A Converter Facilitating Genome Annotation Submission to European Nucleotide Archive Martin Norling1,2, Niclas Jareborg1,3 and Jacques Dainat1,2*

Total Page:16

File Type:pdf, Size:1020Kb

A Converter Facilitating Genome Annotation Submission to European Nucleotide Archive Martin Norling1,2, Niclas Jareborg1,3 and Jacques Dainat1,2* Norling et al. BMC Res Notes (2018) 11:584 https://doi.org/10.1186/s13104-018-3686-x BMC Research Notes RESEARCH NOTE Open Access EMBLmyGFF3: a converter facilitating genome annotation submission to European Nucleotide Archive Martin Norling1,2, Niclas Jareborg1,3 and Jacques Dainat1,2* Abstract Objective: The state-of-the-art genome annotation tools output GFF3 format fles, while this format is not accepted as submission format by the International Nucleotide Sequence Database Collaboration (INSDC) databases. Convert- ing the GFF3 format to a format accepted by one of the three INSDC databases is a key step in the achievement of genome annotation projects. However, the fexibility existing in the GFF3 format makes this conversion task difcult to perform. Until now, no converter is able to handle any GFF3 favour regardless of source. Results: Here we present EMBLmyGFF3, a robust universal converter from GFF3 format to EMBL format compatible with genome annotation submission to the European Nucleotide Archive. The tool uses json parameter fles, which can be easily tuned by the user, allowing the mapping of corresponding vocabulary between the GFF3 format and the EMBL format. We demonstrate the conversion of GFF3 annotation fles from four diferent commonly used anno- tation tools: Maker, Prokka, Augustus and Eugene. EMBLmyGFF3 is freely available at https​://githu​b.com/NBISw​eden/EMBLm​yGFF3​. Keywords: Annotation, Converter, Submission, EMBL, GFF3 Introduction the GFF3 format has become the de facto reference for- Over the last 20 years, many sequence annotation tools mat for annotations. Despite well-defned specifcations, have been developed, facilitating the production of rela- the format allows great fexibility allowing holding wide tively accurate annotation of a wide range of organisms variety of information. Tis fexibility has resulted in the in all kingdoms of the tree of life. To describe the features format being used by a broad range of annotation tools annotated within the genomes, the Generic Feature For- (e.g. MAKER [2], Augustus [3], Prokka [4], Eugene [5]), mat (GFF) was developed. Facing some limitation using and is used by most genome browsers (e.g. ARTEMIS the originally published Sanger specifcation, GFF has [6], Webapollo [7], IGV [8]). Te fexibility of the GFF3 evolved into diferent favours depending on the difer- format has facilitated its spread but raises a recurrent ent needs of diferent laboratories. Te Sequence Ontol- problem of interoperability with the diferent tools that ogy Project (http://www.seque​nceon​tolog​y.org; [1]) in use it. Indeed, one can meet as many favours of GFF3 2013 proposed the GFF3 format, which “addresses the format as tools producing it. One of the natural out- most common extensions to GFF, while preserving back- comes of a GFF3 fle is to be converted in a format that ward compatibility with previous formats”. Since then, can be submitted in one of the INSDC databases. Since 2016 NCBI released a beta version of a process to submit GFF3 or GTF to GenBank [9]. Tey describe what infor- *Correspondence: [email protected] mation is expected in the GFF3 fle and how to format it 1 National Bioinformatics Infrastructure Sweden (NBIS), SciLifeLab, in order to be accepted by the table2asn_GFF tool, which Uppsala Biomedicinska Centrum (BMC), Husargatan 3, 751 23 Uppsala, Sweden convert the well-formatted GFF3 into.sqn fles for sub- Full list of author information is available at the end of the article mission to GenBank. Modifying a GFF3 fle to fulfl the © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat​iveco​mmons​.org/licen​ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat​iveco​mmons​.org/ publi​cdoma​in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Norling et al. BMC Res Notes (2018) 11:584 Page 2 of 5 requirements is not often an easy task and programming called EMBLmyGFF3, which is a universal GFF3 to skills may be needed to automate it. To facilitate this step, EMBL converter allowing the submission to the ENA a user-friendly bioinformatics tool called Genome Anno- database. To our knowledge, this is the only tool able to tation Generator (GAG) has been implemented [10]. deal with any favour of GFF3 fle without any pre-pro- GAG provides a straightforward and consistent tool for cessing of the fle. Indeed, the originality lies in json map- producing a submission-ready NCBI annotation in.tbl ping fles allowing the mapping of vocabulary between format. Tis.tbl format is a tabulate