Interpro in 2017—Beyond Protein Family and Domain Annotations
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
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. -
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. -
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. -
The Uniprot Knowledgebase BLAST
Introduction to bioinformatics The UniProt Knowledgebase BLAST UniProtKB Basic Local Alignment Search Tool A CRITICAL GUIDE 1 Version: 1 August 2018 A Critical Guide to BLAST BLAST Overview This Critical Guide provides an overview of the BLAST similarity search tool, Briefly examining the underlying algorithm and its rise to popularity. Several WeB-based and stand-alone implementations are reviewed, and key features of typical search results are discussed. Teaching Goals & Learning Outcomes This Guide introduces concepts and theories emBodied in the sequence database search tool, BLAST, and examines features of search outputs important for understanding and interpreting BLAST results. On reading this Guide, you will Be aBle to: • search a variety of Web-based sequence databases with different query sequences, and alter search parameters; • explain a range of typical search parameters, and the likely impacts on search outputs of changing them; • analyse the information conveyed in search outputs and infer the significance of reported matches; • examine and investigate the annotations of reported matches, and their provenance; and • compare the outputs of different BLAST implementations and evaluate the implications of any differences. finding short words – k-tuples – common to the sequences Being 1 Introduction compared, and using heuristics to join those closest to each other, including the short mis-matched regions Between them. BLAST4 was the second major example of this type of algorithm, From the advent of the first molecular sequence repositories in and rapidly exceeded the popularity of FastA, owing to its efficiency the 1980s, tools for searching dataBases Became essential. DataBase searching is essentially a ‘pairwise alignment’ proBlem, in which the and Built-in statistics. -
The FASTA Program Package Introduction
fasta-36.3.8 December 4, 2020 The FASTA program package Introduction This documentation describes the version 36 of the FASTA program package (see W. R. Pearson and D. J. Lipman (1988), “Improved Tools for Biological Sequence Analysis”, PNAS 85:2444-2448, [17] W. R. Pearson (1996) “Effective protein sequence comparison” Meth. Enzymol. 266:227- 258 [15]; and Pearson et. al. (1997) Genomics 46:24-36 [18]. Version 3 of the FASTA packages contains many programs for searching DNA and protein databases and for evaluating statistical significance from randomly shuffled sequences. This document is divided into four sections: (1) A summary overview of the programs in the FASTA3 package; (2) A guide to using the FASTA programs; (3) A guide to installing the programs and databases. Section (4) provides answers to some Frequently Asked Questions (FAQs). In addition to this document, the changes v36.html, changes v35.html and changes v34.html files list functional changes to the programs. The readme.v30..v36 files provide a more complete revision history of the programs, including bug fixes. The programs are easy to use; if you are using them on a machine that is administered by someone else, you can focus on sections (1) and (2) to learn how to use the programs. If you are installing the programs on your own machine, you will need to read section (3) carefully. FASTA and BLAST – FASTA and BLAST have the same goal: to identify statistically signifi- cant sequence similarity that can be used to infer homology. The FASTA programs offer several advantages over BLAST: 1. -
Uniprot.Ws: R Interface to Uniprot Web Services
Package ‘UniProt.ws’ September 26, 2021 Type Package Title R Interface to UniProt Web Services Version 2.33.0 Depends methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8) Imports AnnotationDbi, BiocFileCache, rappdirs Suggests RUnit, BiocStyle, knitr Description A collection of functions for retrieving, processing and repackaging the UniProt web services. Collate AllGenerics.R AllClasses.R getFunctions.R methods-select.R utilities.R License Artistic License 2.0 biocViews Annotation, Infrastructure, GO, KEGG, BioCarta VignetteBuilder knitr LazyLoad yes git_url https://git.bioconductor.org/packages/UniProt.ws git_branch master git_last_commit 5062003 git_last_commit_date 2021-05-19 Date/Publication 2021-09-26 Author Marc Carlson [aut], Csaba Ortutay [ctb], Bioconductor Package Maintainer [aut, cre] Maintainer Bioconductor Package Maintainer <[email protected]> R topics documented: UniProt.ws-objects . .2 UNIPROTKB . .4 utilities . .8 Index 11 1 2 UniProt.ws-objects UniProt.ws-objects UniProt.ws objects and their related methods and functions Description UniProt.ws is the base class for interacting with the Uniprot web services from Bioconductor. In much the same way as an AnnotationDb object allows acces to select for many other annotation packages, UniProt.ws is meant to allow usage of select methods and other supporting methods to enable the easy extraction of data from the Uniprot web services. select, columns and keys are used together to extract data via an UniProt.ws object. columns shows which kinds of data can be returned for the UniProt.ws object. keytypes allows the user to discover which keytypes can be passed in to select or keys via the keytype argument. keys returns keys for the database contained in the UniProt.ws object . -
A Major Update of the Protein Ensemble Database for Intrinsically Disordered Proteins
