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The ELIXIR Core Data Resources: Fundamental Infrastructure for The
Supplementary Data: The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences The “Supporting Material” referred to within this Supplementary Data can be found in the Supporting.Material.CDR.infrastructure file, DOI: 10.5281/zenodo.2625247 (https://zenodo.org/record/2625247). Figure 1. Scale of the Core Data Resources Table S1. Data from which Figure 1 is derived: Year 2013 2014 2015 2016 2017 Data entries 765881651 997794559 1726529931 1853429002 2715599247 Monthly user/IP addresses 1700660 2109586 2413724 2502617 2867265 FTEs 270 292.65 295.65 289.7 311.2 Figure 1 includes data from the following Core Data Resources: ArrayExpress, BRENDA, CATH, ChEBI, ChEMBL, EGA, ENA, Ensembl, Ensembl Genomes, EuropePMC, HPA, IntAct /MINT , InterPro, PDBe, PRIDE, SILVA, STRING, UniProt ● Note that Ensembl’s compute infrastructure physically relocated in 2016, so “Users/IP address” data are not available for that year. In this case, the 2015 numbers were rolled forward to 2016. ● Note that STRING makes only minor releases in 2014 and 2016, in that the interactions are re-computed, but the number of “Data entries” remains unchanged. The major releases that change the number of “Data entries” happened in 2013 and 2015. So, for “Data entries” , the number for 2013 was rolled forward to 2014, and the number for 2015 was rolled forward to 2016. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences 1 Figure 2: Usage of Core Data Resources in research The following steps were taken: 1. API calls were run on open access full text articles in Europe PMC to identify articles that mention Core Data Resource by name or include specific data record accession numbers. -
Dual Proteome-Scale Networks Reveal Cell-Specific Remodeling of the Human Interactome
bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome Edward L. Huttlin1*, Raphael J. Bruckner1,3, Jose Navarrete-Perea1, Joe R. Cannon1,4, Kurt Baltier1,5, Fana Gebreab1, Melanie P. Gygi1, Alexandra Thornock1, Gabriela Zarraga1,6, Stanley Tam1,7, John Szpyt1, Alexandra Panov1, Hannah Parzen1,8, Sipei Fu1, Arvene Golbazi1, Eila Maenpaa1, Keegan Stricker1, Sanjukta Guha Thakurta1, Ramin Rad1, Joshua Pan2, David P. Nusinow1, Joao A. Paulo1, Devin K. Schweppe1, Laura Pontano Vaites1, J. Wade Harper1*, Steven P. Gygi1*# 1Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA. 2Broad Institute, Cambridge, MA, 02142, USA. 3Present address: ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, 02115, USA. 4Present address: Merck, West Point, PA, 19486, USA. 5Present address: IQ Proteomics, Cambridge, MA, 02139, USA. 6Present address: Vor Biopharma, Cambridge, MA, 02142, USA. 7Present address: Rubius Therapeutics, Cambridge, MA, 02139, USA. 8Present address: RPS North America, South Kingstown, RI, 02879, USA. *Correspondence: [email protected] (E.L.H.), [email protected] (J.W.H.), [email protected] (S.P.G.) #Lead Contact: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
Sequence Motifs, Correlations and Structural Mapping of Evolutionary
Talk overview • Sequence profiles – position specific scoring matrix • Psi-blast. Automated way to create and use sequence Sequence motifs, correlations profiles in similarity searches and structural mapping of • Sequence patterns and sequence logos evolutionary data • Bioinformatic tools which employ sequence profiles: PFAM BLOCKS PROSITE PRINTS InterPro • Correlated Mutations and structural insight • Mapping sequence data on structures: March 2011 Eran Eyal Conservations Correlations PSSM – position specific scoring matrix • A position-specific scoring matrix (PSSM) is a commonly used representation of motifs (patterns) in biological sequences • PSSM enables us to represent multiple sequence alignments as mathematical entities which we can work with. • PSSMs enables the scoring of multiple alignments with sequences, or other PSSMs. PSSM – position specific scoring matrix Assuming a string S of length n S = s1s2s3...sn If we want to score this string against our PSSM of length n (with n lines): n alignment _ score = m ∑ s j , j j=1 where m is the PSSM matrix and sj are the string elements. PSSM can also be incorporated to both dynamic programming algorithms and heuristic algorithms (like Psi-Blast). Sequence space PSI-BLAST • For a query sequence use Blast to find matching sequences. • Construct a multiple sequence alignment from the hits to find the common regions (consensus). • Use the “consensus” to search again the database, and get a new set of matching sequences • Repeat the process ! Sequence space Position-Specific-Iterated-BLAST • Intuition – substitution matrices should be specific to sites and not global. – Example: penalize alanine→glycine more in a helix •Idea – Use BLAST with high stringency to get a set of closely related sequences. -
A Beginner's Guide to Eukaryotic Genome Annotation
REVIEWS STUDY DESIGNS A beginner’s guide to eukaryotic genome annotation Mark Yandell and Daniel Ence Abstract | The falling cost of genome sequencing is having a marked impact on the research community with respect to which genomes are sequenced and how and where they are annotated. Genome annotation projects have generally become small-scale affairs that are often carried out by an individual laboratory. Although annotating a eukaryotic genome assembly is now within the reach of non-experts, it remains a challenging task. Here we provide an overview of the genome annotation process and the available tools and describe some best-practice approaches. Genome annotation Sequencing costs have fallen so dramatically that a sin- with some basic UNIX skills, ‘do-it-yourself’ genome A term used to describe two gle laboratory can now afford to sequence large, even annotation projects are quite feasible using present- distinct processes. ‘Structural’ human-sized, genomes. Ironically, although sequencing day tools. Here we provide an overview of the eukary- genome annotation is the has become easy, in many ways, genome annotation has otic genome annotation process, describe the available process of identifying genes and their intron–exon become more challenging. Several factors are respon- toolsets and outline some best-practice approaches. structures. ‘Functional’ genome sible for this. First, the shorter read lengths of second- annotation is the process of generation sequencing platforms mean that current Assembly and annotation: an overview attaching meta-data such as genome assemblies rarely attain the contiguity of the Assembly. The first step towards the successful annota- gene ontology terms to classic shotgun assemblies of the Drosophila mela- tion of any genome is determining whether its assem- structural annotations. -
Downloaded from the National Center for Cide Resistance Mechanisms
Zhou et al. Parasites & Vectors (2018) 11:32 DOI 10.1186/s13071-017-2584-8 RESEARCH Open Access ASGDB: a specialised genomic resource for interpreting Anopheles sinensis insecticide resistance Dan Zhou, Yang Xu, Cheng Zhang, Meng-Xue Hu, Yun Huang, Yan Sun, Lei Ma, Bo Shen* and Chang-Liang Zhu Abstract Background: Anopheles sinensis is an important malaria vector in Southeast Asia. The widespread emergence of insecticide resistance in this mosquito species poses a serious threat to the efficacy of malaria control measures, particularly in China. Recently, the whole-genome sequencing and de novo assembly of An. sinensis (China strain) has been finished. A series of insecticide-resistant studies in An. sinensis have also been reported. There is a growing need to integrate these valuable data to provide a comprehensive database for further studies on insecticide-resistant management of An. sinensis. Results: A bioinformatics database named An. sinensis genome database (ASGDB) was built. In addition to being a searchable database of published An. sinensis genome sequences and annotation, ASGDB provides in-depth analytical platforms for further understanding of the genomic and genetic data, including visualization of genomic data, orthologous relationship analysis, GO analysis, pathway analysis, expression analysis and resistance-related gene analysis. Moreover, ASGDB provides a panoramic view of insecticide resistance studies in An. sinensis in China. In total, 551 insecticide-resistant phenotypic and genotypic reports on An. sinensis distributed in Chinese malaria- endemic areas since the mid-1980s have been collected, manually edited in the same format and integrated into OpenLayers map-based interface, which allows the international community to assess and exploit the high volume of scattered data much easier. -
