Interactive tools and programming environments

for sequence analysis

Bernardo Barbiellini

Northeastern University

TATACATAAAGACCCAAATGGAACTGTTCTAGATGATACACTAGCATTAAGAGAAAAATTCGAAGAATCAGTCGATAAATACAAACTTCATTTTACTGGATTAATCGCTGACAAAATTGCAAAAGAAAAACTGAATACTTACGTCCTCACTTATAAAAAAGCAGACGAAGCTATGCCTGCAGACGAAGCTATGCCAACTGATGTACCTAGTACTTCTGTTACTGGATCAACAATGGCAAAC………………….

Overview

• Matlab and Darwin – bioinformatics tools

• Dotplot and Statistical signifance of alignments

• Scoring Matrices from Evolution Model

• Evolutionary Distances and Phylogenetic Trees.

• Unified approach for the sequence alignment and structure prediction

Matlab toolbox and Darwin• Computer language appropriate for bioinformatics

• A workbench to automate repetitive tasks

• Based on Linear Algebra & Statistics

• Matlab toolbox developed by Mathworks

• Darwin developed by Gaston Gonnet (ETHZ)

Extra features

• Loading of and retrieval in sequence databases• Fast searching for sequence fragments• Sequence alignment• Generation of random sequences, distributions and

mutations• Creation of Phylogenetic trees• Plotting functions - matrix and vector arithmetic• I/O comunicate with other programs

Calling Bioperl functions in MATLAB Documentation by Brian Madsen (NU and coop at the Mathworks)

>> help perl

PERL calls perl script using appropriate operating system PERL(PERLFILE) calls perl script specified by the file PERLFILE using appropriate perl executable.

PERL(PERLFILE,ARG1,ARG2,...) passes the arguments ARG1,ARG2,... to the perl script file PERLFILE, and calls it by using appropriate perl executable. RESULT=PERL(...) outputs the result of attempted perl call.

Visual Tool: Dotplot (1)

Pairwise sequence comparison

Visual Tool: Dotplot (2)

Filtered Image

The best alignment is achieved with dynamic programming . A score is obtained

Quantitative Tools To CheckStatistical Significance

Simulation with random sequences

Score in bits

extreme value distribution.

PAM Evolution Model

PAM means Accepted Point Mutation

The score of a paiwise alignment is obtained by using a scoring matrix.

We need a model to build scoring matrices.

This model is based on evolution in order to calculate evolution distances between species.

Step1: Order of the Amino-Acids

Step 2: Mutation Matrices

Markov Model pamX=(pam1)^X Stochastic matrices

Step 3: Distribution of Amino Acids

Eigenvector of the mutation matrix (eigenvalue 1)

Step 4: Evolutionary time vs. sequences differences

Step 5: Scoring Matrix

The Dayhoff scoring matrix is symmetric

Tree Construction 1:Evolutionary distance calculations

Maximum Likelihood

Tree Construction 2:Table of distances

PAM Spinach Rice Mosquito Monkey Human

Spinach 0.0 84.9 105.6 90.8 86.3

Rice 84.9 0.0 117.8 122.4 122.6

Mosquito 105.6 117.8 0.0 84.7 80.8

Monkey 90.8 122.4 84.7 0.0 3.3

Human 86.3 122.6 80.8 3.3 0.0

Tree Construction 3:Neighbor joining algorithm

Unified approach for the sequence alignment and structure prediction

Protein

Protein

Protein

Optimizationwith DynamicProgramming approach

Needleman-Wunsch Algorithm

orSmith-Waterman

Algorithm

Query

Subject Protein (letter of amino acids)

Scoring Matrix

Log (Aij/pi)

Penalties Gaps

Protein

Structure

Viterbi AlgorithmHMM

Protein

Structure (, , coil)

Log (P(im)/pi)

Transition from structure to another

Conclusions

• The highly efficient dynamic programming algorithms, used in this integrated environment, are particularly suitable for the high performance computers.

• Trees constructed using optimal PAM distances are better than the routinesingle distance scores obtained using a single scoring matrix.

• The unified approach for the sequence alignment and structure prediction provides a powerful formalism for biologists.

ASCC Northeastern University

Northeastern University (NU)/Hewlett-Packard (HP) Company Collaborative Research Program on Bioinformatics

Bernardo Barbiellini, Assoc. Director, ASCC

Arun Bansil, Professor of Physics & Director ASCC.

Bill Detrich, Prof. Biochem. & Marine Biology, Director Bioinformatics M. S.

Kostia Bergman, Prof. Biology

Mike Malioutov, Stone Professor of Applied Statistics

Mary Jo Ondrechen, Professor of Chemistry

Nagarajan Sankrithi, graduate student NU

Imtiaz Khan, graduate student NU

Alper Uzun, graduate student NU

Larry Weissman, staff HP/Compaq

Barry Latham, staff HP/Compaq

Bob Morgan, staff HP/Compaq

Other Bioinformatics activities at ASCC

• BIO3580: DNA and Protein Sequence Analysis (2001, 2002)

• MATLAB BIOINFORMATICS TOOL presentation (Robert Henson)

• Summer Institute of Mathematical Studies on Bioinformatics (2002) (Professor Mike Malioutov)

• Student projects proposed by Dr. Matteo Pellegrini, (Proteinpathways/UCLA).