format required with GFF3 and EMBL formats. two other fles (.sbt and.fsa) by the tbl2asn tool provided by the NCBI in order to produce a.sqn fle for submission Main text to GenBank. Implementation While the NCBI doesn’t accept their GenBank Flat File Te EMBLmyGFF3 tool is an implementation in the format but rather an.sqn intermediate fle for submission, python programming language of the verbose documen- EBI accepts submission in their EMBL fat fle format. tation provided by the European Bioinformatics Institute Here the difculty is to generate an EMBL fat fle from [13]. Te implementation is structured around two main a GFF3 fle. Several tools have been developed to per- modules: feature and qualifer. form this step i.e. Artemis [6], seqret from EMBOSS [11], (i) Te feature module contains the description of all GFF3toEMBL [12] but have limitations. While pletho- the EMBL features and their associated qualifers. Te ric number of annotation tools exist, GFF3toEMBL [12] feature module handles a parameter fle in json for- only deals with the GFF3 produced by the prokaryote mat, called translation_gf_feature_to_embl_feature. annotation tool Prokka. So, for annotation produced by json, allowing the proper mapping of the feature types other tools, users have to turn to other solutions. Arte- described in the 3rd column of the GFF3 fle to the cho- mis has a graphical user interface, which doesn’t allow sen EMBL features. an automation of the process. Seqret is designed to deal Below is an example how to map the GFF3 feature type with only one record at a time, which makes its use for “three_prime_UTR” to the EMBL feature type “3′UTR”: genome-wide annotation not straightforward. Te main bottleneck is that both tools need a properly formatted "three_prime_UTR": { GFF3 containing the INSDC expected vocabulary (3th and 9th column), while the annotation tools do not nec- "target": "3'UTR" essarily use this vocabulary. Te EMBL format follows the INSDC defnitions and accepts 52 diferent feature } types, whereas the GFF3 mandates the use of a Sequence Ontology term or accession number (3rd column of the GFF3), which nevertheless constitutes 2278 terms in We also provide the possibility to decide which features version 2.5.3 of the Sequence Ontology. Moreover, the will be printed in the output using the “remove” param- EMBL format accepts 98 diferent qualifers, where the eter. In the following example the feature type “three_ corresponding attribute tag types in the 9th column of prime_UTR” will be ignored: the GFF3 are unlimited. Consequently, in many cases the user may have to pre-process the GFF3 to adapt it to the "three_prime_UTR": { expected vocabulary. Te information contained and the vocabulary used "target": "3'UTR", in a GFF3 fle can difer a lot depending of the annota- tion tool used. On top of that the vocabulary used by the GFF3 format and by the EMBL format can difer in "remove": true many points. Tose diferences make it difcult to cre- ate a universal GFF3 to EMBL converter that would avoid } pre-processing of the GFF3 annotation fle. Te chal- lenge undoubtedly lies in the implementation of a correct mapping between the feature types described in the 3rd (ii) Te qualifer module defnes all the EMBL qualifers column, as well as the diferent attribute’s tags of the 9th (a defnition, a comment, and the format of the expected column of the GFF3 fle and the corresponding EMBL value) and has methods to access and print them. Within features and qualifers. the GFF3 fle, the qualifers are the attribute’s tags of the In collaboration with the European Nucleotide Archive 9th column. It is common that an attribute’s tag doesn’t we have developed a tool addressing these difculties correspond to a EMBL qualifer name. To address this Norling et al. BMC Res Notes (2018) 11:584 Page 3 of 5 difculty, the module handles a parameter fle in json produce the annotation, as well as metadata required format, called translation_gf_attribute_to_embl_quali- by ENA. Tere are metadata that are not contained in fer.json, allowing proper mapping of the attribute’s tag GFF3 format, but that are mandatory or recommended described in the 9th column of the GFF3 fle to the cho- for producing a valid EMBL fat fle. When all the manda- sen EMBL qualifer. Below is an example how to map the tory metadata are flled, the tool will proceed to the con- “Dbxref” attribute’s tags from the GFF3 fle to the “db_ version; otherwise it will help the user to fll the needed xref” qualifer expected by the EMBL format. information. "Dbxref": { Results "source description": "A database cross reference.", Te software has been used to convert the GFF3 anno- tation fle produced by diferent annotation tools (e.g. "target": "db_xref", Prokka, Maker, Augustus, Eugene). Tree test cases are included into the source code distribution.
