DOCSLIB.ORG

Alignments in Practice BLAST and CLUSTAL

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Alignments in Practice BLAST and CLUSTAL Alignments in Practice BLAST and CLUSTAL Introduction to Bioinformatics Dortmund, 16.-20.07.2007 Lectures: Sven Rahmann Exercises: Udo Feldkamp, Michael Wurst 1 Overview ● Dot Plots ● Nucleotide BLAST ● Protein BLAST ● BLAST Statistics ● BLAT ● CLUSTAL ● JalView 2 Dotter – Tool for Dot Plots ● http://www.cgb.ki.se/cgb/groups/sonnhammer/Dotter.html ● Dotlet: a Java applet for Dot Plots 3 Dot Plots ● Hemoglobin Alpha against Hemoglobin Beta 4 EBI Alignment Service 5 BLAST ● URL: http://www.ncbi.nlm.nih.gov/BLAST/ ● Basic Local Alignment Search Tool 6 Choose the right BLAST 7 Nucleotide BLAST Interface 8 BLAST Parameters ● Expect threshold: low [0.01] = strict high [100] = loose ● Word size: speed vs. sensitivity high = faster low = slower, but more sensitive 9 Protein BLAST 10 Protein BLAST Parameters 11 Translated BLAST ● protein query against nucleotide database – nucleotide sequence not unique – also consider reverse complement ● nucleotide query against protein database – consider all 6 reading frames 12 BLAST Output 13 BLAST Output II Database + Accession Description Bit score E-value Link 14 BLAST Statistics ● How good / reliable is a hit found by BLAST? ● Raw score := score of the alignment according to scoring matrix and gap penalties ● Bit score := score (log2 units), length-normalized ● E-value := Number of hits of such or better score in a hypothetical database of random proteins of the same size 15 More on Statistics ● Null model := random model describing sequences without intentional signal (here: pair of random sequences without intentional similarity) ● (single) p-value for observed score s := Prob(Score >= s) in the null model ● (multiple) p-value := Prob(Score >= s at least once) 16 BLAT ● BLAST-Like Alignment Tool ● index-based ● developed at UC Santa Cruz ● especially for searching in whole genomes ● very fast ● limited to nearly exact matches 17 UCSC Genome Browser + BLAT 18 CLUSTAL 19 What Clustal Did (“Output file”) 20 Clustal Results (pretty) 21 Clustal Results (“alignment file”) CLUSTAL W (1.83) multiple sequence alignment FOS_RAT MMFSGFNADYEASSSRCSSASPAGDSLSYYHSPADSFSSMGSPVNTQDFCADLSVSSANF 60 FOS_MOUSE MMFSGFNADYEASSSRCSSASPAGDSLSYYHSPADSFSSMGSPVNTQDFCADLSVSSANF 60 FOS_HUMAN MMFSGFNADYEASSSRCSSASPAGDSLSYYHSPADSFSSMGSPVNAQDFCTDLAVSSANF 60 FOS_CHICK MMYQGFAGEYEAPSSRCSSASPAGDSLTYYPSPADSFSSMGSPVNSQDFCTDLAVSSANF 60 FOS_ZEBRAFISH MMFTSLNADCDASS-RCSTASPSGDSVGYY------------PLNQTQEFTDLSVSSASF 47 **: .: .: :*.* ***:***:***: ** *:* : :**:****.* FOS_RAT IPTVTAISTSPDLQWLVQPTLVSSVAPSQTRAPHPYGLPTPS-TGAYARAGVVKTMSGGR 119 FOS_MOUSE IPTVTAISTSPDLQWLVQPTLVSSVAPSQTRAPHPYGLPTQS-AGAYARAGMVKTVSGGR 119 FOS_HUMAN IPTVTAISTSPDLQWLVQPALVSSVAPSQTRAPHPFGVPAPS-AGAYSRAGVVKTMTGGR 119 FOS_CHICK VPTVTAISTSPDLQWLVQPTLISSVAPSQNRG-HPYGVPAPAPPAAYSRPAVLKAP-GGR 118 FOS_ZEBRAFISH VPTVTAISSCPDLQWMVQP-MISSAAPS-------NGAAQSYNPSSYPKMRVTGAK---- 95 :*******:.*****:*** ::**.