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Uniprot at EMBL-EBI's Role in CTTV
Barbara P. Palka, Daniel Gonzalez, Edd Turner, Xavier Watkins, Maria J. Martin, Claire O’Donovan European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK UniProt at EMBL-EBI’s role in CTTV: contributing to improved disease knowledge Introduction The mission of UniProt is to provide the scientific community with a The Centre for Therapeutic Target Validation (CTTV) comprehensive, high quality and freely accessible resource of launched in Dec 2015 a new web platform for life- protein sequence and functional information. science researchers that helps them identify The UniProt Knowledgebase (UniProtKB) is the central hub for the collection of therapeutic targets for new and repurposed medicines. functional information on proteins, with accurate, consistent and rich CTTV is a public-private initiative to generate evidence on the annotation. As much annotation information as possible is added to each validity of therapeutic targets based on genome-scale experiments UniProtKB record and this includes widely accepted biological ontologies, and analysis. CTTV is working to create an R&D framework that classifications and cross-references, and clear indications of the quality of applies to a wide range of human diseases, and is committed to annotation in the form of evidence attribution of experimental and sharing its data openly with the scientific community. CTTV brings computational data. together expertise from four complementary institutions: GSK, Biogen, EMBL-EBI and Wellcome Trust Sanger Institute. UniProt’s disease expert curation Q5VWK5 (IL23R_HUMAN) This section provides information on the disease(s) associated with genetic variations in a given protein. The information is extracted from the scientific literature and diseases that are also described in the OMIM database are represented with a controlled vocabulary. -
Sequencing Alignment I Outline: Sequence Alignment
Sequencing Alignment I Lectures 16 – Nov 21, 2011 CSE 527 Computational Biology, Fall 2011 Instructor: Su-In Lee TA: Christopher Miles Monday & Wednesday 12:00-1:20 Johnson Hall (JHN) 022 1 Outline: Sequence Alignment What Why (applications) Comparative genomics DNA sequencing A simple algorithm Complexity analysis A better algorithm: “Dynamic programming” 2 1 Sequence Alignment: What Definition An arrangement of two or several biological sequences (e.g. protein or DNA sequences) highlighting their similarity The sequences are padded with gaps (usually denoted by dashes) so that columns contain identical or similar characters from the sequences involved Example – pairwise alignment T A C T A A G T C C A A T 3 Sequence Alignment: What Definition An arrangement of two or several biological sequences (e.g. protein or DNA sequences) highlighting their similarity The sequences are padded with gaps (usually denoted by dashes) so that columns contain identical or similar characters from the sequences involved Example – pairwise alignment T A C T A A G | : | : | | : T C C – A A T 4 2 Sequence Alignment: Why The most basic sequence analysis task First aligning the sequences (or parts of them) and Then deciding whether that alignment is more likely to have occurred because the sequences are related, or just by chance Similar sequences often have similar origin or function New sequence always compared to existing sequences (e.g. using BLAST) 5 Sequence Alignment Example: gene HBB Product: hemoglobin Sickle-cell anaemia causing gene Protein sequence (146 aa) MVHLTPEEKS AVTALWGKVN VDEVGGEALG RLLVVYPWTQ RFFESFGDLS TPDAVMGNPK VKAHGKKVLG AFSDGLAHLD NLKGTFATLS ELHCDKLHVD PENFRLLGNV LVCVLAHHFG KEFTPPVQAA YQKVVAGVAN ALAHKYH BLAST (Basic Local Alignment Search Tool) The most popular alignment tool Try it! Pick any protein, e.g. -
Comparative Analysis of Multiple Sequence Alignment Tools
