Alignment principles and homology searching using (PSI-)BLAST. Jaap Heringa Centre for Integrative Bioinformatics VU (IBIVU)

Size: px
Start display at page:

Download "Alignment principles and homology searching using (PSI-)BLAST. Jaap Heringa Centre for Integrative Bioinformatics VU (IBIVU)"

Transcription

1 Alignment principles and homology searching using (PSI-)BLAST Jaap Heringa Centre for Integrative Bioinformatics VU (IBIVU)

2 Bioinformatics Nothing in Biology makes sense except in the light of evolution (Theodosius Dobzhansky ( )) Nothing in bioinformatics makes sense except in the light of Biology

3 Evolution Four requirements: Template structure providing stability (DNA) Copying mechanism (meiosis) Mechanism providing variation (mutations; insertions and deletions; crossing-over; etc.) Selection (enzyme specificity, activity, etc.)

4 Evolution Ancestral sequence: ABCD ACCD (B C) ABD (C ø) mutation deletion ACCD or ACCD Pairwise Alignment AB D A BD See Primer of Genome Science P. 114 box Phylogenetics

5 Evolution Ancestral sequence: ABCD ACCD (B C) ABD (C ø) mutation deletion ACCD or ACCD Pairwise Alignment AB D A BD true alignment See Primer of Genome Science P. 114 box Phylogenetics

6 Comparing two sequences We want to be able to choose the best alignment between two sequences. Alignment assumes divergent evolution (common ancestry) as opposed to convergent evolution The first sequence to be compared is assigned to the horizontal axis and the second is assigned to the vertical axis. See Primer of Genome Science P box Pairwise Sequence Alignment

7 M T S A V L P A A Y D R K H T T S W Q MTSAVLPAAYDRKHTSIIFQTSWQ All possible alignments between the two sequences can be represented as a path through the search matrix

8 M T S A V L P A A Y D R K H T T S W Q MTSAVLPAAYDRKHTSIIFQTSWQ Corresponds to stretch SIIFQ in horizontal sequence (indel) All possible alignments between the two sequences can be represented as a path through the search matrix

9 A protein sequence alignment MSTGAVLIY--TSILIKECHAMPAGNE GGILLFHRTHELIKESHAMANDEGGSNNS A DNA sequence alignment attcgttggcaaatcgcccctatccggccttaa attt---ggcggatcg-cctctacgggcc----

10 Sequence alignment History 1970 Needleman-Wunsch global pair-wise alignment 1981 Smith-Waterman local pair-wise alignment 1984 Hogeweg-Hesper progressive multiple alignment 1989 Lipman-Altschul-Kececioglu simultaneous multiple alignment 1994 Hidden Markov Models (HMM) for multiple alignment 1996 Iterative strategies for progressive multiple alignment revived 1997 PSI-Blast (PSSM)

11 Pair-wise alignment Combinatorial explosion - 1 gap in 1 sequence: n+1 possibilities - 2 gaps in 1 sequence: (n+1)n - 3 gaps in 1 sequence: (n+1)n(n-1), etc. 2n (2n)! 2 2n = ~ n (n!) 2 πn T D W V T A L K T D W L - - I K 2 sequences of 300 a.a.: ~10 88 alignments 2 sequences of 1000 a.a.: ~ alignments!

12 Dynamic programming Scoring alignments i j l a, b S a,b = + s ( ) Nk gp(k) k gp(k) is gap of size k, N k is the number of gaps of length k gp(k) = -P open -k P extension affine gap penalties P open and P extension are the penalties for gap initialisation and extension, respectively i j l a, b s ) ( describes the likelihood of a given residue match in the alignment

13 Amino acid exchange matrices How do we get one? First systematic method to derive amino acid exchange matrices by Margaret Dayhoff et al. (1978) Atlas of Protein Structure. There are now various matrix series (PAM, BLOSUM) corresponding to different evolutionary speeds or time since divergence And how do we get associated gap penalties? Gap-opening penalty Gap-extension penalty Formalisms are available for exchange matrices but for gap penalties no formal theory exists yet. Most researchers use recommended gap penalty values provided by experts

14 Dynamic programming Scoring alignments T D W V T A L K T D W L - - I K Amino Acid Exchange Matrix 10 1 Affine gap penalties (P open, P extension ) Gap is 2 positions long Score: s(t,t)+s(d,d)+s(w,w)+s(v,l) -P open -2P ext + +s(l,i)+s(k,k)

15 A 2 R -2 6 N D C Q E G H I L K M F P S T W Y V PAM250 matrix amino acid exchange matrix (log odds) B Z A R N D C Q E G H I L K M F P S T W Y V B Z Positive exchange values denote mutations that are more likely than randomly expected, while negative numbers correspond to avoided mutations compared to the randomly expected situation

16 Pairwise sequence alignment needs M D A S T I L C G S sense of evolution Global dynamic programming MDAGSTVILCFVG Search matrix Evolution Amino Acid Exchange Matrix MDAGSTVILCFVG- MDAAST-ILC--GS Alignment Gap penalties (open,extension)

17 Pairwise sequence alignment Global dynamic programming M D A S T I L C G S MDAGSTVILCFVG Search matrix Evolution Amino Acid Exchange Matrix MDAGSTVILCFVG- MDAAST-ILC--GS Gap penalties (open,extension)

18 Global dynamic programming j-1 i-1 Max{S 0<x<i-1, j-1 - Pi - (i-x-1)px} S i,j = s i,j + Max S i-1,j-1 Max{S i-1, 0<y<j-1 - Pi - (j-y-1)px}

19 Global dynamic programming

20 Global dynamic programming

21 Pairwise alignment Global alignment: all gaps are penalised Semi-global alignment: N- and C-terminal gaps (end-gaps) are not penalised MSTGAVLIY--TS GGILLFHRTSGTSNS End-gaps End-gaps

22 Local dynamic programming (Smith & Waterman, 1981) E D A S T I L C G S LCFVMLAGSTVIVGTR Search matrix AGSTVIVG Amino Acid Exchange Matrix Gap penalties (open, extension) A-STILCG This is a local alignment (only part of the sequences aligned) Negative numbers