*** * . ..:*.: : : FOS_RAT AQSIGRRGKVEQLSPEEEEKRRIRRERNKMAAAKCRNRRRELTDTLQAETDQLEDEKSAL 179 FOS_MOUSE AQSIGRRGKVEQLSPEEEEKRRIRRERNKMAAAKCRNRRRELTDTLQAETDQLEDEKSAL 179 FOS_HUMAN AQSIGRRGKVEQLSPEEEEKRRIRRERNKMAAAKCRNRRRELTDTLQAETDQLEDEKSAL 179 FOS_CHICK GQSIGRRGKVEQLSPEEEEKRRIRRERNKMAAAKCRNRRRELTDTLQAETDQLEEEKSAL 178 FOS_ZEBRAFISH --TSNKRSRSEQLSPEEEEKKRVRRERSKMAAAKCRNRRRELTDTLQAETDQLEDEKSAL 153 : .:*.: **********:*:****.**************************:***** FOS_RAT QTEIANLLKEKEKLEFILAAHRPACKIPNDLGFPEE----MSVTS-LDLTGGLPEATTPE 234 FOS_MOUSE QTEIANLLKEKEKLEFILAAHRPACKIPDDLGFPEE----MSVAS-LDLTGGLPEASTPE 234 FOS_HUMAN QTEIANLLKEKEKLEFILAAHRPACKIPDDLGFPEE----MSVAS-LDLTGGLPEVATPE 234 FOS_CHICK QAEIANLLKEKEKLEFILAAHRPACKMPEELRFSEE----LAAATALDLG----APSPAA 230 FOS_ZEBRAFISH QNDIANLLKEKERLEFILAAHKPICKIPADASFPEPSSSPMSSISVPEIVTTSVVSSTPN 21322 * :*********:********:* **:* : *.* :: : :: :.. Clustal Guide Tree 23 Clustal Guide Tree ● Guide Tree is not a phylogenetic tree, just a computational device ● Cladogram: edge lengths have no meaning ● Phylogram: edgle lengths correspond to distances 24 JalView: Alignment Editor (start from the CLUSTAL web site) 25 Simple JalView Window ● Simple alignment editor (Java applet) ● Complex alignment editor (Java application) – Web Start, or – Download installer 26 Starting or Installing JalView www.jalview.org 27 Multiple Alignment @ BiBiServ 28 For Windows/MAC: QAlign2 ● URL: http://gi.cebitec.uni-bielefeld.de/QAlign/ ● Live Demo of QAlign2 29.
Load more

Recommended publications
  • 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.
    [Show full text]
  • Homology & Alignment
    Protein Bioinformatics Johns Hopkins Bloomberg School of Public Health 260.655 Thursday, April 1, 2010 Jonathan Pevsner Outline for today 1. Homology and pairwise alignment 2. BLAST 3. Multiple sequence alignment 4. Phylogeny and evolution Learning objectives: homology & alignment 1. You should know the definitions of homologs, orthologs, and paralogs 2. You should know how to determine whether two genes (or proteins) are homologous 3. You should know what a scoring matrix is 4. You should know how alignments are performed 5. You should know how to align two sequences using the BLAST tool at NCBI 1 Pairwise sequence alignment is the most fundamental operation of bioinformatics • It is used to decide if two proteins (or genes) are related structurally or functionally • It is used to identify domains or motifs that are shared between proteins • It is the basis of BLAST searching (next topic) • It is used in the analysis of genomes myoglobin Beta globin (NP_005359) (NP_000509) 2MM1 2HHB Page 49 Pairwise alignment: protein sequences can be more informative than DNA • protein is more informative (20 vs 4 characters); many amino acids share related biophysical properties • codons are degenerate: changes in the third position often do not alter the amino acid that is specified • protein sequences offer a longer “look-back” time • DNA sequences can be translated into protein, and then used in pairwise alignments 2 Find BLAST from the home page of NCBI and select protein BLAST… Page 52 Choose align two or more sequences… Page 52 Enter the two sequences (as accession numbers or in the fasta format) and click BLAST.