Annual Scientific Report 2013 on the Cover Structure 3Fof in the Protein Data Bank, Determined by Laponogov, I
EMBL-European Bioinformatics Institute Annual Scientific Report 2013 On the cover Structure 3fof in the Protein Data Bank, determined by Laponogov, I. et al. (2009) Structural insight into the quinolone-DNA cleavage complex of type IIA topoisomerases. Nature Structural & Molecular Biology 16, 667-669. © 2014 European Molecular Biology Laboratory This publication was produced by the External Relations team at the European Bioinformatics Institute (EMBL-EBI) A digital version of the brochure can be found at www.ebi.ac.uk/about/brochures For more information about EMBL-EBI please contact: [email protected] Contents Introduction & overview 3 Services 8 Genes, genomes and variation 8 Molecular atlas 12 Proteins and protein families 14 Molecular and cellular structures 18 Chemical biology 20 Molecular systems 22 Cross-domain tools and resources 24 Research 26 Support 32 ELIXIR 36 Facts and figures 38 Funding & resource allocation 38 Growth of core resources 40 Collaborations 42 Our staff in 2013 44 Scientific advisory committees 46 Major database collaborations 50 Publications 52 Organisation of EMBL-EBI leadership 61 2013 EMBL-EBI Annual Scientific Report 1 Foreword Welcome to EMBL-EBI’s 2013 Annual Scientific Report. Here we look back on our major achievements during the year, reflecting on the delivery of our world-class services, research, training, industry collaboration and European coordination of life-science data. The past year has been one full of exciting changes, both scientifically and organisationally. We unveiled a new website that helps users explore our resources more seamlessly, saw the publication of ground-breaking work in data storage and synthetic biology, joined the global alliance for global health, built important new relationships with our partners in industry and celebrated the launch of ELIXIR. -
The Biogrid Interaction Database
D470–D478 Nucleic Acids Research, 2015, Vol. 43, Database issue Published online 26 November 2014 doi: 10.1093/nar/gku1204 The BioGRID interaction database: 2015 update Andrew Chatr-aryamontri1, Bobby-Joe Breitkreutz2, Rose Oughtred3, Lorrie Boucher2, Sven Heinicke3, Daici Chen1, Chris Stark2, Ashton Breitkreutz2, Nadine Kolas2, Lara O’Donnell2, Teresa Reguly2, Julie Nixon4, Lindsay Ramage4, Andrew Winter4, Adnane Sellam5, Christie Chang3, Jodi Hirschman3, Chandra Theesfeld3, Jennifer Rust3, Michael S. Livstone3, Kara Dolinski3 and Mike Tyers1,2,4,* 1Institute for Research in Immunology and Cancer, Universite´ de Montreal,´ Montreal,´ Quebec H3C 3J7, Canada, 2The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada, 3Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA, 4School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK and 5Centre Hospitalier de l’UniversiteLaval´ (CHUL), Quebec,´ Quebec´ G1V 4G2, Canada Received September 26, 2014; Revised November 4, 2014; Accepted November 5, 2014 ABSTRACT semi-automated text-mining approaches, and to en- hance curation quality control. The Biological General Repository for Interaction Datasets (BioGRID: http://thebiogrid.org) is an open access database that houses genetic and protein in- INTRODUCTION teractions curated from the primary biomedical lit- Massive increases in high-throughput DNA sequencing erature for all major model organism species and technologies (1) have enabled an unprecedented level of humans. As of September 2014, the BioGRID con- genome annotation for many hundreds of species (2–6), tains 749 912 interactions as drawn from 43 149 pub- which has led to tremendous progress in the understand- lications that represent 30 model organisms. -
An Integrated Mosquito Small RNA Genomics Resource Reveals