I.J. Information Technology and Computer Science, 2018, 8, 24-30 Published Online August 2018 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2018.08.04 Comparative Analysis of Multiple Sequence Alignment Tools Eman M. Mohamed Faculty of Computers and Information, Menoufia University, Egypt E-mail: [email protected]. Hamdy M. Mousa, Arabi E. keshk Faculty of Computers and Information, Menoufia University, Egypt E-mail: [email protected], [email protected]. Received: 24 April 2018; Accepted: 07 July 2018; Published: 08 August 2018 Abstract—The perfect alignment between three or more global alignment algorithm built-in dynamic sequences of Protein, RNA or DNA is a very difficult programming technique [1]. This algorithm maximizes task in bioinformatics. There are many techniques for the number of amino acid matches and minimizes the alignment multiple sequences. Many techniques number of required gaps to finds globally optimal maximize speed and do not concern with the accuracy of alignment. Local alignments are more useful for aligning the resulting alignment. Likewise, many techniques sub-regions of the sequences, whereas local alignment maximize accuracy and do not concern with the speed. maximizes sub-regions similarity alignment. One of the Reducing memory and execution time requirements and most known of Local alignment is Smith-Waterman increasing the accuracy of multiple sequence alignment algorithm [2]. on large-scale datasets are the vital goal of any technique. The paper introduces the comparative analysis of the Table 1. Pairwise vs. multiple sequence alignment most well-known programs (CLUSTAL-OMEGA, PSA MSA MAFFT, BROBCONS, KALIGN, RETALIGN, and Compare two biological Compare more than two MUSCLE). -
Chapter 6: Multiple Sequence Alignment Learning Objectives
Chapter 6: Multiple Sequence Alignment Learning objectives • Explain the three main stages by which ClustalW performs multiple sequence alignment (MSA); • Describe several alternative programs for MSA (such as MUSCLE, ProbCons, and TCoffee); • Explain how they work, and contrast them with ClustalW; • Explain the significance of performing benchmarking studies and describe several of their basic conclusions for MSA; • Explain the issues surrounding MSA of genomic regions Outline: multiple sequence alignment (MSA) Introduction; definition of MSA; typical uses Five main approaches to multiple sequence alignment Exact approaches Progressive sequence alignment Iterative approaches Consistency-based approaches Structure-based methods Benchmarking studies: approaches, findings, challenges Databases of Multiple Sequence Alignments Pfam: Protein Family Database of Profile HMMs SMART Conserved Domain Database Integrated multiple sequence alignment resources MSA database curation: manual versus automated Multiple sequence alignments of genomic regions UCSC, Galaxy, Ensembl, alignathon Perspective Multiple sequence alignment: definition • a collection of three or more protein (or nucleic acid) sequences that are partially or completely aligned • homologous residues are aligned in columns across the length of the sequences • residues are homologous in an evolutionary sense • residues are homologous in a structural sense Example: 5 alignments of 5 globins Let’s look at a multiple sequence alignment (MSA) of five globins proteins. We’ll use five prominent MSA programs: ClustalW, Praline, MUSCLE (used at HomoloGene), ProbCons, and TCoffee. Each program offers unique strengths. We’ll focus on a histidine (H) residue that has a critical role in binding oxygen in globins, and should be aligned. But often it’s not aligned, and all five programs give different answers. -
How to Generate a Publication-Quality Multiple Sequence Alignment (Thomas Weimbs, University of California Santa Barbara, 11/2012)
Tutorial: How to generate a publication-quality multiple sequence alignment (Thomas Weimbs, University of California Santa Barbara, 11/2012) 1) Get your sequences in FASTA format: • Go to the NCBI website; find your sequences and display them in FASTA format. Each sequence should look like this (http://www.ncbi.nlm.nih.gov/protein/6678177?report=fasta): >gi|6678177|ref|NP_033320.1| syntaxin-4 [Mus musculus] MRDRTHELRQGDNISDDEDEVRVALVVHSGAARLGSPDDEFFQKVQTIRQTMAKLESKVRELEKQQVTIL ATPLPEESMKQGLQNLREEIKQLGREVRAQLKAIEPQKEEADENYNSVNTRMKKTQHGVLSQQFVELINK CNSMQSEYREKNVERIRRQLKITNAGMVSDEELEQMLDSGQSEVFVSNILKDTQVTRQALNEISARHSEI QQLERSIRELHEIFTFLATEVEMQGEMINRIEKNILSSADYVERGQEHVKIALENQKKARKKKVMIAICV SVTVLILAVIIGITITVG 2) In a text editor, paste all your sequences together (in the order that you would like them to appear in the end). It should look like this: >gi|6678177|ref|NP_033320.1| syntaxin-4 [Mus musculus] MRDRTHELRQGDNISDDEDEVRVALVVHSGAARLGSPDDEFFQKVQTIRQTMAKLESKVRELEKQQVTIL ATPLPEESMKQGLQNLREEIKQLGREVRAQLKAIEPQKEEADENYNSVNTRMKKTQHGVLSQQFVELINK CNSMQSEYREKNVERIRRQLKITNAGMVSDEELEQMLDSGQSEVFVSNILKDTQVTRQALNEISARHSEI QQLERSIRELHEIFTFLATEVEMQGEMINRIEKNILSSADYVERGQEHVKIALENQKKARKKKVMIAICV SVTVLILAVIIGITITVG >gi|151554658|gb|AAI47965.1| STX3 protein [Bos taurus] MKDRLEQLKAKQLTQDDDTDEVEIAVDNTAFMDEFFSEIEETRVNIDKISEHVEEAKRLYSVILSAPIPE PKTKDDLEQLTTEIKKRANNVRNKLKSMERHIEEDEVQSSADLRIRKSQHSVLSRKFVEVMTKYNEAQVD FRERSKGRIQRQLEITGKKTTDEELEEMLESGNPAIFTSGIIDSQISKQALSEIEGRHKDIVRLESSIKE LHDMFMDIAMLVENQGEMLDNIELNVMHTVDHVEKAREETKRAVKYQGQARKKLVIIIVIVVVLLGILAL IIGLSVGLK -