    [Show full text]
  • Assembly Exercise
    Assembly Exercise Turning reads into genomes Where we are • 13:30-14:00 – Primer Design to Amplify Microbial Genomes for Sequencing • 14:00-14:15 – Primer Design Exercise • 14:15-14:45 – Molecular Barcoding to Allow Multiplexed NGS • 14:45-15:15 – Processing NGS Data – de novo and mapping assembly • 15:15-15:30 – Break • 15:30-15:45 – Assembly Exercise • 15:45-16:15 – Annotation • 16:15-16:30 – Annotation Exercise • 16:30-17:00 – Submitting Data to GenBank Log onto ILRI cluster • Log in to HPC using ILRI instructions • NOTE: All the commands here are also in the file - assembly_hands_on_steps.txt • If you are like me, it may be easier to cut and paste Linux commands from this file instead of typing them in from the slides Start an interactive session on larger servers • The interactive command will start a session on a server better equipped to do genome assembly $ interactive • Switch to csh (I use some csh features) $ csh • Set up Newbler software that will be used $ module load 454 A norovirus sample sequenced on both 454 and Illumina • The vendors use different file formats unknown_norovirus_454.GACT.sff unknown_norovirus_illumina.fastq • I have converted these files to additional formats for use with the assembly tools unknown_norovirus_454_convert.fasta unknown_norovirus_454_convert.fastq unknown_norovirus_illumina_convert.fasta Set up and run the Newbler de novo assembler • Create a new de novo assembly project $ newAssembly de_novo_assembly • Add read data to the project $ addRun de_novo_assembly unknown_norovirus_454.GACT.sff
    [Show full text]
  • 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.
    [Show full text]
  • Multiple Alignments, Blast and Clustalw
    Multiple alignments, blast and clustalW 1. Blast idea: a. Filter out low complexity regions (tandem repeats… that sort of thing) [optional] b. Compile list of high-scoring strings (words, in BLAST jargon) of fixed length in query (threshold T) c. Extend alignments (highs scoring pairs) d. Report High Scoring pairs: score at least S (or an E value lower than some threshold) 2. Multiple Sequence Alignments: a. Attempts to extend dynamic programming techniques to multiple sequences run into problems after only a few proteins (8 average proteins were a problem early in 2000s) b. Heuristic approach c. Idea (Progressive Approach): i. homologous sequences are evolutionarily related ii. Build multiple alignment by series of pairwise alignments based off some phylogenetic tree (the initial tree or the guide tree ) iii. Add in more distantly related sequences d. Progressive Sequence alignment example: 1. NYLS & NKYLS: N YLS N(K|-)YLS NKYLS 2. NFS & NFLS: N YLS NF S NF(L|-)S NKYLS NFLS 3. N(K|-)YLS & NF(L|-)S N YLS N(K|-)(Y|F)(L|-)S NKYLS N YLS N F S N FLS e. Assessment: i. Works great for fairly similar sequences ii. Not so well for highly divergent ones f. Two Problems: i. local minimum problem: Algorithm greedily adds sequences based off of tree— might miss global solution ii. Alignment parameters: Mistakes (misaligned regions) early in procedure can’t be corrected later. g. ClustalW does multiple alignments and attempts to solve alignment parameter problem i. gap costs are dynamically varied based on position and amino acid ii. weight matrices are changed as the level of divergence between sequence increases (say going from PAM30 -> PAM60) iii.
    [Show full text]
  • BLAST Practice
    Using BLAST BLAST (Basic Local Alignment Search Tool) is an online search tool provided by NCBI (National Center for Biotechnology Information). It allows you to “find regions of similarity between biological sequences” (nucleotide or protein). The NCBI maintains a huge database of biological sequences, which it compares the query sequences to in order to find the most similar ones. Using BLAST, you can input a gene sequence of interest and search entire genomic libraries for identical or similar sequences in a matter of seconds. The amount of information on the BLAST website is a bit overwhelming — even for the scientists who use it on a frequent basis! You are not expected to know every detail of the BLAST program. BLAST results have the following fields: E value: The E value (expected value) is a number that describes how many times you would expect a match by chance in a database of that size. The lower the E value is, the more significant the match. Percent Identity: The percent identity is a number that describes how similar the query sequence is to the target sequence (how many characters in each sequence are identical). The higher the percent identity is, the more significant the match. Query Cover: The query cover is a number that describes how much of the query sequence is covered by the target sequence. If the target sequence in the database spans the whole query sequence, then the query cover is 100%. This tells us how long the sequences are, relative to each other. FASTA format FASTA format is used to represent either nucleotide or peptide sequences.