bioRxiv preprint doi: https://doi.org/10.1101/2020.04.25.061598; this version posted April 27, 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. 1 2 3 An integrated mosquito small RNA genomics resource reveals 4 dynamic evolution and host responses to viruses and transposons. 5 6 7 Qicheng Ma1† Satyam P. Srivastav1†, Stephanie Gamez2†, Fabiana Feitosa-Suntheimer3, 8 Edward I. Patterson4, Rebecca M. Johnson5, Erik R. Matson1, Alexander S. Gold3, Douglas E. 9 Brackney6, John H. Connor3, Tonya M. Colpitts3, Grant L. Hughes4, Jason L. Rasgon5, Tony 10 Nolan4, Omar S. Akbari2, and Nelson C. Lau1,7* 11 1. Boston University School of Medicine, Department of Biochemistry 12 2. University of California San Diego, Division of Biological Sciences, Section of Cell and 13 Developmental Biology, La Jolla, CA 92093-0335, USA. 14 3. Boston University School of Medicine, Department of Microbiology and the National 15 Emerging Infectious Disease Laboratory 16 4. Departments of Vector Biology and Tropical Disease Biology, Centre for Neglected Tropical 17 Diseases, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK 18 5. Pennsylvania State University, Department of Entomology, Center for Infectious Disease 19 Dynamics, and the Huck Institutes for the Life Sciences 20 6. Department of Environmental Sciences, The Connecticut Agricultural Experiment Station 21 7. Boston University Genome Science Institute 22 23 * Corresponding author: NCL: [email protected] 24 † These authors contributed equally to this study. 25 26 27 28 Running title: Mosquito small RNA genomics 29 30 Keywords: mosquitoes, small RNAs, piRNAs, viruses, transposons microRNAs, siRNAs 31 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.04.25.061598; this version posted April 27, 2020. -
The Interpro Database, an Integrated Documentation Resource for Protein
The InterPro database, an integrated documentation resource for protein families, domains and functional sites R Apweiler, T K Attwood, A Bairoch, A Bateman, E Birney, M Biswas, P Bucher, L Cerutti, F Corpet, M D Croning, et al. To cite this version: R Apweiler, T K Attwood, A Bairoch, A Bateman, E Birney, et al.. The InterPro database, an integrated documentation resource for protein families, domains and functional sites. Nucleic Acids Research, Oxford University Press, 2001, 29 (1), pp.37-40. 10.1093/nar/29.1.37. hal-01213150 HAL Id: hal-01213150 https://hal.archives-ouvertes.fr/hal-01213150 Submitted on 7 Oct 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. © 2001 Oxford University Press Nucleic Acids Research, 2001, Vol. 29, No. 1 37–40 The InterPro database, an integrated documentation resource for protein families, domains and functional sites R. Apweiler1,*, T. K. Attwood2,A.Bairoch3, A. Bateman4,E.Birney1, M. Biswas1, P. Bucher5, L. Cerutti4,F.Corpet6, M. D. R. Croning1,2, R. Durbin4,L.Falquet5,W.Fleischmann1, J. Gouzy6,H.Hermjakob1,N.Hulo3, I. Jonassen7,D.Kahn6,A.Kanapin1, Y. Karavidopoulou1, R. -
Supplementary Material
SUPPLEMENTARY MATERIAL Transcriptomics supports local sensory regulation in the antennae of the kissing bug Rhodnius prolixus Jose Manuel Latorre-Estivalis; Marcos Sterkel; Sheila Ons; and Marcelo Gustavo Lorenzo DATABASES Database S1 – Protein sequences of all target genes in fasta format. Database S2 – Edited Generic Feature Format (GFF) file of the R. prolixus genome used for read mapping and gene expression analysis. Database S3 - FPKM values of target genes in the three libraries. Database S4 – Fasta sequences from different insects used in the CT/DH – CRF/DH and nuclear receptor phylogenetic analyses. FIGURES Figure S1 - Molecular phylogenetic analyses of calcitonin diuretic (CT) and corticotropin-releasing factor- related (CRF) like diuretic hormone (DH) receptors of R. prolixus and other insects. The evolutionary history of R. prolixus CT/DH and CRF/DH receptors was inferred by using the Maximum Likelihood method in PhyML v3.0. The support values on the bipartitions correspond to SH-like P values, which were calculated by means of aLRT SH-like test. The CT/DH receptor 3 clade was highlighted in red. The CT/DH and CRF/DH R. prolixus receptors were displayed in blue. The LG substitution amino-acid model was used. Species abbreviations: Dmel, Drosophila melanogaster; Aaeg, Aedes aegytpi; Agam, Anopheles gambiae; Clec, Cimex lecturiaus; Hhal, Halomorpha halys; Rpro, Rhodnius prolixus; Amel, Apis mellifera, Acyrthosiphon pisum; and Tcas, Tribolium castaneum. The glutamate receptor sequence from the D. melanogaster (FlyBase Acc. N° GC11144) was used as an out-group. The sequences used from other insects are in Supplementary Database S4). Figure S2 – Molecular phylogenetic analysis of nuclear receptor genes of R. -