Recommended publications
  • Bioinformatics Study of Lectins: New Classification and Prediction In
    Bioinformatics study of lectins : new classification and prediction in genomes François Bonnardel To cite this version: François Bonnardel. Bioinformatics study of lectins : new classification and prediction in genomes. Structural Biology [q-bio.BM]. Université Grenoble Alpes [2020-..]; Université de Genève, 2021. En- glish. NNT : 2021GRALV010. tel-03331649 HAL Id: tel-03331649 https://tel.archives-ouvertes.fr/tel-03331649 Submitted on 2 Sep 2021 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. THÈSE Pour obtenir le grade de DOCTEUR DE L’UNIVERSITE GRENOBLE ALPES préparée dans le cadre d’une cotutelle entre la Communauté Université Grenoble Alpes et l’Université de Genève Spécialités: Chimie Biologie Arrêté ministériel : le 6 janvier 2005 – 25 mai 2016 Présentée par François Bonnardel Thèse dirigée par la Dr. Anne Imberty codirigée par la Dr/Prof. Frédérique Lisacek préparée au sein du laboratoire CERMAV, CNRS et du Computer Science Department, UNIGE et de l’équipe PIG, SIB Dans les Écoles Doctorales EDCSV et UNIGE Etude bioinformatique des lectines: nouvelle classification et prédiction dans les génomes Thèse soutenue publiquement le 8 Février 2021, devant le jury composé de : Dr. Alexandre de Brevern UMR S1134, Inserm, Université Paris Diderot, Paris, France, Rapporteur Dr.
    [Show full text]
  • Trichoderma Reesei Complete Genome Sequence, Repeat-Induced Point
    Li et al. Biotechnol Biofuels (2017) 10:170 DOI 10.1186/s13068-017-0825-x Biotechnology for Biofuels RESEARCH Open Access Trichoderma reesei complete genome sequence, repeat‑induced point mutation, and partitioning of CAZyme gene clusters Wan‑Chen Li1,2,3†, Chien‑Hao Huang3,4†, Chia‑Ling Chen3, Yu‑Chien Chuang3, Shu‑Yun Tung3 and Ting‑Fang Wang1,3* Abstract Background: Trichoderma reesei (Ascomycota, Pezizomycotina) QM6a is a model fungus for a broad spectrum of physiological phenomena, including plant cell wall degradation, industrial production of enzymes, light responses, conidiation, sexual development, polyketide biosynthesis, and plant–fungal interactions. The genomes of QM6a and its high enzyme-producing mutants have been sequenced by second-generation-sequencing methods and are pub‑ licly available from the Joint Genome Institute. While these genome sequences have ofered useful information for genomic and transcriptomic studies, their limitations and especially their short read lengths make them poorly suited for some particular biological problems, including assembly, genome-wide determination of chromosome architec‑ ture, and genetic modifcation or engineering. Results: We integrated Pacifc Biosciences and Illumina sequencing platforms for the highest-quality genome assem‑ bly yet achieved, revealing seven telomere-to-telomere chromosomes (34,922,528 bp; 10877 genes) with 1630 newly predicted genes and >1.5 Mb of new sequences. Most new sequences are located on AT-rich blocks, including 7 centromeres, 14 subtelomeres, and 2329 interspersed AT-rich blocks. The seven QM6a centromeres separately consist of 24 conserved repeats and 37 putative centromere-encoded genes. These fndings open up a new perspective for future centromere and chromosome architecture studies.
