Multiple Sequence Alignment
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Bioinformatics 1: Lecture 3
Bioinformatics 1: Lecture 3 •Pairwise alignment •Substitution •Dynamic Programming algorithm Scoring matrix To prepare an alignment, we first consider the score for aligning (associating) any one character of the first sequence with any one character of the second sequence. A A G A C G T T T A G A C T 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 Exact match 0 0 1 0 0 1 0 0 0 0 1/0 0 0 0 0 0 0 1 1 1 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 The cost of mutation is not a constant DNA: A change in the 3rd base in a codon, and sometimes the first base, sometimes conserves the amino acid. No selective pressure. Protein: A change in amino acids that are in the same chemical class conserve their chemical environment. For example: Lys to Arg is conservative because both a positively charged. Conservative amino acid changes N Lys <--> Arg C + N` N N` C N C + C C C N` C C C C O C O C C N N Ile <--> Leu C C C C C C C C O C O C C Ser <--> Thr Asp <--> Glu Asn <--> Gln If the “chemistry” of the sidechain is conserved, then the mutation is less likely to change structure/function. -
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. -
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. -
BASS: Approximate Search on Large String Databases
BASS: Approximate Search on Large String Databases Jiong Yang Wei Wang Philip Yu UIUC UNC Chapel Hill IBM [email protected] [email protected] [email protected] Abstract Similarity search on a string database can be classified into two categories: exact match and approximate match. The In this paper, we study the problem on how to build an index struc- search of exact match looks for substrings in the database, ture for large string databases to efficiently support various types of which is exactly identical to the query pattern while the search string matching without the necessity of mapping the substrings to of approximate match allows some types of imperfection such a numerical space (e.g., string B-tree and MRS-index) nor the re- as substitutions between certain symbols, some degree of mis- striction of in-memory practice (e.g., suffix tree and suffix array). alignment, and the presence of “wild-card” in the query pat- Towards this goal, we propose a new indexing scheme, BASS-tree, tern. We shall mention that supporting approximate match is to efficiently support general approximate substring match (in terms very important to many applications. For instance, biologists of certain symbol substitutions and misalignments) in sublinear time have observed that mutations between certain pair of amino on a large string database. The key idea behind the design is that all acids may occur at a noticeable probability in some proteins positions in each string are grouped recursively into a fully balanced and such a mutation usually does not alter the biological func- tree according to the similarities of the subsequent segments starting tion of the proteins. -
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. -
Computational Biology Lecture 8: Substitution Matrices Saad Mneimneh
Computational Biology Lecture 8: Substitution matrices Saad Mneimneh As we have introduced last time, simple scoring schemes like +1 for a match, -1 for a mismatch and -2 for a gap are not justifiable biologically, especially for amino acid sequences (proteins). Instead, more elaborated scoring functions are used. These scores are usually obtained as a result of analyzing chemical properties and statistical data for amino acids and DNA sequences. For example, it is known that same size amino acids are more likely to be substituted by one another. Similarly, amino acids with same affinity to water are likely to serve the same purpose in some cases. On the other hand, some mutations are not acceptable (may lead to demise of the organism). PAM and BLOSUM matrices are amongst results of such analysis. We will see the techniques through which PAM and BLOSUM matrices are obtained. Substritution matrices Chemical properties of amino acids govern how the amino acids substitue one another. In principle, a substritution matrix s, where sij is used to score aligning character i with character j, should reflect the probability of two characters substituing one another. The question is how to build such a probability matrix that closely maps reality? Different strategies result in different matrices but the central idea is the same. If we go back to the concept of a high scoring segment pair, theory tells us that the alignment (ungapped) given by such a segment is governed by a limiting distribution such that ¸sij qij = pipje where: ² s is the subsitution matrix used ² qij is the probability of observing character i aligned with character j ² pi is the probability of occurrence of character i Therefore, 1 qij sij = ln ¸ pipj This formula for sij suggests a way to constrcut the matrix s. -
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. -
B.Sc. (Hons.) Biotech BIOT 3013 Unit-5 Satarudra P Singh
B.Sc. (Hons.) Biotechnology Core Course 13: Basics of Bioinformatics and Biostatistics (BIOT 3013 ) Unit 5: Sequence Alignment and database searching Dr. Satarudra Prakash Singh Department of Biotechnology Mahatma Gandhi Central University, Motihari Challenges in bioinformatics 1. Obtain the genome of an organism. 2. Identify and annotate genes. 3. Find the sequences, three dimensional structures, and functions of proteins. 4. Find sequences of proteins that have desired three dimensional structures. 5. Compare DNA sequences and proteins sequences for similarity. 6. Study the evolution of sequences and species. Sequence alignments lie at the heart of all bioinformatics Definition of Sequence Alignment • Sequence alignment is the procedure of comparing two or more sequences by searching for a series of individual characters or character patterns that are in the same order in the sequences. LGPSSKQTGKGS - SRIWDN Global Alignment LN – ITKSAGKGAIMRLFDA --------TGKG --------- Local Alignment --------AGKG --------- • In global alignment, an attempt is made to align the entire sequences, as many characters as possible. • In local alignment, stretches of sequence with the highest density of matches are given the highest priority, thus generating one or more islands of matches in the aligned sequences. • Eg: problem of locating the famous TATAAT - box (a bacterial promoter) in a piece of DNA. Method for pairwise sequence Alignment: Dynamic Programming • Global Alignment: Needleman- Wunsch Algorithm • Local Alignment: Smith-Waterman Algorithm Needleman & Wunsch algorithm : Global alignment • There are three major phases: 1. initialization 2. Fill 3. Trace back. • Initialization assign values for the first row and column. • The score of each cell is set to the gap score multiplied by the distance from the origin. -
Parameter Advising for Multiple Sequence Alignment
PARAMETER ADVISING FOR MULTIPLE SEQUENCE ALIGNMENT by Daniel Frank DeBlasio Copyright c Daniel Frank DeBlasio 2016 A Dissertation Submitted to the Faculty of the DEPARTMENT OF COMPUTER SCIENCE In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY In the Graduate College THE UNIVERSITY OF ARIZONA 2016 2 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE As members of the Dissertation Committee, we certify that we have read the disser- tation prepared by Daniel Frank DeBlasio, entitled Parameter Advising for Multiple Sequence Alignment and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy. Date: 15 April 2016 John Kececioglu Date: 15 April 2016 Alon Efrat Date: 15 April 2016 Stephen Kobourov Date: 15 April 2016 Mike Sanderson Final approval and acceptance of this dissertation is contingent upon the candidate's submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement. Date: 15 April 2016 Dissertation Director: John Kececioglu 3 STATEMENT BY AUTHOR This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this dissertation are allowable without special permission, pro- vided that accurate acknowledgment of the source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the copyright holder.