Biocuration 2016 - Posters
Biocuration 2016 - Posters Source: http://www.sib.swiss/events/biocuration2016/posters 1 RAM: A standards-based database for extracting and analyzing disease-specified concepts from the multitude of biomedical resources Jinmeng Jia and Tieliu Shi Each year, millions of people around world suffer from the consequence of the misdiagnosis and ineffective treatment of various disease, especially those intractable diseases and rare diseases. Integration of various data related to human diseases help us not only for identifying drug targets, connecting genetic variations of phenotypes and understanding molecular pathways relevant to novel treatment, but also for coupling clinical care and biomedical researches. To this end, we built the Rare disease Annotation & Medicine (RAM) standards-based database which can provide reference to map and extract disease-specified information from multitude of biomedical resources such as free text articles in MEDLINE and Electronic Medical Records (EMRs). RAM integrates disease-specified concepts from ICD-9, ICD-10, SNOMED-CT and MeSH (http://www.nlm.nih.gov/mesh/MBrowser.html) extracted from the Unified Medical Language System (UMLS) based on the UMLS Concept Unique Identifiers for each Disease Term. We also integrated phenotypes from OMIM for each disease term, which link underlying mechanisms and clinical observation. Moreover, we used disease-manifestation (D-M) pairs from existing biomedical ontologies as prior knowledge to automatically recognize D-M-specific syntactic patterns from full text articles in MEDLINE. Considering that most of the record-based disease information in public databases are textual format, we extracted disease terms and their related biomedical descriptive phrases from Online Mendelian Inheritance in Man (OMIM), National Organization for Rare Disorders (NORD) and Orphanet using UMLS Thesaurus. -
Multiple Sequence Alignment
ELB18S Entry Level Bioinformatics 05-09 November 2018 (Second 2018 run of this Course) Basic Bioinformatics Sessions Practical 6: Multiple Sequence Alignment Sunday 4 November 2018 Practical 6: Multiple Sequence Alignment Sunday 4 November 2018 Multiple Sequence Alignment Here we will look at some software tools to align some protein sequences. Before we can do that, we need some sequences to align. I propose we try all the human homeobox domains from the well annotated section of UniprotKB. Getting the sequences is a trifle clumsy, so concentrate now! There used to be a much easier way, but that was made redundant by foolish people intent on making the future ever more tricky!! So, begin by going to the home of Uniprot: http://www.uniprot.org/ Choose the option of the button. First specify that you are only interested in Human proteins. To do this, set the first field to Organism [OS] and Term to Human [9606]. Set the second field selector to Reviewed and the corresponding Term to Reviewed (that is, only SwissProt entries). If required, Click on the button to request a further field selection option. Set the new field to Function. Set the type of Function to DNA binding. Set the Term selection to Homeobox. From previous investigations, you should be aware that a Homeobox domain is generally 60 amino acids in length. To avoid partial and/or really weird Homeobox proteins, set the Length range settings to recognise only homeoboxs between 50 and 70 amino acids long. Leave the Evidence box as Any assertion method, one does not wish to be too fussy! Address the button with authority to get the search going.