23 Local dynamic programming (Smith & Waterman, 1981) j-1 i-1 S i,j + Max{S 0<x<i-1,j-1 -Pi -(i-x-1)px} S i,j = Max S i,j + S i-1,j-1 S i,j + Max {S i-1,0<y<j-1 -Pi -(j-y-1)px} 0

24 Local dynamic programming

25 Multiple sequence alignment (MSA) of 12 * Flavodoxin + chey sequence

26 Progressive multiple alignment - general principle Score 1-2 Score Score Allagainstall pairwise alignment 5 5 Scores to distances Scores Similarity matrix Iteration possibilities Guide tree Multiple alignment

27 Sequence database (or homology) searching -available techniques Dynamic Programming (DP) FASTA BLAST and PSI-BLAST QUEST HMMER SAM-T99 This lecture Fast heuristics Hidden Markov modelling (more recent, slow) DP too slow for repeated database searches

28 Homology Searching Motivation If you have an unknown gene, you can try and find a homologous sequence (an ortholog or a paralog) in an annotated sequence database, i.e. a database containing sequences for which the functions are known You then transfer the information from a putatively homologous database sequence to the query sequence This transfer of information based on homology has arguably produced more knowledge about genes than any other technique See Primer of Genome Science Pp box GenBank Files

29 Heuristic Alignment Motivation dynamic programming has performance O(mn), where m and n are the sequence lengths, which is too slow for large databases with high query traffic heuristic methods do fast approximation to dynamic programming FASTA [Pearson & Lipman, 1988] BLAST [Altschul et al., 1990]

30 Heuristic Alignment Motivation consider the task of searching SWISS-PROT against a query sequence: say our query sequence is 362 amino-acids long SWISS-PROT release 38 contains 29,085,265 amino acids finding local alignments via dynamic programming would entail O(10 10 ) matrix operations many servers handle thousands of such queries a day (NCBI > 50,000)

31 BLAST Basic Local Alignment Search Tool BLAST heuristically finds high scoring segment pairs (HSPs): identical length segments each time from 2 sequences (query and database sequence) with statistically significant match scores i.e. ungapped local alignments key tradeoff: sensitivity vs. speed Sensitivity = number of significant matches detected/ number of significant matches in DB

32 BLAST Overview Given: query sequence q, word length w, word score threshold T, segment score threshold S compile a list of words that score at least T when compared to words from q To gain speed, BLAST generates all words (tripeptides) from a query sequence and for each of those the derivation of a table of similar tripeptides: the number of tripeptides is only a fraction of total number possible. scan database for matches to words in list The initial search is done for each tripeptide that can be found in the table of similar tripeptides for each query tripeptide, and scores at least the threshold value T when compared to the query tripeptide using a substitution matrix for scoring. extend all matches to seek high-scoring segment pairs BLAST quickly scans each sequence in a database of protein sequences for ungapped regions showing high similarity, which are called high-scoring segment pairs (HSP), using the tables of similar peptides. The word hits are extended in either direction in an attempt to generate an alignment with a score exceeding the threshold of S, and as far as the cumulative alignment score can be increased. Return: segment pairs (HSPs) scoring at least S

33 Compiling list of words Given: query sequence: QLNFSAGW word length w = 3 word score threshold T = 8 Step 1: determine all words of length w in query sequence QLN LNF NFS FSA SAG AGW

34 Compiling list of words (Ctd) Step 2: determine all words that score at least T when compared to a word in the query sequence: words from query words w/ T=8 sequence QLN QLN=11, QMD=9, HLN=8, ZLN=9, LNF LNF=9, LBF=8, LBY=7, FNW=7, NFS NFS=12, AFS=8, NYS=8, DFT=10, SAG none...

35 Scanning the Database Search all sequences in the database for all occurrences of query words that Remember hits

36 Extending Hits Extend hits in both directions (without allowing gaps) Terminate extension in one direction when score falls certain distance below best score for shorter extensions return segment pairs scoring at least S

37 Sensitivity versus Running Time the main parameter controlling the sensitivity vs. running-time trade-off is T (threshold for what becomes a query word) small T: greater sensitivity, more hits to expand large T: lower sensitivity, fewer hits to expand

38 BLAST Notes may fail to find all HSPs may miss seeds if T is too stringent extension is greedy empirically, 10 to 50 times faster than Smith- Waterman is a heuristic local alignment technique large impact: NCBI s BLAST server handles more than 50,000 queries a day most used bioinformatics program

39 BLAST flavours blastp compares an amino acid query sequence against a protein sequence database blastn compares a nucleotide query sequence against a nucleotide sequence database blastx compares the six-frame conceptual protein translation products of a nucleotide query sequence against a protein sequence database tblastn compares a protein query sequence against a nucleotide sequence database translated in six reading frames tblastx compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.

40 More Recent BLAST Extensions the two-hit method gapped BLAST PSI-BLAST all are aimed at increasing sensitivity while limiting run-time Altschul et al., Nucleic Acids Research 1997

41 The Two-Hit Method extension step typically accounts for 90% of BLAST s execution time key idea: do extension only when there are two hits on the same diagonal within distance A of each other to maintain sensitivity, lower T parameter more single hits found but only small fraction have associated 2nd hit

42 The Two-Hit Method Figure from: Altschul et al. Nucleic Acids Research 25, 1997

43 Gapped BLAST Start gapped alignment only if two-hit extension has a sufficiently high score find length-11 segment with highest score; use central pair in this segment as seed run DP process both forward & backward from seed prune cells when local alignment score falls a certain distance below best score yet

44 Gapped BLAST The black parts in the figure are the parts that are covered by Dynamic Programming starting in two directions from the seed: the best alignment found in both directions are then combined in the final optimal gapped alignment. Figure from: Altschul et al. Nucleic Acids Research 25, 1997

45 BLAST usage BLAST produces a list of sequences that score higher than the specified threshold (putative homologs) But there is always the problem of false positives and false negatives As a trick to find more sequences, you can use database sequences found as a query for a new BLAST search or use PSI-BLAST Q Pos. T Neg. DB See Primer of Genome Science P box Searching Sequence Databases Using BLAST