Lawrence Berkeley National Laboratory Recent Work Title PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins. Permalink https://escholarship.org/uc/item/0jm2h524 Journal Nucleic acids research, 49(D1) ISSN 0305-1048 Authors Lazar, Tamas Martínez-Pérez, Elizabeth Quaglia, Federica et al. Publication Date 2021 DOI 10.1093/nar/gkaa1021 Peer reviewed eScholarship.org Powered by the California Digital Library University of California D404–D411 Nucleic Acids Research, 2021, Vol. 49, Database issue Published online 10 December 2020 doi: 10.1093/nar/gkaa1021 PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins Tamas Lazar 1,2, Elizabeth Mart´ınez-Perez´ 3,4, Federica Quaglia 5, Andras´ Hatos 5, Luc´ıa B. Chemes 6,JavierA.Iserte 3, Nicolas´ A. Mendez´ 6, Nicolas´ A. Garrone 6, Tadeo E. Saldano˜ 7, Julia Marchetti 7, Ana Julia Velez Rueda 7, Pau Bernado´ 8, Martin Blackledge 9, Tiago N. Cordeiro 8,10, Eric Fagerberg 11, Julie D. Forman-Kay 12,13, Maria S. Fornasari 7, Toby J. Gibson 4, Gregory-Neal W. Gomes 14,15, Claudiu C. Gradinaru 14,15, Teresa Head-Gordon 16, Malene Ringkjøbing Jensen 9, Edward A. Lemke 17,18, Sonia Longhi 19, Cristina Marino-Buslje 3, Giovanni Minervini 5, Tanja Mittag 20, Alexander Miguel Monzon 5, Rohit V. Pappu 21, Gustavo Parisi 7, Sylvie Ricard-Blum 22,KierstenM.Ruff21, Edoardo Salladini 19, Marie Skepo¨ 11,23, Dmitri Svergun 24, Sylvain D. Vallet 22, Mihaly Varadi 25, Peter Tompa 1,2,26,*, Silvio C.E. Tosatto 5,* and Damiano Piovesan 5 1VIB-VUB Center for Structural Biology, Flanders Institute for Biotechnology, Brussels 1050, Belgium, 2Structural Biology Brussels, Bioengineering Sciences Department, Vrije Universiteit Brussel, Brussels 1050, Belgium, 3Bioinformatics Unit, Fundacion´ Instituto Leloir, Buenos Aires, C1405BWE, Argentina, 4Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany, 5Dept. -
EBI Web Resources II: Ensembl and Interpro
EBI web resources II: Ensembl and InterPro Yanbin Yin Spring 2013 1 Outline • Intro to genome annotation • Protein family/domain databases – InterPro, Pfam, Superfamily etc. • Genome browser – Ensembl • Hands on Practice 2 Genome annotation • Predict genes (where are the genes?) – protein coding – RNA coding • Function annotation (What are the genes?) – Search against UniProt or NCBI-nr (GenPept) Function vocabularies – Search against protein family/domain databases defined in – Search against Pathway databases Gene Ontology 3 Superfamily Gene3D SCOP CATH PDB 4 InterPro components 1. CATH/Gene3D University College, London, UK 2. PANTHER University of Southern California, CA, USA 3. PIRSF Protein Information Resource, Georgetown University, USA 4. Pfam Wellcome Trust Sanger Institute, Hinxton, UK 5. PRINTS University of Manchester, UK 6. ProDom PRABI Villeurbanne, France 7. PROSITE Swiss Institute of Bioinformatics (SIB), Geneva, Switzerland 8. SMART EMBL, Heidelberg, Germany 9. SUPERFAMILY University of Bristol, UK 10. TIGRFAMs J. Craig Venter Institute, Rockville, MD, US 11. HAMAP Swiss Institute of Bioinformatics (SIB), Geneva, Switzerland CDD components Pfam, SMART, TIGRFAM, COG, KOG, PRK, CD, LOAD 5 6 Protein Classification Nearly all proteins have structural similarities with other proteins and, in some of these cases, share a common evolutionary origin. Proteins are classified to reflect both structural and evolutionary relatedness. Many levels exist in the hierarchy, but the principal levels are family, superfamily and fold, described below. Family: Clear evolutionarily relationship Proteins clustered together into families are clearly evolutionarily related. Generally, this means that pairwise residue identities between the proteins are 30% and greater. Superfamily: Probable common evolutionary origin Proteins that have low sequence identities, but whose structural and functional features suggest that a common evolutionary origin is probable are placed together in superfamilies. -
JOBIM 2020 Montpellier, 30 Juin
JOBIM 2020 Montpellier, 30 juin - 3 juillet Long papers 1 Paper 1 Long-read error correction: a survey and qualitative comparison Pierre Morisse1, Thierry Lecroq2 and Arnaud Lefebvre2 1 Normandie Universit´e,UNIROUEN, INSA Rouen, LITIS, 76000 Rouen, France 2 Normandie Univ, UNIROUEN, LITIS, 76000 Rouen, France Corresponding author: [email protected] Extended preprint: https://doi.org/10.1101/2020.03.06.977975 Abstract Third generation sequencing technologies Pacific Biosciences and Oxford Nanopore Technologies were respectively made available in 2011 and 2014. In contrast with second generation sequencing technologies such as Illumina, these new technologies allow the se- quencing of long reads of tens to hundreds of kbps. These so-called long reads are partic- ularly promising, and are especially expected to solve various problems such as contig and haplotype assembly or scaffolding, for instance. However, these reads are also much more error prone than second generation reads, and display error rates reaching 10 to 30%, depending on the sequencing technology and to the version of the chemistry. Moreover, these errors are mainly composed of insertions and deletions, whereas most errors are substitutions in Illumina reads. As a result, long reads require efficient error correction, and a plethora of error correction tools, directly targeted at these reads, were developed in the past nine years. These methods can adopt a hybrid approach, using complementary short reads to perform correction, or a self-correction approach, only making use of the information contained in the long reads sequences. Both these approaches make use of various strategies such as multiple sequence alignment, de Bruijn graphs, hidden Markov models, or even combine different strategies.