Combined Study on DSI in Public and Private Databases and DSI Traceability
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In Silico Analysis of Single Nucleotide Polymorphisms
Central JSM Bioinformatics, Genomics and Proteomics Original Research *Corresponding author Anfal Ibrahim Bahereldeen, Department of medical laboratory sciences, Sudan University of Science and In silico analysis of Single Technology, Khartoum, Sudan; Tel: 249916060120; Email: [email protected] Submitted: 14 October 2019 Nucleotide Polymorphisms Accepted: 23 January 2020 Published: 27 January 2020 (SNPs) in Human HFE Gene ISSN: 2576-1102 Copyright coding region © 2020 Bahereldeen AI, et ail. OPEN ACCESS Anfal Ibrahim Bahereldeen1*, Reel Gamal Alfadil2, Hala Mohammed Ali2, Randa Musa Nasser2, Nada Tagelsir Eisa2, Keywords Wesal Hassan Mahmoud2, and Shaymaa Mohammedazeem • In silico • HFE gene, Polyphen-2 2 Osman • I mutant 1Department of Medical laboratory sciences, Sudan University of science and • Project hope Technology, Sudan 2Department of Medical laboratory sciences, Al-Neelain University, Sudan Abstract Background: HFE gene is a HLA class1-like molecule, expressed in the different cells and tissues, mutations on this gene reported to cause about 80-90% of Hereditary haemochromatosis (HHC) and it is increasing the risk of different diseases. In this study; we aimed to analysis the SNPs in HFE gene by using different computational methods. Methodology: We obtained HFE gene nsSNPs from dbSNP/NCBI database, Deleterious nsSNPs predicted by different bioinformatics servers including; SIFT, polyphen-2, I-mutant and SNPs & GO servers. Protein structure analysis done by using Project hope and RaptorX tools then visualized by Chimera software and function, interaction and network of HFE gene analysis done by Gene MANIA program. Results: SIFT and Polyphen-2 servers predicted 75 deleterious nsSNPs and nine polymorphisms from them predicted as highly damaging and disease associated. -
PROGRAM and ABSTRACTS for 2020 ANNUAL MEETING of the SOCIETY for GLYCOBIOLOGY November 9–12, 2020 Phoenix, AZ, USA 1017 2020 Sfg Virtual Meeting Preliminary Schedule
Downloaded from https://academic.oup.com/glycob/article/30/12/1016/5948902 by guest on 25 January 2021 PROGRAM AND ABSTRACTS FOR 2020 ANNUAL MEETING OF THE SOCIETY FOR GLYCOBIOLOGY November 9–12, 2020 Phoenix, AZ, USA 1017 2020 SfG Virtual Meeting Preliminary Schedule Mon. Nov 9 (Day 1) TOKYO ROME PACIFIC EASTERN EASTERN SESSION TIME TIME TIME START END TIME TIME 23:30 15:30 6:30 9:30 9:50 Welcome and Introduction - Michael Tiemeyer, CCRC UGA Downloaded from https://academic.oup.com/glycob/article/30/12/1016/5948902 by guest on 25 January 2021 23:30 15:30 6:30 9:50 – 12:36 Session 1: Glycobiology of Normal and Disordered Development | Chair: Kelly Ten-Hagen, NIH/NIDCR 23:50 15:50 6:50 9:50 10:10 KEYNOTE: “POGLUT1 mutations cause myopathy with reduced Notch signaling and α-dystroglycan hypoglycosylation” - Carmen Paradas Lopez, Biomedical Institute Sevilla 0:12 16:12 7:12 10:12 10:24 Poster Talk: “Regulation of Notch signaling by O-glycans in the intestine” – Mohd Nauman, Albert Einstein 0:26 16:26 7:26 10:26 10:38 Poster Talk: “Generation of an unbiased interactome for the tetratricopeptide repeat domain of the O-GlcNAc transferase indicates a role for the enzyme in intellectual disability” – Hannah Stephen, University of Georgia 0:40 16:30 7:30 10:40 10:50 Q&A 10:52 11:12 7:52 10:52 11:12 KEYNOTE: “Aberrations in N-cadherin Processing Drive PMM2-CDG Pathogenesis” - Heather Flanagan-Steet, Greenwood Genetics Center 1:14 11:26 8:14 11:14 11:26 Poster Talk: “Functional analyses of TMTC-type protein O-mannosyltransferases in Drosophila model -
The Transcriptional Landscape and Hub Genes Associated with Physiological Responses to Drought Stress in Pinus Tabuliformis