46 PSI-BLAST PSI (Position Specific Iterated) BLAST basic idea: 1. Carry out gapped-blast using the query sequence to find first hits Query sequence is first scanned for the presence of so-called low-complexity regions (Wooton and Federhen, 1996), i.e. regions with a biased composition likely to lead to spurious hits are excluded from alignment. 2. use results from (gapped) BLAST query to construct a profile matrix (PSSM), containing information about the query sequence and hits found The program takes significant local alignments found (E-value better than threshold), constructs a (master-slave) multiple alignment and abstracts a position specific scoring matrix (PSSM) from this alignment. 3. search database with PSSM (containing improved information from multiple sequence segments) instead of single query sequence 4. Iterate preceding two steps Rescan the database in a subsequent round to find more homologous sequences. Iteration continues until user decides to stop or search has converged (no more hits found)

47 Lowcomplexity region make new PSSM Q Q A C D.. Y Pi Px A C D.. Y Pi Px PSI-BLAST iteration xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx Gapped BLAST search make PSSM Gapped BLAST search Query sequence Query sequence Database hits PSSM PSSM Database hits

48 A Profile Matrix (Position Specific Scoring Matrix PSSM)

49 Searching with a Profile PSI BLAST aligning profile matrix to a simple sequence like aligning two sequences except score for aligning a character with a matrix position is given by the matrix itself not a substitution matrix

50 PSI BLAST: Constructing the Profile Matrix Remember that only local fragments are fished out of the database by BLAST! These can cover only part of the query sequence. Figure from: Altschul et al. Nucleic Acids Research 25, 1997

51 PSI-BLAST output example

52 Normalised sequence similarity The p-value is defined as the probability of seeing at least one unrelated score S greater than or equal to a given score x in a database search over n sequences. This probability follows the Poisson distribution (Waterman and Vingron, 1994): P(x, n) = 1 e -n P(S x), where n is the number of sequences in the database Depending on x and n (fixed)

53 Normalised sequence similarity Statistical significance The E-value is defined as the expected number of nonhomologous sequences with score greater than or equal to a score x in a database of n sequences: E(x, n) = n P(S x) if E-value = 0.01, then the expected number of random hits with score S x is 0.01, which means that this E- value is expected by chance only once in 100 independent searches over the database. if the E-value of a hit is 5, then five fortuitous hits with S x are expected within a single database search, which renders the hit not significant.

54 Normalised sequence similarity Statistical significance Database searching is commonly performed using an E-value in between 0.1 and Low E-values decrease the number of false positives in a database search, but increase the number of false negatives, thereby lowering the sensitivity of the search.

55 Functional annotation by BLAST local search Serious problem: multi-domain proteins See Primer of Genome Science Pp : Functional Annotation and Gene Family Clusters

56 Homology-derived Secondary Structure of Proteins (HSSP) Sander & Schneider, 1991

57 Literature: Read the following pages in Gibson and Muse s Primer of Genome Science Pp box GenBank Files Pp box Pairwise Sequence Alignment Pp box Searching Sequence Databases Using BLAST Pp : Functional Annotation and Gene Family Clusters P. 114 box Phylogenetics

C E N T R. Introduction to bioinformatics 2007 E B I O I N F O R M A T I C S V U F O R I N T. Lecture 5 G R A T I V. Pair-wise Sequence Alignment

C E N T R. Introduction to bioinformatics 2007 E B I O I N F O R M A T I C S V U F O R I N T. Lecture 5 G R A T I V. Pair-wise Sequence Alignment C E N T R E F O R I N T E G R A T I V E B I O I N F O R M A T I C S V U Introduction to bioinformatics 2007 Lecture 5 Pair-wise Sequence Alignment Bioinformatics Nothing in Biology makes sense except in

More information

Bioinformatics (GLOBEX, Summer 2015) Pairwise sequence alignment

Bioinformatics (GLOBEX, Summer 2015) Pairwise sequence alignment Bioinformatics (GLOBEX, Summer 2015) Pairwise sequence alignment Substitution score matrices, PAM, BLOSUM Needleman-Wunsch algorithm (Global) Smith-Waterman algorithm (Local) BLAST (local, heuristic) E-value

More information

Algorithms in Bioinformatics FOUR Pairwise Sequence Alignment. Pairwise Sequence Alignment. Convention: DNA Sequences 5. Sequence Alignment

Algorithms in Bioinformatics FOUR Pairwise Sequence Alignment. Pairwise Sequence Alignment. Convention: DNA Sequences 5. Sequence Alignment Algorithms in Bioinformatics FOUR Sami Khuri Department of Computer Science San José State University Pairwise Sequence Alignment Homology Similarity Global string alignment Local string alignment Dot

More information

Tools and Algorithms in Bioinformatics

Tools and Algorithms in Bioinformatics Tools and Algorithms in Bioinformatics GCBA815, Fall 2015 Week-4 BLAST Algorithm Continued Multiple Sequence Alignment Babu Guda, Ph.D. Department of Genetics, Cell Biology & Anatomy Bioinformatics and

More information

Grundlagen der Bioinformatik, SS 08, D. Huson, May 2,

Grundlagen der Bioinformatik, SS 08, D. Huson, May 2, Grundlagen der Bioinformatik, SS 08, D. Huson, May 2, 2008 39 5 Blast This lecture is based on the following, which are all recommended reading: R. Merkl, S. Waack: Bioinformatik Interaktiv. Chapter 11.4-11.7

More information

Bioinformatics and BLAST

Bioinformatics and BLAST Bioinformatics and BLAST Overview Recap of last time Similarity discussion Algorithms: Needleman-Wunsch Smith-Waterman BLAST Implementation issues and current research Recap from Last Time Genome consists

More information

Computational Biology

Computational Biology Computational Biology Lecture 6 31 October 2004 1 Overview Scoring matrices (Thanks to Shannon McWeeney) BLAST algorithm Start sequence alignment 2 1 What is a homologous sequence? A homologous sequence,

More information

Introduction to Bioinformatics

Introduction to Bioinformatics Introduction to Bioinformatics Jianlin Cheng, PhD Department of Computer Science Informatics Institute 2011 Topics Introduction Biological Sequence Alignment and Database Search Analysis of gene expression

More information

Basic Local Alignment Search Tool

Basic Local Alignment Search Tool Basic Local Alignment Search Tool Alignments used to uncover homologies between sequences combined with phylogenetic studies o can determine orthologous and paralogous relationships Local Alignment uses