    [Show full text]
  • Introduction to Bioinformatics (Elective) – SBB1609
    SCHOOL OF BIO AND CHEMICAL ENGINEERING DEPARTMENT OF BIOTECHNOLOGY Unit 1 – Introduction to Bioinformatics (Elective) – SBB1609 1 I HISTORY OF BIOINFORMATICS Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biologicaldata. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine. Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data. Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. "Classical" bioinformatics: "The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information.” The National Center for Biotechnology Information (NCBI 2001) defines bioinformatics as: "Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline.
    [Show full text]
  • A Comprehensive Review and Performance Evaluation of Sequence Alignment Algorithms for DNA Sequences
    International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 11251 - 11265 A Comprehensive Review and Performance Evaluation of Sequence Alignment Algorithms for DNA Sequences Neelofar Sohi1 and Amardeep Singh2 1Assistant Professor, Department of Computer Science & Engineering, Punjabi University Patiala, Punjab, India 2Professor, Department of Computer Science & Engineering, Punjabi University Patiala, Punjab, India [email protected],[email protected] Abstract Background: Sequence alignment is very important step for high level sequence analysis applications in the area of biocomputing. Alignment of DNA sequences helps in finding origin of sequences, homology between sequences, constructing phylogenetic trees depicting evolutionary relationships and other tasks. It helps in identifying genetic variations in DNA sequences which might lead to diseases. Objectives: This paper presents a comprehensive review on sequence alignment approaches, methods and various state-of-the-art algorithms. Performance evaluation and comparison of few algorithms and tools is performed. Methods and Results: In this study, various tools and algorithms are studied, implemented and their performance is evaluated and compared using Identity Percentage is used as the main metric. Conclusion: It is observed that for pairwise sequence alignment, Clustal Omega Emboss Matcher outperforms other tools & algorithms followed by Clustal Omega Emboss Water further followed by Blast (Needleman-Wunsch algorithm for global alignment). Keywords: sequence alignment; DNA sequences; progressive alignment; iterative alignment; natural computing approach; identity percentage. 1. Brief History Sequence alignment is very important area in the field of Biocomputing and Bioinformatics. Sequence alignment aims to identify the regions of similarity between two or more sequences. Alignment is also termed as ‘mapping’ that is done to identify and compare the nucleotide bases i.e.
    [Show full text]
  • GPS@: Bioinformatics Grid Portal for Protein Sequence Analysis on EGEE Grid
    Enabling Grids for E-sciencE GPS@: Bioinformatics grid portal for protein sequence analysis on EGEE grid Blanchet, C., Combet, C., Lefort, V. and Deleage, G. Pôle BioInformatique de Lyon – PBIL Institut de Biologie et Chimie des Protéines IBCP – CNRS UMR 5086 Lyon-Gerland, France [email protected] www.eu-egee.org INFSO-RI-508833 TOC Enabling Grids for E-sciencE • NPS@ Web portal – Online since 1998 – Production mode • Gridification of NPS@ • Bioinformatics description with XML-based Framework • Legacy mode for application file access • GPS@ Web portal for Bioinformatics on Grid INFSO-RI-508833 GPS@ Bioinformatics Grid Portal, EGEE User Forum, March 1st, 2006, Cern 2 Institute of Biology and Enabling Grids for E-sciencE Chemistry of Proteins • French CNRS Institute, associated to Univ. Lyon1 – Life Science – About 160 people – http://www.ibcp.fr – Located in Lyon, France • Study of proteins in their biological context ♣ Approaches used include integrative cellular (cell culture, various types of microscopies) and molecular techniques, both experimental (including biocrystallography and nuclear magnetic resonance) and theoretical (structural bioinformatics). • Three main departments, bringing together 13 groups ♣ topics such as cancer, extracellular matrix, tissue engineering, membranes, cell transport and signalling, bioinformatics and structural biology INFSO-RI-508833 GPS@ Bioinformatics Grid Portal, EGEE User Forum, March 1st, 2006, Cern 3 EGEE Biomedical Activity Enabling Grids for E-sciencE • Chair: ♣ Johan Montagnat ♣ Christophe
