ‘Royal Gala’ trees e-mail: [email protected]; exposed to either low temperature (5°C for 24 h) or water deficit (45% of [email protected] saturated pot mass for 2 weeks). Over 22 600 clones from the nine libraries # Received 26 September 2007; revised 3 were subjected to 5 single-pass sequencing, clustered and annotated using January 2008 BLASTX. The number of clusters in the libraries ranged from 170 to 1430. Regarding annotation of the sequences, BLASTX analysis indicated that within doi: 10.1111/j.1399-3054.2008.01063.x the libraries 65–72% of the clones had a high similarity to known function genes, 6–15% had no functional assignment and 15–26% were completely novel. The expressed sequence tags were combined into three classes (control, low-temperature and water deficit) and the annotated genes in each class were placed into 1 of 10 different functional categories. -
Dbsnp Resources in the UCSC Database Today We Will Discuss
dbSNP resources in the UCSC database Today we will discuss some of the variation data from dbSNP as displayed on the UCSC Genome Browser. We will start at genome.ucsc.edu at the main Browser page and we will ‘reset all user settings’, which brings us to the most recent genome assembly, hg38. [0:28 Set up GB to hg18 defaults] So to begin, let’s go down to hg18, one of the earlier version of the genome, and we will hit the go button. At the Browser graphic page, we will hit the ‘hide all’ button to turn all the data tracks off. [0:45 Turn on SNPs (130) track] Scrolling to the bottom of the screen, to the Variation and Repeats bluebar group, we’ll draw your attention to the dbSNP track, SNPs version 130. In addition, we have earlier versions: 129, 128 and 126. At the time of version 130, before moving to hg19, dbSNP was mapping all new SNPs to the hg18 genome assembly. If we click into the link above the pulldown menu, we can see that this table was last updated in 2009. We’re going to turn the track on to ‘dense’ and then ‘Submit’. This shows us all the SNPs in version 130, and let’s turn on the gene track, UCSC Genes to ‘pack’so we will see the scale. We have 310,000 bases and we have the default gene GABRA3. The region is annotated by many SNPs. Now let’s zoom into one of the exons here in the middle of the screen, and you can that at this resolution, around 5,000 basepairs, there are a dozen or so SNPs in view. -
1 a Primer on Molecular Biology
1APrimer on Molecular Biology Alexander Zien Modern molecular biology provides a rich source of challenging machine learning problems. This tutorial chapter aims to provide the necessary biological background knowledge required to communicate with biologists and to understand and properly formalize a number of most interesting problems in this application domain. The largest part of the chapter (its first section) is devoted to the cell as the basic unit of life. Four aspects of cells are reviewed in sequence: (1) the molecules that cells make use of (above all, proteins, RNA, and DNA); (2) the spatial organization of cells (“compartmentalization”); (3) the way cells produce proteins (“protein expression”); and (4) cellular communication and evolution (of cells and organisms). In the second section, an overview is provided of the most frequent measurement technologies, data types, and data sources. Finally, important open problems in the analysis of these data (bioinformatics challenges) are briefly outlined. 1.1 The Cell The basic unit of all (biological) life is the cell. A cell is basically a watery solution of certain molecules, surrounded by a lipid (fat) membrane. Typical sizes of cells range from 1 µm (bacteria) to 100 µm (plant cells). The most important properties Life of a living cell (and, in fact, of life itself) are the following: It consists of a set of molecules that is separated from the exterior (as a human being is separated from his or her surroundings). It has a metabolism, that is, it can take up nutrients and convert them into other molecules and usable energy. The cell uses nutrients to renew its constituents, to grow, and to drive its actions (just like a human does). -
The Genetics of Bipolar Disorder