International Journal of Molecular Sciences Article The Transcriptional Landscape and Hub Genes Associated with Physiological Responses to Drought Stress in Pinus tabuliformis Tariq Pervaiz 1,† , Shuang-Wei Liu 1,†, Saleem Uddin 1 , Muhammad Waqas Amjid 2 , Shi-Hui Niu 1,* and Harry X. Wu 1,3,4,* 1 Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; [email protected] (T.P.); [email protected] (S.-W.L.); [email protected] (S.U.) 2 State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Germplasm Enhancement and Application Engineering Research Center (Ministry of Education), Nanjing Agricultural University, Nanjing 210095, China; [email protected] 3 Umea Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Linnaeus vag 6, SE-901 83 Umea, Sweden 4 CSIRO National Research Collection Australia, Black Mountain Laboratory, Canberra, ACT 2601, Australia * Correspondence: [email protected] (S.-H.N.); [email protected] (H.X.W.) † These authors contributed equally. Abstract: Drought stress has an extensive impact on regulating various physiological, metabolic, and molecular responses. In the present study, the Pinus tabuliformis transcriptome was studied to Citation: Pervaiz, T.; Liu, S.-W.; evaluate the drought-responsive genes using RNA- Sequencing approache. The results depicted Uddin, S.; Amjid, M.W.; Niu, S.-H.; that photosynthetic rate and H2O conductance started to decline under drought but recovered 24 h Wu, H.X. The Transcriptional after re-watering; however, the intercellular CO2 concentration (Ci) increased with the onset of Landscape and Hub Genes drought. -
Biological Databases
Biological Databases Biology Is A Data Science ● Hundreds of thousand of species ● Million of articles in scientific literature ● Genetic Information ○ Gene names ○ Phenotype of mutants ○ Location of genes/mutations on chromosomes ○ Linkage (relationships between genes) 2 Data and Metadata ● Data are “concrete” objects ○ e.g. number, tweet, nucleotide sequence ● Metadata describes properties of data ○ e.g. object is a number, each tweet has an author ● Database structure may contain metadata ○ Type of object (integer, float, string, etc) ○ Size of object (strings at most 4 characters long) ○ Relationships between data (chromosomes have zero or more genes) 3 What is a Database? ● A data collection that needs to be : ○ Organized ○ Searchable ○ Up-to-date ● Challenge: ○ change “meaningless” data into useful, accessible information 4 A spreadsheet can be a Database ● Rectangular data ● Structured ● No metadata ● Search tools: ○ Excel ○ grep ○ python/R 5 A filesystem can be a Database ● Hierarchical data ● Some metadata ○ File, symlink, etc ● Unstructured ● Search tools: ○ ls ○ find ○ locate 6 Organization and Types of Databases ● Every database has tools that: ○ Store ○ Extract ○ Modify ● Flat file databases (flat DBMS) ○ Simple, restrictive, table ● Hierarchical databases ○ Simple, restrictive, tables ● Relational databases (RDBMS) ○ Complex, versatile, tables ● Object-oriented databases (ODBMS) ● Data warehouses and distributed databases ● Unstructured databases (object store DBs) 7 Where do the data come from ? 8 Types of Biological Data -
Supplementary Methods
Supplementary methods Somatic mutation and gene expression data This section describes the somatic mutation and gene expression data used in our pathway and network analysis. Gene-level mutation data Pathway and network databases record interactions at the gene or protein level. Therefore, we combine somatic mutation data for coding and non-coding elements into gene-level scores using the following procedure. P-values from the PCAWG-2-5-9-14 analysis summarize the statistical significance of somatic mutations on these regions. For each gene, we use Fisher’s method to combine P-values for multiple regions that are associated to the gene to create three gene scores: (1) a coding gene score (GS-C); (2) a non-coding (promoter, 5’ UTR, 3’ UTR, and enhancer) gene score (GS-N); and (3) a combined coding-and-non-coding (coding, promoter, 5’ UTR, 3’ UTR, and enhancer) gene score (GS-CN). Mutation data We obtained and processed two sources of somatic mutation data on various coding and non- coding regions associated with one or more genes: (1) binary mutation data that describe the presence or absence of mutations in a region for each sample in a tumor cohort and (2) integrated driver score P-values that describe the statistical significance of mutations in a region across samples in a cohort. 