    [Show full text]
  • Software List for Biology, Bioinformatics and Biostatistics CCT
    Software List for biology, bioinformatics and biostatistics v CCT - Delta Software Version Application short read assembler and it works on both small and large (mammalian size) ALLPATHS-LG 52488 genomes provides a fast, flexible C++ API & toolkit for reading, writing, and manipulating BAMtools 2.4.0 BAM files a high level of alignment fidelity and is comparable to other mainstream Barracuda 0.7.107b alignment programs allows one to intersect, merge, count, complement, and shuffle genomic bedtools 2.25.0 intervals from multiple files Bfast 0.7.0a universal DNA sequence aligner tool analysis and comprehension of high-throughput genomic data using the R Bioconductor 3.2 statistical programming BioPython 1.66 tools for biological computation written in Python a fast approach to detecting gene-gene interactions in genome-wide case- Boost 1.54.0 control studies short read aligner geared toward quickly aligning large sets of short DNA Bowtie 1.1.2 sequences to large genomes Bowtie2 2.2.6 Bowtie + fully supports gapped alignment with affine gap penalties BWA 0.7.12 mapping low-divergent sequences against a large reference genome ClustalW 2.1 multiple sequence alignment program to align DNA and protein sequences assembles transcripts, estimates their abundances for differential expression Cufflinks 2.2.1 and regulation in RNA-Seq samples EBSEQ (R) 1.10.0 identifying genes and isoforms differentially expressed EMBOSS 6.5.7 a comprehensive set of sequence analysis programs FASTA 36.3.8b a DNA and protein sequence alignment software package FastQC
    [Show full text]
  • A New Graphical User Interface to EMBOSS
    Comparative and Functional Genomics Comp Funct Genom 2002; 3: 75–78. DOI: 10.1002/cfg.136 Feature Website Update: A new graphical user interface to EMBOSS Tim J. Carver* and Lisa J. Mullan MRC UK HGMP Resource Centre, Genome Campus, Hinxton, Cambridge, Cambridgeshire, CB10 1SB, UK *Correspondence to: MRC UK HGMP Resource Centre, Genome Campus, Hinxton, Cambridge, Keywords: EMBOSS; Bioinformatics; HGMP; graphical interface; Java Cambridgeshire, CB10 1SB, UK. E-mail: [email protected] Received: 1 December 2001 Accepted: 4 December 2001 Published online: 17 January 2002 EMBOSS, the European Molecular Biology Open A server for the new interface, accessible to Software Suite is a collection of over 150 bioinfor- registered HGMP users, has been set up at the matics programs. It provides individual applications EMBOSS web site [5]. It is also linked from the for retrieval, editing, and analysis of nucleotide and ‘Bioinformatics Applications’ menu on the HGMP peptide sequences. It also includes software for site. Jemboss will function in client-server mode analysing protein structure. Distributed under the allowing many users to run their analyses remotely. GNU General Public License software agreements The Jemboss interface window (the client) is [6], all programs are available, free of charge, to all launched on the users’ workstations. Analyses are academic or commercial users. EMBOSS brings then run on the HGMP machines and the results together programs from various sources including returned to the local screen. This has been achieved the EMBOSS development team, individuals and using the Simple Object Access Protocol (SOAP) members of the EMBnet community. [1], a new advance in Internet Communication To mark the fifth anniversary of EMBOSS, an Technology designed specifically to circumvent fire- exciting new graphical user interface (GUI), ‘Jem- wall restrictions.