To Find Information About Arabidopsis Genes Leonore Reiser1, Shabari
UNIT 1.11 Using The Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes Leonore Reiser1, Shabari Subramaniam1, Donghui Li1, and Eva Huala1 1Phoenix Bioinformatics, Redwood City, CA USA ABSTRACT The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, orthologs gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, genome organization, images of mutant plants, protein sub-cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of Web-based search and display tools. This unit primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and genome, Additionally we describe how members of the community can share data using TAIR’s Online Annotation Submission Tool (TOAST), in order to make their published research more accessible and visible. Keywords: Arabidopsis ● databases ● bioinformatics ● data mining ● genomics INTRODUCTION The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource for the biology of Arabidopsis thaliana (Huala et al., 2001; Garcia-Hernandez et al., 2002; Rhee et al., 2003; Weems et al., 2004; Swarbreck et al., 2008, Lamesch, et al., 2010, Berardini et al., 2016). The TAIR database contains information about genes, proteins, gene expression, mutant phenotypes, germplasms, clones, genetic markers, genetic and physical maps, genome organization, publications, and the research community. In addition, seed and DNA stocks from the Arabidopsis Biological Resource Center (ABRC; Scholl et al., 2003) are integrated with genomic data, and can be ordered through TAIR. -
PIR Brochure
Protein Information Resource Integrated Protein Informatics Resource for Genomic & Proteomic Research For four decades the Protein Information Resource (PIR) has provided databases and protein sequence analysis tools to the scientific community, including the Protein Sequence Database, which grew out from the Atlas of Protein Sequence and Structure, edited by Margaret Dayhoff [1965-1978]. Currently, PIR major activities include: i) UniProt (Universal Protein Resource) development, ii) iProClass protein data integration and ID mapping, iii) PIRSF protein pir.georgetown.edu classification, and iv) iProLINK protein literature mining and ontology development. UniProt – Universal Protein Resource What is UniProt? UniProt is the central resource for storing and UniProt (Universal Protein Resource) http://www.uniprot.org interconnecting information from large and = + + disparate sources and the most UniProt: the world's most comprehensive catalog of information on proteins comprehensive catalog of protein sequence and functional annotation. UniProt Knowledgebase UniProt Reference UniProt Archive (UniProtKB) Clusters (UniRef) (UniParc) When to use UniProt databases Integration of Swiss-Prot, TrEMBL Non-redundant reference A stable, and PIR-PSD sequences clustered from comprehensive Use UniProtKB to retrieve curated, reliable, Fully classified, richly and accurately UniProtKB and UniParc for archive of all publicly annotated protein sequences with comprehensive or fast available protein comprehensive information on proteins. minimal redundancy and extensive sequence searches at 100%, sequences for Use UniRef to decrease redundancy and cross-references 90%, or 50% identity sequence tracking from: speed up sequence similarity searches. TrEMBL section UniRef100 Swiss-Prot, Computer-annotated protein sequences TrEMBL, PIR-PSD, Use UniParc to access to archived sequences EMBL, Ensembl, IPI, and their source databases. -
Cdna Cloning, Expression and Homology Modeling of a Luciferase from the Firefly Lampyroidea Maculata