1. For binary mutation data we used the following procedure: a. We obtained somatic mutations from the PCAWG MAF (syn7364923). b. We retained mutations in a pan-cancer tumor cohort that excludes samples from the lymphoma and melanoma tumor cohorts, i.e., the Lymph-BNHL, Lymph-CLL, Lymph-NOS, and Skin-Melanoma cohorts, as well as 69 hypermutated samples with over 30 mutations/MB, which are listed by donor (syn7894281) or aliquot ID (syn7814911). -
The Draft Genomes of Softshell Turtle and Green Sea Turtle Yield Insights
LETTERS OPEN The draft genomes of soft-shell turtle and green sea turtle yield insights into the development and evolution of the turtle-specific body plan Zhuo Wang1,12, Juan Pascual-Anaya2,12, Amonida Zadissa3,12, Wenqi Li4,12, Yoshihito Niimura5, Zhiyong Huang1, Chunyi Li4, Simon White3, Zhiqiang Xiong1, Dongming Fang1, Bo Wang1, Yao Ming1, Yan Chen1, Yuan Zheng1, Shigehiro Kuraku2, Miguel Pignatelli6, Javier Herrero6, Kathryn Beal6, Masafumi Nozawa7, Qiye Li1, Juan Wang1, Hongyan Zhang4, Lili Yu1, Shuji Shigenobu7, Junyi Wang1, Jiannan Liu4, Paul Flicek6, Steve Searle3, Jun Wang1,8,9, Shigeru Kuratani2, Ye Yin4, Bronwen Aken3, Guojie Zhang1,10,11 & Naoki Irie2 The unique anatomical features of turtles have raised Three major hypotheses have been proposed for the evolutionary unanswered questions about the origin of their unique body origin of turtles, including that they (i) constitute early-diverged rep- plan. We generated and analyzed draft genomes of the soft- tiles, called anapsids3, (ii) are a sister group of the lizard-snake-tuatara shell turtle (Pelodiscus sinensis) and the green sea turtle (Lepidosauria) clade4 or (iii) are closely related to a lineage that (Chelonia mydas); our results indicated the close relationship includes crocodilians and birds (Archosauria)5–8. Even using molecular of the turtles to the bird-crocodilian lineage, from which they approaches, inconsistency still remains6–9. To clarify the evolution of split ~267.9–248.3 million years ago (Upper Permian to Triassic). the turtle-specific body plan, we first addressed the question of evolu- We also found extensive expansion of olfactory receptor genes tionary origin of the turtle by performing the first genome-wide phylo- in these turtles. -
Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases
Genomics Proteomics Bioinformatics 18 (2020) 91–103 Genomics Proteomics Bioinformatics www.elsevier.com/locate/gpb www.sciencedirect.com PERSPECTIVE Quality Matters: Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases Qingyu Chen 1,*, Ramona Britto 2, Ivan Erill 3, Constance J. Jeffery 4, Arthur Liberzon 5, Michele Magrane 2, Jun-ichi Onami 6,7, Marc Robinson-Rechavi 8,9, Jana Sponarova 10, Justin Zobel 1,*, Karin Verspoor 1,* 1 School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia 2 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK 3 Department of Biological Sciences, University of Maryland, Baltimore, MD 21250, USA 4 Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, USA 5 Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 6 Japan Science and Technology Agency, National Bioscience Database Center, Tokyo 102-8666, Japan 7 National Institute of Health Sciences, Tokyo 158-8501, Japan 8 Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland 9 Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland 10 Nebion AG, 8048 Zurich, Switzerland Received 8 December 2017; revised 24 October 2018; accepted 14 December 2018 Available online 9 July 2020 Handled by Zhang Zhang Introduction assembled, annotated, and ultimately submitted to primary nucleotide databases such as GenBank [2], European Nucleo- tide Archive (ENA) [3], and DNA Data Bank of Japan Biological databases represent an extraordinary collective vol- (DDBJ) [4] (collectively known as the International Nucleotide ume of work. -