    [Show full text]
  • EBI, Expasy, EMBOSS, DTU)
    EBI web resources III: Web-based tools in Europe (EBI, ExPASy, EMBOSS, DTU) Yanbin Yin Fall 2015 1 Homework assignment 4 1. Download h<p://cys.bios.niu.edu/yyin/teach/PBB/purdue.cellwall.list.lignin.fa to your computer 2. Select a C3H protein and a F5H protein from the above file and calculate the sequence idenLty between them using the Water server at EBI. 3. Perform a mulLple sequence alignment using MAFFT with all FASTA sequences in the file 4. Built a phylogeny with the alignment using the "A la Carte" mode at hp://www.phylogeny.fr/ 5. Build another phylogeny starLng from the unaligned sequences using the “one-click” mode at hp://www.phylogeny.fr/; if you encounter any error reports, try to figure out why and how to solve it (hint: skip the Gblocks step). Write a report (in word or ppt) to include all the operaons, screen shots and the final phylogenies from step 3 and 4. Office hour: Due on 10/13 (send by email) Tue, Thu and Fri 2-4pm, MO325A Or email: [email protected] 2 Outline • Hands on exercises! 3 Pairwise alignment (including database search) tools Slower SSEARCH FASTA BLAST BWA Faster More matches PSI-BLAST BLAT Bowe Less matches PSI-Search HMMER3 RPS-BLAST 4 h<p://www.ebi.ac.uk/ To the bo<om of the page Click by names (A-Z) 5 We are gonna try FASTA tool This is a very long list of tools Scroll down to find FASTA Or Ctrl+F and type fasta Click on FASTA [nucleode] 6 Click Genomes We’re gonna search Arabidopsis genome 7 Click on this li<le arrow Choose Arabidopsis Change here to protein Go to h<p://cys.bios.niu.edu/yyin/teach/PBB/cesa-pr.fa
    [Show full text]
  • Web & Grid Technologies in Bioinformatics, Computational And
    Current Bioinformatics, 2008, 3, 000-000 1 Web & Grid Technologies in Bioinformatics, Computational and Systems Biology: A Review Azhar A. Shah1, Daniel Barthel1, Piotr Lukasiak2,3, Jacek Blazewicz2,3 and Natalio Krasnogor*,1 1School of Computer Science, University of Nottingham, Jubilee Campus, NG81BB, Nottingham, UK 2Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland 3Institute of Bioorganic Chemistry, Laboratory of Bioinformatics, Polish Academy of Sciences, Noskowskiego 12/14, 61- 704 Poznan, Poland Abstract: The acquisition of biological data, ranging from molecular characterization and simulations (e.g. protein fold- ing dynamics), to systems biology endeavors (e.g. whole organ simulations) all the way up to ecological observations (e.g. as to ascertain climate change’s impact on the biota) is growing at unprecedented speed. The use of computational and networking resources is thus unavoidable. As the datasets become bigger and the acquisition technology more refined, the biologist is empowered to ask deeper and more complex questions. These, in turn, drive a runoff effect where large re- search consortia emerge that span beyond organizations and national boundaries. Thus the need for reliable, robust, certi- fied, curated, accessible, secure and timely data processing and management becomes entrenched within, and crucial to, 21st century biology. Furthermore, the proliferation of biotechnologies and advances in biological sciences has produced a strong drive for new informatics solutions, both at the basic science and technological levels. The previously unknown situation of dealing with, on one hand, (potentially) exabytes of data, much of which is noisy, has large experimental er- rors or theoretical uncertainties associated with it, or on the other hand, large quantities of data that require automated computationally intense analysis and processing, have produced important innovations in web and grid technology.