Journal of Biochemistry and Molecular Biology, Vol. 39, No. 5, September 2006, pp. 578-585 cDNA Cloning, Expression and Homology Modeling of a Luciferase from the Firefly Lampyroidea maculata Abdo Rahman Emamzadeh1, Saman Hosseinkhani1,*, Majid Sadeghizadeh2, Maryam Nikkhah3, Mohammad Javad Chaichi4 and Mojtaba Mortazavi1 1Department of Biochemistry, Faculty of Basic Sciences, Tarbiat Modarres University, Tehran, Iran 2Department of Genetics, Faculty of Basic Sciences, Tarbiat Modarres University, Tehran, Iran 3Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran 4Department of Chemistry, Mazandaran University, Babolsar, Iran Received 7 April 2006, Accepted 23 May 2006 The cDNA of a firefly luciferase from lantern mRNA of Introduction Lampyroidea maculata has been cloned, sequenced and functionally expressed. The cDNA has an open reading Firefly luciferase (EC 1.13.12.7) is a well-characterized enzyme frame of 1647 bp and codes for a 548-residue-long that is responsible for the bioluminescence reaction. It polypeptide. Noteworthy, sequence comparison as well as catalyzes the oxidation of firefly luciferin with molecular homology modeling showed the highest degree of similarity oxygen in the presence of ATP and Mg2+ to emit yellow-green with H. unmunsana and L. mingrelica luciferases, light (McElroy, 1969; White et al., 1971; DeLuca, 1976; suggesting a close phylogenetic relationship despite the Wood, 1995). The initial reaction catalyzed by firefly geographical distance separation. The deduced amino acid luciferase is the formation of luciferyl adenylate with the sequence of the luciferase gene of firefly L. maculata release of inorganic pyrophosphate. The luciferase-bound showed 93% identity to H. unmunsana. Superposition of luciferyl adenylate reacts rapidly with molecular oxygen to the three-dimensional model of L. -
Loss of BCL-3 Sensitises Colorectal Cancer Cells to DNA Damage, Revealing A
bioRxiv preprint doi: https://doi.org/10.1101/2021.08.03.454995; this version posted August 4, 2021. 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. Title: Loss of BCL-3 sensitises colorectal cancer cells to DNA damage, revealing a role for BCL-3 in double strand break repair by homologous recombination Authors: Christopher Parker*1, Adam C Chambers*1, Dustin Flanagan2, Tracey J Collard1, Greg Ngo3, Duncan M Baird3, Penny Timms1, Rhys G Morgan4, Owen Sansom2 and Ann C Williams1. *Joint first authors. Author affiliations: 1. Colorectal Tumour Biology Group, School of Cellular and Molecular Medicine, Faculty of Life Sciences, Biomedical Sciences Building, University Walk, University of Bristol, Bristol, BS8 1TD, UK 2. Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Bearsden Glasgow, G61 1BD UK 3. Division of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN UK 4. School of Life Sciences, University of Sussex, Sussex House, Falmer, Brighton, BN1 9RH UK 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.08.03.454995; this version posted August 4, 2021. 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. Abstract (250 words) Objective: The proto-oncogene BCL-3 is upregulated in a subset of colorectal cancers (CRC) and increased expression of the gene correlates with poor patient prognosis. The aim is to investigate whether inhibiting BCL-3 can increase the response to DNA damage in CRC. -
Update on Genome Completion and Annotations
UPDATE ON GENOME COMPLETION AND ANNOTATIONS Update on genome completion and annotations: Protein Information Resource Cathy Wu1and Daniel W. Nebert2* 1Director of PIR, Department of Biochemistry and Molecular Biology, Georgetown University Medical Center, Washington, DC, USA 2Department of Environmental Health and Center for Environmental Genetics (CEG), University of Cincinnati Medical Center, Cincinnati, OH 45267–0056, USA *Correspondence to: Tel: þ1 513 558 4347; Fax: þ1 513 558 3562; E-mail: [email protected] Date received (in revised form): 11th January 2004 Abstract The Protein Information Resource (PIR) recently joined the European Bioinformatics Institute (EBI) and Swiss Institute of Bioinformatics (SIB) to establish UniProt — the Universal Protein Resource — which now unifies the PIR, Swiss-Prot and TrEMBL databases. The PIRSF (SuperFamily) classification system is central to the PIR/UniProt functional annotation of proteins, providing classifications of whole proteins into a network structure to reflect their evolutionary relationships. Data integration and associative studies of protein family, function and structure are supported by the iProClass database, which offers value-added descriptions of all UniProt proteins with highly informative links to more than 50 other databases. The PIR system allows consistent, rich and accurate protein annotation for all investigators. Keywords: protein web sites, protein family, functional annotation Introduction system.2 This framework is supported by the iProClass integrated database of protein family, function and structure.3 The high-throughput genome projects have resulted in a rapid iProClass offers value-added descriptions of all UniProt accumulation of genome sequences for a large number of proteins and has highly informative links to more than 50 organisms. -