    [Show full text]
  • L11: Alignments 5 Evolution: MEGA
    Biochem 711 – 2008 1 L11: Alignments Evolution: MEGA Table of Contents Introduction............................................................................................. 3 Acknowledgements.................................................................................. 4 L11 Exercise A: Set up............................................................................. 4 1. Launch MEGA............................................................................................... 4 2. Retrieve Sequence ........................................................................................ 5 L11 Exercise B: BLAST and Align within MEGA ....................................... 6 1. Launch MEGA web browser........................................................................... 6 2. BLAST search within MEGA .......................................................................... 6 2.1. Paste sequence........................................................................................ 6 2.2. Select the database to be searched ............................................................ 6 2.3. Optimization algorithm: blastn................................................................... 7 2.4. Press BLAST ............................................................................................ 7 2.5. BLAST results .......................................................................................... 7 2.6. Selecting results for alignment................................................................... 9 3. Preparing
    [Show full text]
  • Teachers Notes(.Pdf, 116.2
    FUNCTION FINDERS BLAST Teacher’s notes BACKGROUND TO ACTIVITY Function Finders BLAST provides a hands-on exercise that introduces the concept of genes coding for proteins. The activity involves translating DNA sequences into amino acid chains and using this information to find a matching protein with the correct corresponding sequence. Estimated duration: 30 – 45 minutes MATERIALS TO RUN THE ACTIVITY - Student worksheets - Codon Wheel sheets - Function Finders presentation files - Computer (one between two students) - Internet connection for each computer Optional animations (recommended for AS Level) DNA to protein animation: www.yourgenome.org/video/from-dna-to-protein Optional activity: View the proteins in 3D Rasmol (available from www.rasmol.org) is a molecular modelling software that can be used to view proteins in 3D. It can be used by the teacher or students to view the proteins they have identified and initiate discussions about the tertiary structures of proteins. It is not an essential part of the activity. ACTIVITY PREPARATION The following activity components need to be prepared before the activity starts. Working in pairs, students require: - One Function Finders worksheet - One Codon Wheel sheet - One computer We recommend one computer between two students, but the activity can be run with groups of three students. OPTIONAL ACTIVITY PREPARATION 1/8 yourgenome.org FUNCTION FINDERS BLAST Teacher’s notes 1. Download Rasmol program (optional) If you wish to use the Rasmol programme to view the protein structure, download the programme from www.rasmol.org and select “Latest windows installer” at the top of the page. 2. Save protein files If you plan to use the Rasmol programme ensure you have the protein files saved in an accessible folder on the laptops.
    [Show full text]
  • Bioinformatics: Analyzing DNA Sequence Using BLAST
    Bioinformatics: Analyzing DNA Sequence using BLAST By Nadim Naimur Rahman ID#03201042 Department of Computer Science and Engineering BRAC University Thesis submitted to the faculty of the BRAC University In partial fulfillment of the requirements of the degree of BACHELOR OF SCIENCE in Computer Science and Engineering Supervised By Dr. Mumit Khan Associate Professor BRAC University September 5th, 2007 BRAC University DHAKA ii DECLARATION We, hereby, declare that the work presented in this thesis is the outcome of the investigation performed by me under the supervision of Dr. Mumit Khan, Associate Professor, Department of Computer Science and Engineering, BRAC University, Dhaka. I also declare that no part of this thesis and thereof has been or is being submitted elsewhere for the award of any degree or Diploma. Sign ____________________ (Nadim Naimur Rahman) Countersigned _____________ (Dr. Mumit Khan) Supervisor iii Acknowledgements: I would like to thank my thesis supervisor Dr. Mumit Khan for his all- out help and cooperation during the period of my thesis. His guidance played a pivotal part in completing my thesis. I am also thankful to Dr. Naiyyum Chowdhury and Ms. Nazli Sharmin of the BRAC University Biotechnology Department for helping me out in the interpretation process of the output. iv Bioinformatics: Analyzing DNA Sequence using BLAST Nadim Naimur Rahman Abstract This paper attempts to use the BLAST simulator to analyze a DNA sequence and interpret the results in a way that are understandable for biotechnologists. It shows how to install, build and run the simulator using an input DNA sequence, comparing it with a database and obtain an output that can be used for many different purposes.