Molecular Psychiatry (2008) 13, 742–771 & 2008 Nature Publishing Group All rights reserved 1359-4184/08 $30.00 www.nature.com/mp FEATURE REVIEW The genetics of bipolar disorder: genome ‘hot regions,’ genes, new potential candidates and future directions A Serretti and L Mandelli Institute of Psychiatry, University of Bologna, Bologna, Italy Bipolar disorder (BP) is a complex disorder caused by a number of liability genes interacting with the environment. In recent years, a large number of linkage and association studies have been conducted producing an extremely large number of findings often not replicated or partially replicated. Further, results from linkage and association studies are not always easily comparable. Unfortunately, at present a comprehensive coverage of available evidence is still lacking. In the present paper, we summarized results obtained from both linkage and association studies in BP. Further, we indicated new potential interesting genes, located in genome ‘hot regions’ for BP and being expressed in the brain. We reviewed published studies on the subject till December 2007. We precisely localized regions where positive linkage has been found, by the NCBI Map viewer (http://www.ncbi.nlm.nih.gov/mapview/); further, we identified genes located in interesting areas and expressed in the brain, by the Entrez gene, Unigene databases (http://www.ncbi.nlm.nih.gov/entrez/) and Human Protein Reference Database (http://www.hprd.org); these genes could be of interest in future investigations. The review of association studies gave interesting results, as a number of genes seem to be definitively involved in BP, such as SLC6A4, TPH2, DRD4, SLC6A3, DAOA, DTNBP1, NRG1, DISC1 and BDNF. -
Dbsnp Columns
dbSNP Columns RS dbSNP ID (i.e. rs number) RSPOS Chr position reported in dbSNP RV RS orientation is reversed Variation Property. Documentation is at VP ftp://ftp.ncbi.nlm.nih.gov/snp/specs/dbSNP_BitField_latest.pdf Pairs each of gene symbol:gene id. The gene symbol and id are delimited by a GENEINFO colon (:) and each pair is delimited by a vertical bar (|) dbSNPBuildID First dbSNP Build for RS SAO Variant Allele Origin: 0 - unspecified, 1 - Germline, 2 - Somatic, 3 - Both Variant Suspect Reason Codes (may be more than one value added together) 0 - unspecified, 1 - Paralog, 2 - byEST, 4 - oldAlign, 8 - Para_EST, 16 - SSR 1kg_failed, 1024 - other WGT Weight, 00 - unmapped, 1 - weight 1, 2 - weight 2, 3 - weight 3 or more VC Variation Class PM Variant is Precious(Clinical,Pubmed Cited) Provisional Third Party Annotation(TPA) (currently rs from PHARMGKB who will TPA give phenotype data) PMC Links exist to PubMed Central article S3D Has 3D structure - SNP3D table SLO Has SubmitterLinkOut - From SNP->SubSNP->Batch.link_out Has non-synonymous frameshift A coding region variation where one allele in NSF the set changes all downstream amino acids. FxnClass = 44 Has non-synonymous missense A coding region variation where one allele in NSM the set changes protein peptide. FxnClass = 42 Has non-synonymous nonsense A coding region variation where one allele in NSN the set changes to STOP codon (TER). FxnClass = 41 Has reference A coding region variation where one allele in the set is identical REF to the reference sequence. FxnCode = 8 Has synonymous A coding region variation where one allele in the set does not SYN change the encoded amino acid. -
Biomolecule and Bioentity Interaction Databases in Systems Biology: a Comprehensive Review
biomolecules Review Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review Fotis A. Baltoumas 1,* , Sofia Zafeiropoulou 1, Evangelos Karatzas 1 , Mikaela Koutrouli 1,2, Foteini Thanati 1, Kleanthi Voutsadaki 1 , Maria Gkonta 1, Joana Hotova 1, Ioannis Kasionis 1, Pantelis Hatzis 1,3 and Georgios A. Pavlopoulos 1,3,* 1 Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; zafeiropoulou@fleming.gr (S.Z.); karatzas@fleming.gr (E.K.); [email protected] (M.K.); [email protected] (F.T.); voutsadaki@fleming.gr (K.V.); [email protected] (M.G.); hotova@fleming.gr (J.H.); [email protected] (I.K.); hatzis@fleming.gr (P.H.) 2 Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark 3 Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece * Correspondence: baltoumas@fleming.gr (F.A.B.); pavlopoulos@fleming.gr (G.A.P.); Tel.: +30-210-965-6310 (G.A.P.) Abstract: Technological advances in high-throughput techniques have resulted in tremendous growth Citation: Baltoumas, F.A.; of complex biological datasets providing evidence regarding various biomolecular interactions. Zafeiropoulou, S.; Karatzas, E.; To cope with this data flood, computational approaches, web services, and databases have been Koutrouli, M.; Thanati, F.; Voutsadaki, implemented to deal with issues such as data integration, visualization, exploration, organization, K.; Gkonta, M.; Hotova, J.; Kasionis, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more I.; Hatzis, P.; et al. Biomolecule and and more difficult for an end user to know what the scope and focus of each repository is and how Bioentity Interaction Databases in redundant the information between them is. -
The Impact of Complement Genes on the Risk of Late-Onset Alzheimer's