More information

CISC 889 Bioinformatics (Spring 2004) Sequence pairwise alignment (I)

CISC 889 Bioinformatics (Spring 2004) Sequence pairwise alignment (I) CISC 889 Bioinformatics (Spring 2004) Sequence pairwise alignment (I) Contents Alignment algorithms Needleman-Wunsch (global alignment) Smith-Waterman (local alignment) Heuristic algorithms FASTA BLAST

More information

Tools and Algorithms in Bioinformatics

Tools and Algorithms in Bioinformatics Tools and Algorithms in Bioinformatics GCBA815, Fall 2013 Week3: Blast Algorithm, theory and practice Babu Guda, Ph.D. Department of Genetics, Cell Biology & Anatomy Bioinformatics and Systems Biology

More information

BLAST. Varieties of BLAST

BLAST. Varieties of BLAST BLAST Basic Local Alignment Search Tool (1990) Altschul, Gish, Miller, Myers, & Lipman Uses short-cuts or heuristics to improve search speed Like speed-reading, does not examine every nucleotide of database

More information

Algorithms in Bioinformatics

Algorithms in Bioinformatics Algorithms in Bioinformatics Sami Khuri Department of omputer Science San José State University San José, alifornia, USA khuri@cs.sjsu.edu www.cs.sjsu.edu/faculty/khuri Pairwise Sequence Alignment Homology

More information

3. SEQUENCE ANALYSIS BIOINFORMATICS COURSE MTAT

3. SEQUENCE ANALYSIS BIOINFORMATICS COURSE MTAT 3. SEQUENCE ANALYSIS BIOINFORMATICS COURSE MTAT.03.239 25.09.2012 SEQUENCE ANALYSIS IS IMPORTANT FOR... Prediction of function Gene finding the process of identifying the regions of genomic DNA that encode

More information

Sara C. Madeira. Universidade da Beira Interior. (Thanks to Ana Teresa Freitas, IST for useful resources on this subject)

Sara C. Madeira. Universidade da Beira Interior. (Thanks to Ana Teresa Freitas, IST for useful resources on this subject) Bioinformática Sequence Alignment Pairwise Sequence Alignment Universidade da Beira Interior (Thanks to Ana Teresa Freitas, IST for useful resources on this subject) 1 16/3/29 & 23/3/29 27/4/29 Outline

More information

Week 10: Homology Modelling (II) - HHpred

Week 10: Homology Modelling (II) - HHpred Week 10: Homology Modelling (II) - HHpred Course: Tools for Structural Biology Fabian Glaser BKU - Technion 1 2 Identify and align related structures by sequence methods is not an easy task All comparative

More information

EECS730: Introduction to Bioinformatics

EECS730: Introduction to Bioinformatics EECS730: Introduction to Bioinformatics Lecture 07: profile Hidden Markov Model http://bibiserv.techfak.uni-bielefeld.de/sadr2/databasesearch/hmmer/profilehmm.gif Slides adapted from Dr. Shaojie Zhang

More information

Single alignment: Substitution Matrix. 16 march 2017

Single alignment: Substitution Matrix. 16 march 2017 Single alignment: Substitution Matrix 16 march 2017 BLOSUM Matrix BLOSUM Matrix [2] (Blocks Amino Acid Substitution Matrices ) It is based on the amino acids substitutions observed in ~2000 conserved block

More information

EECS730: Introduction to Bioinformatics

EECS730: Introduction to Bioinformatics EECS730: Introduction to Bioinformatics Lecture 05: Index-based alignment algorithms Slides adapted from Dr. Shaojie Zhang (University of Central Florida) Real applications of alignment Database search

More information

In-Depth Assessment of Local Sequence Alignment

In-Depth Assessment of Local Sequence Alignment 2012 International Conference on Environment Science and Engieering IPCBEE vol.3 2(2012) (2012)IACSIT Press, Singapoore In-Depth Assessment of Local Sequence Alignment Atoosa Ghahremani and Mahmood A.

More information

Chapter 5. Proteomics and the analysis of protein sequence Ⅱ

Chapter 5. Proteomics and the analysis of protein sequence Ⅱ Proteomics Chapter 5. Proteomics and the analysis of protein sequence Ⅱ 1 Pairwise similarity searching (1) Figure 5.5: manual alignment One of the amino acids in the top sequence has no equivalent and

More information

Bioinformatics for Biologists

Bioinformatics for Biologists Bioinformatics for Biologists Sequence Analysis: Part I. Pairwise alignment and database searching Fran Lewitter, Ph.D. Head, Biocomputing Whitehead Institute Bioinformatics Definitions The use of computational

More information

BLAST Database Searching. BME 110: CompBio Tools Todd Lowe April 8, 2010

BLAST Database Searching. BME 110: CompBio Tools Todd Lowe April 8, 2010 BLAST Database Searching BME 110: CompBio Tools Todd Lowe April 8, 2010 Admin Reading: Read chapter 7, and the NCBI Blast Guide and tutorial http://www.ncbi.nlm.nih.gov/blast/why.shtml Read Chapter 8 for

More information

Sequence Alignment: A General Overview. COMP Fall 2010 Luay Nakhleh, Rice University

Sequence Alignment: A General Overview. COMP Fall 2010 Luay Nakhleh, Rice University Sequence Alignment: A General Overview COMP 571 - Fall 2010 Luay Nakhleh, Rice University Life through Evolution All living organisms are related to each other through evolution This means: any pair of

More information

Alignment & BLAST. By: Hadi Mozafari KUMS

Alignment & BLAST. By: Hadi Mozafari KUMS Alignment & BLAST By: Hadi Mozafari KUMS SIMILARITY - ALIGNMENT Comparison of primary DNA or protein sequences to other primary or secondary sequences Expecting that the function of the similar sequence

More information

THEORY. Based on sequence Length According to the length of sequence being compared it is of following two types

THEORY. Based on sequence Length According to the length of sequence being compared it is of following two types Exp 11- THEORY Sequence Alignment is a process of aligning two sequences to achieve maximum levels of identity between them. This help to derive functional, structural and evolutionary relationships between

More information

Heuristic Alignment and Searching

Heuristic Alignment and Searching 3/28/2012 Types of alignments Global Alignment Each letter of each sequence is aligned to a letter or a gap (e.g., Needleman-Wunsch). Local Alignment An optimal pair of subsequences is taken from the two