    [Show full text]
  • Hmmer and Applications
    HMMER AND APPLICATIONS MVE360 – Bioinformatics 2013 Fredrik Boulund [email protected] Bioscience / Mathematical Statistics This is me • MSc Biotechnology / Mathematical statistics • PhD student Bioscience / Mathematical statistics • Research on large scale data analysis (metagenomics) • Researcher by day, musician by night • Play guitar in rock band DÖDAREN • (check us out on Spotify or come see us play sometime ;) This talk • HMMER • What it is, what are profile HMMs etc. • A brief history of [HMMER in] time • Example of an application of HMMER in metagenomics: • Finding antibiotic resistance genes in the environment What is HMMER • Sequence alignment software based on a statistical framework using profile hidden Markov models (HMM) Profile HMMs • Probabilistic models of multiple sequence alignments Alternatives • SAM (1994) (Sequence Alignment and Modeling system) • Richard Hughey • Kevin Karplus • Anders Krogh • PSI-BLAST (1997) (Position-Specific Iterative BLAST) • Stephen F. Altschul et al. (PSI-)BLAST vs HMMER BLAST HMMER • Single query sequence • Based on profile HMMs • String matching with • Higher accuracy advanced heuristics for speed • Able to detect even more remote homologs than • Mainly good for finding closely related sequences PSI-BLAST (PSI-BLAST) • Uses position-specific scoring matrices to detect more remote homologs A brief history of HMMER • Based on the principles in: • Krogh,A., Brown,M., Mian,I.S., Sjolander,K. and Haussler,D. (1994) Hidden Markov models in computational biology: Applications to protein modeling. J. Mol. Biol., 235, 1501–1531. • Durbin, Richard; Sean R. Eddy, Anders Krogh, Graeme Mitchison (1998). Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press. • Historically very slow • 100-1000 times slower than BLAST • Instrumental in the construction of: • Pfam • PROSITE • InterPro HMMER3 • Complete rewrite of HMMER2, focus on improving speed: • Eddy, S.R., 2011.
    [Show full text]
  • Predicting Protein Structure and Function with Interpro
    Predicting protein structure and function with InterPro Phil Jones, Alex Mitchell, Amaia Sangrador (V4.0, Jun 2011) www.ebi.ac.uk Contents Course Information ........................................................................................... 3 Course learning objectives ............................................................................... 3 An introduction to InterPro ............................................................................... 4 I. Searching InterPro using a text search ........................................................ 7 Learning objectives ............................................................................................. 7 What information can be found in the InterPro entry page? ................................. 8 How do I interpret an InterPro protein view? ........................................................ 9 Summary ........................................................................................................... 11 Exercises .......................................................................................................... 11 Exercise 1 – Searching InterPro using a UniPro identifier ........................... 11 Exercise 2 – Exploring InterPro entries: General annotation ....................... 12 Exercise 3 – Exploring InterPro entries: Relationships ................................ 13 Exercise 4 – Exploring InterPro entries: Structure ....................................... 14 II. InterPro Scan ..............................................................................................
    [Show full text]
  • Sequence Based Function Annotation
    Sequence Based Function Annotation Qi Sun Bioinformatics Facility Biotechnology Resource Center Cornell University Workflow of genomic projects for non-model organisms RNA-seq data Genomic sequencing Assembly (Trinity) Assembly (SOAP de novo) ORF prediction (Trinity) Gene prediction (Maker) Function prediction Sequence Based Function Annotation 1. Given a sequence, how to predict its biological function? 2. How to describe the function of a gene? 3. How to works with 50,000 genes? 1. Given a protein sequence, how to predict its function? >unknow_protein_1 MVHLTDAEKAAVSCLWGKVNSDEVGGEALGRLLVVYPWTQR YFDSFGDLSSASAIMGNAKVKAHGKKVITAFNDGLNHLDSL KGTFASLSELHCDKLHVDPENFRLLGNMIVIVLGHHLGKDF TPAAQAAFQKVVAGVATALAHKYH Common approaches • Identify the homologous gene in a different species with good function annotation; (BLAST, et al.) • Identify conserved motif; (PFAM, InterProScan, et al.) Alternative approaches • Protein 3D structure prediction (threading methods) • Co-expression network modules; (Genevestigator) • Linkage or association mapping; • … BLAST does Local Alignment (B asic Local Alignment Search Tool ) Local Alignment vs Global Alignment (Bowtie, BWA, ClustalW, et al) HSP-1 HSP-2 NCBI BLAST • How does BLAST work? • BLAST and Psi-BLAST: Position independent and position specific scoring matrix. How does BLAST work Step 2. Score each alignment, and report the top alignments Number of Chance Alignments = 2 X 10 -73 Match=+2 Mismatch=-3 Gap -(5 + 4(2))= -13 - NCBI Discovery Workshops BLOSUM62, a position independent matrix How does BLAST work
    [Show full text]