    [Show full text]
  • Annual Scientific Report 2011 Annual Scientific Report 2011 Designed and Produced by Pickeringhutchins Ltd
    European Bioinformatics Institute EMBL-EBI Annual Scientific Report 2011 Annual Scientific Report 2011 Designed and Produced by PickeringHutchins Ltd www.pickeringhutchins.com EMBL member states: Austria, Croatia, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom. Associate member state: Australia EMBL-EBI is a part of the European Molecular Biology Laboratory (EMBL) EMBL-EBI EMBL-EBI EMBL-EBI EMBL-European Bioinformatics Institute Wellcome Trust Genome Campus, Hinxton Cambridge CB10 1SD United Kingdom Tel. +44 (0)1223 494 444, Fax +44 (0)1223 494 468 www.ebi.ac.uk EMBL Heidelberg Meyerhofstraße 1 69117 Heidelberg Germany Tel. +49 (0)6221 3870, Fax +49 (0)6221 387 8306 www.embl.org [email protected] EMBL Grenoble 6, rue Jules Horowitz, BP181 38042 Grenoble, Cedex 9 France Tel. +33 (0)476 20 7269, Fax +33 (0)476 20 2199 EMBL Hamburg c/o DESY Notkestraße 85 22603 Hamburg Germany Tel. +49 (0)4089 902 110, Fax +49 (0)4089 902 149 EMBL Monterotondo Adriano Buzzati-Traverso Campus Via Ramarini, 32 00015 Monterotondo (Rome) Italy Tel. +39 (0)6900 91402, Fax +39 (0)6900 91406 © 2012 EMBL-European Bioinformatics Institute All texts written by EBI-EMBL Group and Team Leaders. This publication was produced by the EBI’s Outreach and Training Programme. Contents Introduction Foreword 2 Major Achievements 2011 4 Services Rolf Apweiler and Ewan Birney: Protein and nucleotide data 10 Guy Cochrane: The European Nucleotide Archive 14 Paul Flicek:
    [Show full text]
  • Bioinformatics Approaches for Functional Predictions in Diverse Informatics Environments. Paula Maria Moolhuijzen, Bsc This Thes
    Bioinformatics approaches for functional predictions in diverse informatics environments. Paula Maria Moolhuijzen, BSc This thesis is presented for the degree of Doctor of Philosophy of Murdoch University 2011 i Declaration I declare that this thesis is my own account of my research and contains as its main content work, which has not previously been submitted for a degree at any tertiary education institution. Signature: Name: Paula Moolhuijzen Date: 4th October 2011 ii Abstract Bioinformatics is the scientific discipline that collates, integrates and analyses data and information sets for the life sciences. Critically important in agricultural and biomedical fields, there is a pressing need to integrate large and diverse data sets into biologically significant information. This places major challenges on research strategies and resources (data repositories, computer infrastructure and software) required to integrate relevant data and analysis workflows. These challenges include: ! The construction of processes to integrate data from disparate and diverse resources and legacy systems that have variable data formats, qualities, availability and accessibility constraints. ! Substantially contributing to hypothesis driven research for biologically significant information. The hypothesis proposed in this thesis is that in organisms from divergent origins, with differing data availability and analysis resources, in silico approaches can identify genomic targets in a range of disease systems. The particular aims were to: 1. Overcome data constraints that impact analysis of different organisms. 2. Make functional genomic predictions in diverse biological systems. 3. Identify specific genomic targets for diagnostics and therapeutics in diverse disease mechanisms. iii In order to test the hypothesis three case studies in human cancer, pathogenic bacteria, and parasitic arthropod were selected, the results are as follows.
    [Show full text]
  • Genetic Variation Regulates the Activation and Specificity of Restriction-Modification Systems in Neisseria Gonorrhoeae
    bioRxiv preprint doi: https://doi.org/10.1101/630434; this version posted May 7, 2019. 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. Genetic variation regulates the activation and specificity of Restriction-Modification systems in Neisseria gonorrhoeae Leonor Sánchez-Busó1,*, Daniel Golparian2, Julian Parkhill1, Magnus Unemo2 and Simon R. Harris1,3,* 1 Pathogen Genomics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom. 2 WHO Collaborating Centre for Gonorrhoea and other Sexually Transmitted Infections, National Reference Laboratory for Sexually Transmitted Infections, Department of Laboratory Medicine, Clinical Microbiology, Faculty of Medicine and Health, Örebro University, Örebro, SE-701 85, Sweden. 3 Microbiotica Ltd, Biodata Innovation Centre, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1DR, UK * To whom correspondence should be addressed. Leonor Sánchez-Busó. Tel: +44 1223 494761; Email: [email protected]. Correspondence may also be addressed to Simon R. Harris ([email protected]). ABSTRACT Restriction-Modification systems (RMS) are one of the main mechanisms of defence against foreign DNA invasion and can have an important role in the regulation of gene expression. The obligate human pathogen Neisseria gonorrhoeae carries one of the highest loads of RMS in its genome; between 13 to 15 of the three main types. Previous work has described their organization in the reference genome FA1090 and has experimentally inferred the associated methylated motifs. Here, we studied the structure of RMS and target methylated motifs in 25 gonococcal strains sequenced with Single Molecule Real-Time (SMRT) technology, which provides data on DNA modification.
    [Show full text]