More information

Large-Scale Genomic Surveys

Large-Scale Genomic Surveys Bioinformatics Subtopics Fold Recognition Secondary Structure Prediction Docking & Drug Design Protein Geometry Protein Flexibility Homology Modeling Sequence Alignment Structure Classification Gene Prediction

More information

Sequence Alignment Techniques and Their Uses

Sequence Alignment Techniques and Their Uses Sequence Alignment Techniques and Their Uses Sarah Fiorentino Since rapid sequencing technology and whole genomes sequencing, the amount of sequence information has grown exponentially. With all of this

More information

Sequence Alignments. Dynamic programming approaches, scoring, and significance. Lucy Skrabanek ICB, WMC January 31, 2013

Sequence Alignments. Dynamic programming approaches, scoring, and significance. Lucy Skrabanek ICB, WMC January 31, 2013 Sequence Alignments Dynamic programming approaches, scoring, and significance Lucy Skrabanek ICB, WMC January 31, 213 Sequence alignment Compare two (or more) sequences to: Find regions of conservation

More information

Homology Modeling. Roberto Lins EPFL - summer semester 2005

Homology Modeling. Roberto Lins EPFL - summer semester 2005 Homology Modeling Roberto Lins EPFL - summer semester 2005 Disclaimer: course material is mainly taken from: P.E. Bourne & H Weissig, Structural Bioinformatics; C.A. Orengo, D.T. Jones & J.M. Thornton,

More information

Module: Sequence Alignment Theory and Applications Session: Introduction to Searching and Sequence Alignment

Module: Sequence Alignment Theory and Applications Session: Introduction to Searching and Sequence Alignment Module: Sequence Alignment Theory and Applications Session: Introduction to Searching and Sequence Alignment Introduction to Bioinformatics online course : IBT Jonathan Kayondo Learning Objectives Understand

More information

Practical considerations of working with sequencing data

Practical considerations of working with sequencing data Practical considerations of working with sequencing data File Types Fastq ->aligner -> reference(genome) coordinates Coordinate files SAM/BAM most complete, contains all of the info in fastq and more!

More information

CONCEPT OF SEQUENCE COMPARISON. Natapol Pornputtapong 18 January 2018

CONCEPT OF SEQUENCE COMPARISON. Natapol Pornputtapong 18 January 2018 CONCEPT OF SEQUENCE COMPARISON Natapol Pornputtapong 18 January 2018 SEQUENCE ANALYSIS - A ROSETTA STONE OF LIFE Sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of

More information

Sequence Database Search Techniques I: Blast and PatternHunter tools

Sequence Database Search Techniques I: Blast and PatternHunter tools Sequence Database Search Techniques I: Blast and PatternHunter tools Zhang Louxin National University of Singapore Outline. Database search 2. BLAST (and filtration technique) 3. PatternHunter (empowered

More information

Pairwise & Multiple sequence alignments

Pairwise & Multiple sequence alignments Pairwise & Multiple sequence alignments Urmila Kulkarni-Kale Bioinformatics Centre 411 007 urmila@bioinfo.ernet.in Basis for Sequence comparison Theory of evolution: gene sequences have evolved/derived

More information

An Introduction to Sequence Similarity ( Homology ) Searching

An Introduction to Sequence Similarity ( Homology ) Searching An Introduction to Sequence Similarity ( Homology ) Searching Gary D. Stormo 1 UNIT 3.1 1 Washington University, School of Medicine, St. Louis, Missouri ABSTRACT Homologous sequences usually have the same,

More information

BLAST: Target frequencies and information content Dannie Durand

BLAST: Target frequencies and information content Dannie Durand Computational Genomics and Molecular Biology, Fall 2016 1 BLAST: Target frequencies and information content Dannie Durand BLAST has two components: a fast heuristic for searching for similar sequences

More information

Pairwise Alignment. Guan-Shieng Huang. Dept. of CSIE, NCNU. Pairwise Alignment p.1/55

Pairwise Alignment. Guan-Shieng Huang. Dept. of CSIE, NCNU. Pairwise Alignment p.1/55 Pairwise Alignment Guan-Shieng Huang shieng@ncnu.edu.tw Dept. of CSIE, NCNU Pairwise Alignment p.1/55 Approach 1. Problem definition 2. Computational method (algorithms) 3. Complexity and performance Pairwise

More information

Biochemistry 324 Bioinformatics. Pairwise sequence alignment

Biochemistry 324 Bioinformatics. Pairwise sequence alignment Biochemistry 324 Bioinformatics Pairwise sequence alignment How do we compare genes/proteins? When we have sequenced a genome, we try and identify the function of unknown genes by finding a similar gene

More information

Fundamentals of database searching

Fundamentals of database searching Fundamentals of database searching Aligning novel sequences with previously characterized genes or proteins provides important insights into their common attributes and evolutionary origins. The principles

More information

Sequence analysis and comparison

Sequence analysis and comparison The aim with sequence identification: Sequence analysis and comparison Marjolein Thunnissen Lund September 2012 Is there any known protein sequence that is homologous to mine? Are there any other species

More information

Statistical Machine Learning Methods for Bioinformatics II. Hidden Markov Model for Biological Sequences

Statistical Machine Learning Methods for Bioinformatics II. Hidden Markov Model for Biological Sequences Statistical Machine Learning Methods for Bioinformatics II. Hidden Markov Model for Biological Sequences Jianlin Cheng, PhD Department of Computer Science University of Missouri 2008 Free for Academic

More information

Tiffany Samaroo MB&B 452a December 8, Take Home Final. Topic 1

Tiffany Samaroo MB&B 452a December 8, Take Home Final. Topic 1 Tiffany Samaroo MB&B 452a December 8, 2003 Take Home Final Topic 1 Prior to 1970, protein and DNA sequence alignment was limited to visual comparison. This was a very tedious process; even proteins with

More information

Similarity or Identity? When are molecules similar?