A Draft Genome Assembly of the Eastern Banjo Frog Limnodynastes Dumerilii Dumerilii (Anura: Limnodynastidae)
bioRxiv preprint doi: https://doi.org/10.1101/2020.03.03.971721; this version posted May 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 A draft genome assembly of the eastern banjo frog Limnodynastes dumerilii 2 dumerilii (Anura: Limnodynastidae) 3 Qiye Li1,2, Qunfei Guo1,3, Yang Zhou1, Huishuang Tan1,4, Terry Bertozzi5,6, Yuanzhen Zhu1,7, 4 Ji Li2,8, Stephen Donnellan5, Guojie Zhang2,8,9,10* 5 6 1 BGI-Shenzhen, Shenzhen 518083, China 7 2 State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, 8 Chinese Academy of Sciences, Kunming 650223, China 9 3 College of Life Science and Technology, Huazhong University of Science and Technology, 10 Wuhan 430074, China 11 4 Center for Informational Biology, University of Electronic Science and Technology of China, 12 Chengdu 611731, China 13 5 South Australian Museum, North Terrace, Adelaide 5000, Australia 14 6 School of Biological Sciences, University of Adelaide, North Terrace, Adelaide 5005, 15 Australia 16 7 School of Basic Medicine, Qingdao University, Qingdao 266071, China 17 8 China National Genebank, BGI-Shenzhen, Shenzhen 518120, China 18 9 Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, 19 650223, Kunming, China 20 10 Section for Ecology and Evolution, Department of Biology, University of Copenhagen, DK- 21 2100 Copenhagen, Denmark 22 * Correspondence: [email protected] (G.Z.). -
The Mouse Gene Expression Database (GXD): 2021 Update
The Jackson Laboratory The Mouseion at the JAXlibrary Faculty Research 2021 Faculty Research 1-8-2021 The mouse Gene Expression Database (GXD): 2021 update. Richard M. Baldarelli Constance M. Smith Jacqueline H. Finger Terry F. Hayamizu Ingeborg J. McCright See next page for additional authors Follow this and additional works at: https://mouseion.jax.org/stfb2021 Part of the Life Sciences Commons, and the Medicine and Health Sciences Commons Authors Richard M. Baldarelli, Constance M. Smith, Jacqueline H. Finger, Terry F. Hayamizu, Ingeborg J. McCright, Jingxia Xu, David R. Shaw, Jonathan S Beal, Olin Blodgett, Jeff Campbell, Lori E. Corbani, Pete J. Frost, Sharon C Giannatto, Dave Miers, James A. Kadin, Joel E Richardson, and Martin Ringwald D924–D931 Nucleic Acids Research, 2021, Vol. 49, Database issue Published online 26 October 2020 doi: 10.1093/nar/gkaa914 The mouse Gene Expression Database (GXD): 2021 update Richard M. Baldarelli, Constance M. Smith, Jacqueline H. Finger, Terry F. Hayamizu, Ingeborg J. McCright, Jingxia Xu, David R. Shaw, Jonathan S. Beal, Olin Blodgett, Jeffrey Campbell, Lori E. Corbani, Pete J. Frost, Sharon C. Giannatto, Dave B. Miers, Downloaded from https://academic.oup.com/nar/article/49/D1/D924/5940496 by Jackson Laboratory user on 27 January 2021 James A. Kadin, Joel E. Richardson and Martin Ringwald * The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA Received September 14, 2020; Revised October 01, 2020; Editorial Decision October 02, 2020; Accepted October 02, 2020 ABSTRACT INTRODUCTION The Gene Expression Database (GXD; www. The longstanding objective of GXD has been to capture informatics.jax.org/expression.shtml) is an exten- and integrate different types of mouse developmental ex- sive and well-curated community resource of mouse pression information, with a focus on endogenous gene ex- developmental gene expression information. -
Efficient and Stable Metabarcoding Sequencing Data Using a DNBSEQ-G400 Sequencer Validated by Comprehensive Community Analyses