G C A T T A C G G C A T genes Article The Impact of Complement Genes on the Risk of Late-Onset Alzheimer’s Disease Sarah M. Carpanini 1,2,† , Janet C. Harwood 3,† , Emily Baker 1, Megan Torvell 1,2, The GERAD1 Consortium ‡, Rebecca Sims 3 , Julie Williams 1 and B. Paul Morgan 1,2,* 1 UK Dementia Research Institute at Cardiff University, School of Medicine, Cardiff, CF24 4HQ, UK; [email protected] (S.M.C.); [email protected] (E.B.); [email protected] (M.T.); [email protected] (J.W.) 2 Division of Infection and Immunity, School of Medicine, Systems Immunity Research Institute, Cardiff University, Cardiff, CF14 4XN, UK 3 Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK; [email protected] (J.C.H.); [email protected] (R.S.) * Correspondence: [email protected] † These authors contributed equally to this work. ‡ Data used in the preparation of this article were obtained from the Genetic and Environmental Risk for Alzheimer’s disease (GERAD1) Consortium. As such, the investigators within the GERAD1 consortia contributed to the design and implementation of GERAD1 and/or provided data but did not participate in analysis or writing of this report. A full list of GERAD1 investigators and their affiliations is included in Supplementary File S1. Abstract: Late-onset Alzheimer’s disease (LOAD), the most common cause of dementia, and a huge global health challenge, is a neurodegenerative disease of uncertain aetiology. To deliver Citation: Carpanini, S.M.; Harwood, effective diagnostics and therapeutics, understanding the molecular basis of the disease is essential. -
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BMC Bioinformatics BioMed Central Methodology article Open Access SNPHunter: a bioinformatic software for single nucleotide polymorphism data acquisition and management Lin Wang1, Simin Liu2,3, Tianhua Niu*†1,3 and Xin Xu†1 Address: 1Program for Population Genetics, Harvard School of Public Health, Boston, MA 02115, USA, 2Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA and 3Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA Email: Lin Wang - [email protected]; Simin Liu - [email protected]; Tianhua Niu* - [email protected]; Xin Xu - [email protected] * Corresponding author †Equal contributors Published: 18 March 2005 Received: 12 January 2005 Accepted: 18 March 2005 BMC Bioinformatics 2005, 6:60 doi:10.1186/1471-2105-6-60 This article is available from: http://www.biomedcentral.com/1471-2105/6/60 © 2005 Wang et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Single nucleotide polymorphisms (SNPs) provide an important tool in pinpointing susceptibility genes for complex diseases and in unveiling human molecular evolution. Selection and retrieval of an optimal SNP set from publicly available databases have emerged as the foremost bottlenecks in designing large-scale linkage disequilibrium studies, particularly in case-control settings. Results: We describe the architectural structure and implementations of a novel software program, SNPHunter, which allows for both ad hoc-mode and batch-mode SNP search, automatic SNP filtering, and retrieval of SNP data, including physical position, function class, flanking sequences at user-defined lengths, and heterozygosity from NCBI dbSNP. -
A Computational Biology Database Digest: Data, Data Analysis, and Data Management
A Computational Biology Database Digest: Data, Data Analysis, and Data Management Franc¸ois Bry and Peer Kroger¨ Institute for Computer Science, University of Munich, Germany http://www.pms.informatik.uni-muenchen.de [email protected] [email protected] Abstract Computational Biology or Bioinformatics has been defined as the application of mathematical and Computer Science methods to solving problems in Molecular Biology that require large scale data, computation, and analysis [26]. As expected, Molecular Biology databases play an essential role in Computational Biology research and development. This paper introduces into current Molecular Biology databases, stressing data modeling, data acquisition, data retrieval, and the integration of Molecular Biology data from different sources. This paper is primarily intended for an audience of computer scientists with a limited background in Biology. 1 Introduction “Computational biology is part of a larger revolution that will affect how all of sci- ence is conducted. This larger revolution is being driven by the generation and use of information in all forms and in enormous quantities and requires the development of intelligent systems for gathering, storing and accessing information.” [26] As this statement suggests, Molecular Biology databases play a central role in Computational Biology [11]. Currently, there are several hundred Molecular Biology databases – their number probably lies between 500 and 1,000. Well-known examples are DDBJ [112], EMBL [9], GenBank [14], PIR [13], and SWISS-PROT [8]. It is so difficult to keep track of Molecular Biology databases that a “meta- 1 database”, DBcat [33], has been developed for this purpose. Nonetheless, DBcat by far does not report on all activities in the rapidly evolving field of Molecular Biology databases.