Positive and Negative Roles of an Initiator Protein at an Origin of Replication
Proc. Natl. Acad. Sci. USA Vol. 83, pp. 9645-9649, December 1986 Genetics Positive and negative roles of an initiator protein at an origin of replication (plasmid R6K/promoter deletions/replication control/autoregulation/immunoassays) MARCIN FILUTOWICZ, MICHAEL J. MCEACHERN, AND DONALD R. HELINSKI Department of Biology, University of California, San Diego, La Jolla, CA 92093 Contributed by Donald R. Helinski, September 5, 1986 ABSTRACT The properties of mutants in the pir gene of origin activity (20) and autogenous regulation of the pir gene, plasmid R6K have suggested that the a protein plays a dual respectively (18, 21). role; it is required for replication to occur and also plays a role A negative role for the irprotein in the control ofR6K copy in the negative control of the plasmid copy number. In our number was suggested originally by the isolation of a mutant present study, we have found that the Xr level in cell extracts of of the R6K derivative plasmid pRK419, designated Cos405, Escherichia coli strains containing R6K derivatives is surpris- that maintained itself at a greatly increased copy number as ingly high (-104 dimers per cell) and that this level is not a result of a single amino acid substitution within the Xr altered in cells carrying high copy number pir mutants. The protein. This mutational change was recessive to wild-type wild-type and a high copy mutant (Cos4O5) pir gene were protein (22). The properties ofthis mutant protein along with inserted downstream of promoters of different strengths to the well-established positive function of ir protein in R6K measure the copy number of an R6K y replicon as a function replication (3, 4) lead to the conclusion that the ir protein ofa 1000-fold range ofintracellular ir concentrations. -
Webnetcoffee
Hu et al. BMC Bioinformatics (2018) 19:422 https://doi.org/10.1186/s12859-018-2443-4 SOFTWARE Open Access WebNetCoffee: a web-based application to identify functionally conserved proteins from Multiple PPI networks Jialu Hu1,2, Yiqun Gao1, Junhao He1, Yan Zheng1 and Xuequn Shang1* Abstract Background: The discovery of functionally conserved proteins is a tough and important task in system biology. Global network alignment provides a systematic framework to search for these proteins from multiple protein-protein interaction (PPI) networks. Although there exist many web servers for network alignment, no one allows to perform global multiple network alignment tasks on users’ test datasets. Results: Here, we developed a web server WebNetcoffee based on the algorithm of NetCoffee to search for a global network alignment from multiple networks. To build a series of online test datasets, we manually collected 218,339 proteins, 4,009,541 interactions and many other associated protein annotations from several public databases. All these datasets and alignment results are available for download, which can support users to perform algorithm comparison and downstream analyses. Conclusion: WebNetCoffee provides a versatile, interactive and user-friendly interface for easily running alignment tasks on both online datasets and users’ test datasets, managing submitted jobs and visualizing the alignment results through a web browser. Additionally, our web server also facilitates graphical visualization of induced subnetworks for a given protein and its neighborhood. To the best of our knowledge, it is the first web server that facilitates the performing of global alignment for multiple PPI networks. Availability: http://www.nwpu-bioinformatics.com/WebNetCoffee Keywords: Multiple network alignment, Webserver, PPI networks, Protein databases, Gene ontology Background tools [7–10] have been developed to understand molec- Proteins are involved in almost all life processes.