Similarity or Identity? When are molecules similar? Similarity or Identity? When are molecules similar? Mapping Identity A -> A T -> T G -> G C -> C or Leu -> Leu Pro -> Pro Arg -> Arg Phe -> Phe etc If we map similarity using identity, how similar are

More information

Local Alignment: Smith-Waterman algorithm

Local Alignment: Smith-Waterman algorithm Local Alignment: Smith-Waterman algorithm Example: a shared common domain of two protein sequences; extended sections of genomic DNA sequence. Sensitive to detect similarity in highly diverged sequences.

More information

Moreover, the circular logic

Moreover, the circular logic Moreover, the circular logic How do we know what is the right distance without a good alignment? And how do we construct a good alignment without knowing what substitutions were made previously? ATGCGT--GCAAGT

More information

Bioinformatics. Scoring Matrices. David Gilbert Bioinformatics Research Centre

Bioinformatics. Scoring Matrices. David Gilbert Bioinformatics Research Centre Bioinformatics Scoring Matrices David Gilbert Bioinformatics Research Centre www.brc.dcs.gla.ac.uk Department of Computing Science, University of Glasgow Learning Objectives To explain the requirement

More information

Sequence analysis and Genomics

Sequence analysis and Genomics Sequence analysis and Genomics October 12 th November 23 rd 2 PM 5 PM Prof. Peter Stadler Dr. Katja Nowick Katja: group leader TFome and Transcriptome Evolution Bioinformatics group Paul-Flechsig-Institute

More information

Lecture 4: Evolutionary Models and Substitution Matrices (PAM and BLOSUM)

Lecture 4: Evolutionary Models and Substitution Matrices (PAM and BLOSUM) Bioinformatics II Probability and Statistics Universität Zürich and ETH Zürich Spring Semester 2009 Lecture 4: Evolutionary Models and Substitution Matrices (PAM and BLOSUM) Dr Fraser Daly adapted from

More information

Sequence Analysis 17: lecture 5. Substitution matrices Multiple sequence alignment

Sequence Analysis 17: lecture 5. Substitution matrices Multiple sequence alignment Sequence Analysis 17: lecture 5 Substitution matrices Multiple sequence alignment Substitution matrices Used to score aligned positions, usually of amino acids. Expressed as the log-likelihood ratio of

More information

InDel 3-5. InDel 8-9. InDel 3-5. InDel 8-9. InDel InDel 8-9

InDel 3-5. InDel 8-9. InDel 3-5. InDel 8-9. InDel InDel 8-9 Lecture 5 Alignment I. Introduction. For sequence data, the process of generating an alignment establishes positional homologies; that is, alignment provides the identification of homologous phylogenetic

More information

CSE : Computational Issues in Molecular Biology. Lecture 6. Spring 2004

CSE : Computational Issues in Molecular Biology. Lecture 6. Spring 2004 CSE 397-497: Computational Issues in Molecular Biology Lecture 6 Spring 2004-1 - Topics for today Based on premise that algorithms we've studied are too slow: Faster method for global comparison when sequences

More information

Motivating the need for optimal sequence alignments...

Motivating the need for optimal sequence alignments... 1 Motivating the need for optimal sequence alignments... 2 3 Note that this actually combines two objectives of optimal sequence alignments: (i) use the score of the alignment o infer homology; (ii) use

More information

Collected Works of Charles Dickens

Collected Works of Charles Dickens Collected Works of Charles Dickens A Random Dickens Quote If there were no bad people, there would be no good lawyers. Original Sentence It was a dark and stormy night; the night was dark except at sunny

More information

Homology Modeling (Comparative Structure Modeling) GBCB 5874: Problem Solving in GBCB

Homology Modeling (Comparative Structure Modeling) GBCB 5874: Problem Solving in GBCB Homology Modeling (Comparative Structure Modeling) Aims of Structural Genomics High-throughput 3D structure determination and analysis To determine or predict the 3D structures of all the proteins encoded

More information

Lecture 2, 5/12/2001: Local alignment the Smith-Waterman algorithm. Alignment scoring schemes and theory: substitution matrices and gap models

Lecture 2, 5/12/2001: Local alignment the Smith-Waterman algorithm. Alignment scoring schemes and theory: substitution matrices and gap models Lecture 2, 5/12/2001: Local alignment the Smith-Waterman algorithm Alignment scoring schemes and theory: substitution matrices and gap models 1 Local sequence alignments Local sequence alignments are necessary

More information

Sequence Comparison. mouse human

Sequence Comparison. mouse human Sequence Comparison Sequence Comparison mouse human Why Compare Sequences? The first fact of biological sequence analysis In biomolecular sequences (DNA, RNA, or amino acid sequences), high sequence similarity

More information

Similarity searching summary (2)

Similarity searching summary (2) Similarity searching / sequence alignment summary Biol4230 Thurs, February 22, 2016 Bill Pearson wrp@virginia.edu 4-2818 Pinn 6-057 What have we covered? Homology excess similiarity but no excess similarity

More information

Pairwise sequence alignment

Pairwise sequence alignment Department of Evolutionary Biology Example Alignment between very similar human alpha- and beta globins: GSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKL G+ +VK+HGKKV A+++++AH+D++ +++++LS+LH KL GNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKL

More information

1.5 Sequence alignment

1.5 Sequence alignment 1.5 Sequence alignment The dramatic increase in the number of sequenced genomes and proteomes has lead to development of various bioinformatic methods and algorithms for extracting information (data mining)

More information

Quantifying sequence similarity

Quantifying sequence similarity Quantifying sequence similarity Bas E. Dutilh Systems Biology: Bioinformatic Data Analysis Utrecht University, February 16 th 2016 After this lecture, you can define homology, similarity, and identity

More information

First generation sequencing and pairwise alignment (High-tech, not high throughput) Analysis of Biological Sequences

First generation sequencing and pairwise alignment (High-tech, not high throughput) Analysis of Biological Sequences First generation sequencing and pairwise alignment (High-tech, not high throughput) Analysis of Biological Sequences 140.638 where do sequences come from? DNA is not hard to extract (getting DNA from a

More information

Syllabus of BIOINF 528 (2017 Fall, Bioinformatics Program)

Syllabus of BIOINF 528 (2017 Fall, Bioinformatics Program) Syllabus of BIOINF 528 (2017 Fall, Bioinformatics Program) Course Name: Structural Bioinformatics Course Description: Instructor: This course introduces fundamental concepts and methods for structural

More information

A profile-based protein sequence alignment algorithm for a domain clustering database

A profile-based protein sequence alignment algorithm for a domain clustering database A profile-based protein sequence alignment algorithm for a domain clustering database Lin Xu,2 Fa Zhang and Zhiyong Liu 3, Key Laboratory of Computer System and architecture, the Institute of Computing

More information

Bioinformatics for Computer Scientists (Part 2 Sequence Alignment) Sepp Hochreiter

Bioinformatics for Computer Scientists (Part 2 Sequence Alignment) Sepp Hochreiter Bioinformatics for Computer Scientists (Part 2 Sequence Alignment) Institute of Bioinformatics Johannes Kepler University, Linz, Austria Sequence Alignment 2. Sequence Alignment Sequence Alignment 2.1

More information

Statistical Machine Learning Methods for Biomedical Informatics II. Hidden Markov Model for Biological Sequences

Statistical Machine Learning Methods for Biomedical Informatics II. Hidden Markov Model for Biological Sequences Statistical Machine Learning Methods for Biomedical Informatics II. Hidden Markov Model for Biological Sequences Jianlin Cheng, PhD William and Nancy Thompson Missouri Distinguished Professor Department

More information

HMMs and biological sequence analysis

HMMs and biological sequence analysis HMMs and biological sequence analysis Hidden Markov Model A Markov chain is a sequence of random variables X 1, X 2, X 3,... That has the property that the value of the current state depends only on the

More information

Sequence Analysis, '18 -- lecture 9. Families and superfamilies. Sequence weights. Profiles. Logos. Building a representative model for a gene.

Sequence Analysis, '18 -- lecture 9. Families and superfamilies. Sequence weights. Profiles. Logos. Building a representative model for a gene. Sequence Analysis, '18 -- lecture 9 Families and superfamilies. Sequence weights. Profiles. Logos. Building a representative model for a gene. How can I represent thousands of homolog sequences in a compact

More information

Genome Annotation. Qi Sun Bioinformatics Facility Cornell University

Genome Annotation. Qi Sun Bioinformatics Facility Cornell University Genome Annotation Qi Sun Bioinformatics Facility Cornell University Some basic bioinformatics tools BLAST PSI-BLAST - Position-Specific Scoring Matrix HMM - Hidden Markov Model NCBI BLAST How does BLAST

More information

Christian Sigrist. November 14 Protein Bioinformatics: Sequence-Structure-Function 2018 Basel

Christian Sigrist. November 14 Protein Bioinformatics: Sequence-Structure-Function 2018 Basel Christian Sigrist General Definition on Conserved Regions Conserved regions in proteins can be classified into 5 different groups: Domains: specific combination of secondary structures organized into a

More information

Sequence Alignment: Scoring Schemes. COMP 571 Luay Nakhleh, Rice University

Sequence Alignment: Scoring Schemes. COMP 571 Luay Nakhleh, Rice University Sequence Alignment: Scoring Schemes COMP 571 Luay Nakhleh, Rice University Scoring Schemes Recall that an alignment score is aimed at providing a scale to measure the degree of similarity (or difference)

More information

Scoring Matrices. Shifra Ben-Dor Irit Orr

Scoring Matrices. Shifra Ben-Dor Irit Orr Scoring Matrices Shifra Ben-Dor Irit Orr Scoring matrices Sequence alignment and database searching programs compare sequences to each other as a series of characters. All algorithms (programs) for comparison

More information

Sequence comparison: Score matrices

Sequence comparison: Score matrices Sequence comparison: Score matrices http://facultywashingtonedu/jht/gs559_2013/ Genome 559: Introduction to Statistical and omputational Genomics Prof James H Thomas FYI - informal inductive proof of best

More information

Sequence Alignment (chapter 6)

Sequence Alignment (chapter 6) Sequence lignment (chapter 6) he biological problem lobal alignment Local alignment Multiple alignment Introduction to bioinformatics, utumn 6 Background: comparative genomics Basic question in biology:

More information

Sequence and Structure Alignment Z. Luthey-Schulten, UIUC Pittsburgh, 2006 VMD 1.8.5

Sequence and Structure Alignment Z. Luthey-Schulten, UIUC Pittsburgh, 2006 VMD 1.8.5 Sequence and Structure Alignment Z. Luthey-Schulten, UIUC Pittsburgh, 2006 VMD 1.8.5 Why Look at More Than One Sequence? 1. Multiple Sequence Alignment shows patterns of conservation 2. What and how many

More information

08/21/2017 BLAST. Multiple Sequence Alignments: Clustal Omega

08/21/2017 BLAST. Multiple Sequence Alignments: Clustal Omega BLAST Multiple Sequence Alignments: Clustal Omega What does basic BLAST do (e.g. what is input sequence and how does BLAST look for matches?) Susan Parrish McDaniel College Multiple Sequence Alignments

More information

Whole Genome Alignments and Synteny Maps

Whole Genome Alignments and Synteny Maps Whole Genome Alignments and Synteny Maps IINTRODUCTION It was not until closely related organism genomes have been sequenced that people start to think about aligning genomes and chromosomes instead of

More information

Sequence comparison: Score matrices. Genome 559: Introduction to Statistical and Computational Genomics Prof. James H. Thomas

Sequence comparison: Score matrices. Genome 559: Introduction to Statistical and Computational Genomics Prof. James H. Thomas Sequence comparison: Score matrices Genome 559: Introduction to Statistical and omputational Genomics Prof James H Thomas FYI - informal inductive proof of best alignment path onsider the last step in

More information

Local Alignment Statistics

Local Alignment Statistics Local Alignment Statistics Stephen Altschul National Center for Biotechnology Information National Library of Medicine National Institutes of Health Bethesda, MD Central Issues in Biological Sequence Comparison

More information

Chapter 7: Rapid alignment methods: FASTA and BLAST

Chapter 7: Rapid alignment methods: FASTA and BLAST Chapter 7: Rapid alignment methods: FASTA and BLAST The biological problem Search strategies FASTA BLAST Introduction to bioinformatics, Autumn 2007 117 BLAST: Basic Local Alignment Search Tool BLAST (Altschul

More information

bioinformatics 1 -- lecture 7

bioinformatics 1 -- lecture 7 bioinformatics 1 -- lecture 7 Probability and conditional probability Random sequences and significance (real sequences are not random) Erdos & Renyi: theoretical basis for the significance of an alignment

More information

Sequence Analysis '17 -- lecture 7

Sequence Analysis '17 -- lecture 7 Sequence Analysis '17 -- lecture 7 Significance E-values How significant is that? Please give me a number for......how likely the data would not have been the result of chance,......as opposed to......a

More information

Copyright 2000 N. AYDIN. All rights reserved. 1

Copyright 2000 N. AYDIN. All rights reserved. 1 Introduction to Bioinformatics Prof. Dr. Nizamettin AYDIN naydin@yildiz.edu.tr Multiple Sequence Alignment Outline Multiple sequence alignment introduction to msa methods of msa progressive global alignment

More information

Sequence alignment methods. Pairwise alignment. The universe of biological sequence analysis

Sequence alignment methods. Pairwise alignment. The universe of biological sequence analysis he universe of biological sequence analysis Word/pattern recognition- Identification of restriction enzyme cleavage sites Sequence alignment methods PstI he universe of biological sequence analysis - prediction

More information

Pairwise Sequence Alignment

Pairwise Sequence Alignment Introduction to Bioinformatics Pairwise Sequence Alignment Prof. Dr. Nizamettin AYDIN naydin@yildiz.edu.tr Outline Introduction to sequence alignment pair wise sequence alignment The Dot Matrix Scoring

More information

Pairwise alignment. 2.1 Introduction GSAQVKGHGKKVADALTNAVAHVDDMPNALSALSD----LHAHKL

Pairwise alignment. 2.1 Introduction GSAQVKGHGKKVADALTNAVAHVDDMPNALSALSD----LHAHKL 2 Pairwise alignment 2.1 Introduction The most basic sequence analysis task is to ask if two sequences are related. This is usually done by first aligning the sequences (or parts of them) and then deciding

More information

Sequence comparison: Score matrices. Genome 559: Introduction to Statistical and Computational Genomics Prof. James H. Thomas

Sequence comparison: Score matrices. Genome 559: Introduction to Statistical and Computational Genomics Prof. James H. Thomas Sequence comparison: Score matrices Genome 559: Introduction to Statistical and omputational Genomics Prof James H Thomas Informal inductive proof of best alignment path onsider the last step in the best

More information

Introduction to protein alignments

Introduction to protein alignments Introduction to protein alignments Comparative Analysis of Proteins Experimental evidence from one or more proteins can be used to infer function of related protein(s). Gene A Gene X Protein A compare

More information

Exercise 5. Sequence Profiles & BLAST

Exercise 5. Sequence Profiles & BLAST Exercise 5 Sequence Profiles & BLAST 1 Substitution Matrix (BLOSUM62) Likelihood to substitute one amino acid with another Figure taken from https://en.wikipedia.org/wiki/blosum 2 Substitution Matrix (BLOSUM62)

More information

Introduction to sequence alignment. Local alignment the Smith-Waterman algorithm

Introduction to sequence alignment. Local alignment the Smith-Waterman algorithm Lecture 2, 12/3/2003: Introduction to sequence alignment The Needleman-Wunsch algorithm for global sequence alignment: description and properties Local alignment the Smith-Waterman algorithm 1 Computational

More information

Multiple sequence alignment

Multiple sequence alignment Multiple sequence alignment Multiple sequence alignment: today s goals to define what a multiple sequence alignment is and how it is generated; to describe profile HMMs to introduce databases of multiple

More information

Orthology Part I concepts and implications Toni Gabaldón Centre for Genomic Regulation (CRG), Barcelona

Orthology Part I concepts and implications Toni Gabaldón Centre for Genomic Regulation (CRG), Barcelona Orthology Part I concepts and implications Toni Gabaldón Centre for Genomic Regulation (CRG), Barcelona Toni Gabaldón Contact: tgabaldon@crg.es Group website: http://gabaldonlab.crg.es Science blog: http://treevolution.blogspot.com

More information

An Introduction to Bioinformatics Algorithms Hidden Markov Models

An Introduction to Bioinformatics Algorithms   Hidden Markov Models Hidden Markov Models Outline 1. CG-Islands 2. The Fair Bet Casino 3. Hidden Markov Model 4. Decoding Algorithm 5. Forward-Backward Algorithm 6. Profile HMMs 7. HMM Parameter Estimation 8. Viterbi Training

More information

DNA and protein databases. EMBL/GenBank/DDBJ database of nucleic acids

DNA and protein databases. EMBL/GenBank/DDBJ database of nucleic acids Database searches 1 DNA and protein databases EMBL/GenBank/DDBJ database of nucleic acids 2 DNA and protein databases EMBL/GenBank/DDBJ database of nucleic acids (cntd) 3 DNA and protein databases SWISS-PROT

More information

Comparing whole genomes

Comparing whole genomes BioNumerics Tutorial: Comparing whole genomes 1 Aim The Chromosome Comparison window in BioNumerics has been designed for large-scale comparison of sequences of unlimited length. In this tutorial you will

More information

Protein function prediction based on sequence analysis

Protein function prediction based on sequence analysis Performing sequence searches Post-Blast analysis, Using profiles and pattern-matching Protein function prediction based on sequence analysis Slides from a lecture on MOL204 - Applied Bioinformatics 18-Oct-2005

More information

Sequence Analysis and Databases 2: Sequences and Multiple Alignments

Sequence Analysis and Databases 2: Sequences and Multiple Alignments 1 Sequence Analysis and Databases 2: Sequences and Multiple Alignments Jose María González-Izarzugaza Martínez CNIO Spanish National Cancer Research Centre (jmgonzalez@cnio.es) 2 Sequence Comparisons:

More information

CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools. Giri Narasimhan

CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools. Giri Narasimhan CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools Giri Narasimhan ECS 254; Phone: x3748 giri@cis.fiu.edu www.cis.fiu.edu/~giri/teach/bioinfs15.html Describing & Modeling Patterns

More information

Bioinformatics Exercises

Bioinformatics Exercises Bioinformatics Exercises AP Biology Teachers Workshop Susan Cates, Ph.D. Evolution of Species Phylogenetic Trees show the relatedness of organisms Common Ancestor (Root of the tree) 1 Rooted vs. Unrooted

More information