DATARELEASE Efficient and stable metabarcoding sequencing data using a DNBSEQ-G400 sequencer validated by comprehensive community analyses Xiaohuan Sun1, Yue-Hua Hu2,*, Jingjing Wang3, Chao Fang1,4, Jiguang Li3, Mo Han1, Xiaofang Wei3, Haotian Zheng1,5, Xiaoqing Luo1,6, Yangyang Jia1, Meihua Gong3, Liang Xiao1 and Zewei Song1,* 1 BGI-Shenzhen, Shenzhen 518083, China 2 CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China 3 MGI, BGI-Shenzhen, Shenzhen 518083, China 4 Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, BGI-Shenzhen, Shenzhen 518083, China 5 BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China 6 State Key Laboratory of Biocontrol and Guangdong Provincial Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, China ABSTRACT Metabarcoding is a widely used method for fast characterization of microbial communities in complex environmental samples. However, the selction of sequencing platform can have a noticeable effect on the estimated community composition. Here, we evaluated the metabarcoding performance of a DNBSEQ-G400 sequencer developed by MGI Tech using 16S and internal transcribed spacer (ITS) markers to investigate bacterial and fungal mock communities, as well as the ITS2 marker to investigate the fungal community of 1144 soil samples, with additional technical replicates. We show that highly accurate sequencing of bacterial and fungal communities is achievable using DNBSEQ-G400. Measures of diversity and correlation from soil metabarcoding showed that the results correlated highly with those of different machines of the Submitted: 22 December 2020 same model, as well as between different sequencing modes (single-end 400 bp and paired-end Accepted: 18 March 2021 200 bp). -
Robust Benchmark Structural Variant Calls of an Asian Using the State-Of-Art Long Fragment Sequencing Technologies
bioRxiv preprint doi: https://doi.org/10.1101/2020.08.10.245308; this version posted August 12, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Du X et al / Robust SV Benchmark of An Asian using long-sequencing 1 Robust Benchmark Structural Variant Calls of An Asian Using the 2 State-of-Art Long Fragment Sequencing Technologies 3 4 Xiao Du2,7,#, Lili Li1,#, Fan Liang3,#, Sanyang Liu4,#, Wenxin Zhang1, Shuai Sun2,7, Yuhui 5 Sun2,8, Fei Fan5,8, Linying Wang5,8, Xinming Liang6, Weijin Qiu6, Guangyi Fan2,7, Ou 6 Wang5,8, Weifei Yang4, Jiezhong Zhang4, Yuhui Xiao3, Yang Wang3, Depeng Wang3,*, 7 Shoufang Qu1,*, Fang Chen5,6,*, Jie Huang1,* 8 9 1 National Institutes for food and drug Control (NIFDC), No.2, Tiantan Xili Dongcheng 10 District, Beijing 10050, China. 11 2 BGI-Qingdao, BGI-Shenzhen, Qingdao 266555, China 12 3 GrandOmics Biosciences, Beijing 102200, China. 13 4 Annoroad Gene Technology (Beijing) Co., Ltd, Beijing 102200, China. 14 5 BGI-Shenzhen, Shenzhen 518083, China 15 6 MGI, BGI-Shenzhen, Shenzhen 518083, China. 16 7 State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen 518083, China 17 8 China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China. 18 19 # These authors contributed equally. 20 * Correspondence authors. 21 Email: [email protected] (Huang J), [email protected] (Chen F), 22 [email protected] (Qu S), [email protected] (Wang D). 23 24 25 Total word counts (from “Introduction” to “Materials and methods”): 5827 26 Total figures: 4 27 Total tables: 2 28 Total supplementary figures: 18 29 Total supplementary tables: 3 30 Total supplementary files: 1 31 Number of all the Reference: 48 32 Number of the Reference from 2014: 28 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.08.10.245308; this version posted August 12, 2020. -
Bioinformatics: a Practical Guide to the Analysis of Genes and Proteins, Second Edition Andreas D
BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto BIOINFORMATICS SECOND EDITION METHODS OF BIOCHEMICAL ANALYSIS Volume 43 BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or ALL CAPITAL LETTERS. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright ᭧ 2001 by John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic or mechanical, including uploading, downloading, printing, decompiling, recording or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher.