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# Correlogram Method for comparing Bio-Sequences - PowerPoint PPT Presentation

Correlogram Method for comparing Bio-Sequences . Gandhali Samant , M.S. Computer Science. Committee Dr. Debasis Mitra, PhD Dr. William Shoaff, PhD Dr. Alan Leonard, PhD. What is Sequence Comparison.

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### Correlogram Method for comparing Bio-Sequences

• Gandhali Samant , M.S. Computer Science

Committee

Dr. Debasis Mitra, PhD

Dr. William Shoaff, PhD

Dr. Alan Leonard, PhD

• Sequence Comparison – One of the most important primitive operations in computational biology.

• Finding resemblance or similarity between sequences

• Basis for many other more complex manipulations.

• Used for database search, phylogeny development, clustering etc.

• Two important notions are -

• Similarity – How similar are the two sequences?

This gives a numeric score of similarity between two sequences

A G T C T C

A T T G T C

--------------------------

1 -1 1 -1 1 1 = 2

• Alignment – Way of placing one sequence above other to make clear the correspondence between them.

A G T C G T C

A _ T C _ T C

--------------------------

1 -2 1 1 -2 1 1 = 1

• Many methods have been proposed for sequence comparison.

• Some Important ones include –

• Dynamic programming algorithms for sequence alignment - Global, Local or Semi-Global Alignment

• Heuristic and Database Search Algorithms - BLAST, FASTA.

• Multiple sequence alignment Algorithms

• Multiple sequence alignment methods are mainly used when there is a need to extract information from a group of sequences.

• Examples of situations in which these techniques are used include the determination of secondary or tertiary structures, characterization of protein families, identification of similar regions etc.

• Also many miscellaneous techniques have been proposed for sequence comparison

• Contact based sequence alignment

• Using Correlation Images

• Some methods have been proposed without using the fundamental tool of Sequence Alignment

• Shortest unique substring

• Basic Concepts of Molecular Biology

• BLAST

• Clustering

• Phylogeny Trees / Phylip

• Proteins –

• Most substances in our body are proteins

• Some of these are structural proteins and some are enzymes.

• Proteins are responsible for what an organism is and what it does in physical sense.

• Amino Acids –

• A protein is a chain of simple molecules called Amino Acids. There are total 20 amino acids

• Nucleic Acids –

• Nucleic Acids encode information necessary to produce proteins

• They are responsible for passing recipe to subsequent generations.

• 2 types of nucleic acids present in living organisms,

• RNA (ribonucleic acid)

• DNA (deoxyribonucleic acid).

• BLAST (Basic Local Alignment Search Tool)

• BLAST algorithms are heuristic search methods

• This method seeks words of length W (default=3 in blastP) that score at least T when aligned with the query and scored with the substitution matrix (e.g. PAM)

• Clustering

• It can be defined as “The process of organizing objects into groups whose members are similar in some way”

• Phylogeny -The context of evolutionary biology

• Phylogeny Trees

• Relationships between different species and their common ancestors shown by constructing a tree.

• PHYLIP, the Phylogeny Inference Package, is a package of programs for inferring phylogenies (evolutionary trees) from University of Washington .

• What Phylip can do??

• Data used by phylip.

• Following are the programs used from Phylip package in this research.

• FITCH - Estimates phylogenies from distance matrix data.

• KITCH - Estimates phylogenies from distance matrix data.

• NEIGHBOR - Produces an un-rooted tree

• DRAWGRAM - Plots rooted phylogenies, cladograms, circular trees and phenograms in a wide variety of user-controllable formats. The program is interactive.

• DRAWTREE - Similar to DRAWGRAM but plots unrooted phylogenies.

3

2

1

0

A T G C

A

T

G

C

Our Approach …Correlogram

• What is a Correlogram??

• Representation of sequence in mathematical space.

• 3-D matrix of which 2 dimensions are the set of entities (e.g.. Amino Acids, Nucleic Acids) and third dimension is distance.

• Correlogram method has already been used for Image comparison.

“Image indexing using color correlograms” By Jing Huang,S Ravi Kumar, Mandar Mitra, Wei-Jing Zhu, Ramin Zabih

• A color correlogram expresses how the spatial correlation of pairs of colors changes with the distance

• Color correlogram has also been used recently for object tracking

• Correlograms were used to analyze autocorrelation characteristics of active polypeptides.

• MF Macchiato, V Cuomo and A Tramontano (1985), “Determination of the autocorrelation orders of proteins”

• For analyzing spatial patterns in various experiments.

• Giorgio Bertorelle and Guido Barbujanit (1995), “Analysis of DNA Diversity by Spatial Auto Correlation”

• In studies regarding patterns of transitional mutation biases within and among mammalian genomes

• Michael S. Rosenberg, Sankar Subramanian, and Sudhir Kumar (2003), “Patterns of Transitional Mutation Biases Within and Among Mammalian Genomes”

• Example

• Sequence ….. agcttactgt

• If we calculate the

appearance of every pair of

characters at distance 1 ..

• The Correlogram Plane for

distance 1 will be ->

• Correlogram can be

constructed as a set of

frequencies for different

distances.

d = 1

• Example

• Sequence ….. agcttactgt

• Correlogram plane for d=0

d = 0

• Example

• Sequence ….. agcttactgt

• Correlogram plane for d=2

d = 2

• Correlogram plane shown here is of a protein sequence for distance 0.

• At distance 0 each character is compared with itself so we can see all the peaks at diagonal.

• This is a Histogram.

• Similarly Correlogram frequencies for distance 1 and distance 2 can be represented as…

• Need for normalization – Finding similarity between sequences of different length.

• For every correlogram plane, each value is divided by the total volume of that plane.

0.5

0.5

0.25

0.25

d -> 2 3 4 5 6

Extension - Gapped Correlogram

• Gapped Correlogram - Consideration the gapped alignment of sequences

• The reason is if a pair of character is at distance d, there is probability that in other sequence it might appear at distance d-1 or d+1.

• Adding a ‘delta’ to Correlogram.

For every pair at distance n, frequency f and with delta = d, a fraction of frequency f/(2|n-distance|) is added at distances n-1,n-2… n-d and distances n+1,n+2… n+d.

D=2

Delta = 1

D=3

D=4

previous plane

next plane

• We are using these Correlograms for comparison of 2 sequences.

• Correlograms were constructed using same set of distances for both the sequences being compared.

• Then distance between each cell of two Correlograms (i.e. Two 3-D Matrices) is calculated as

dijk = (Sijk – S’ijk )2 / (1+ Sijk + S’ijk )

where i, j and k are 3 dimensions.

• These distances were then added to get a final distance between two sequences.

d = √ ∑ dijk

• One major difference !!

• Purpose

• To discriminate and compare the capability of correlogram-method with one of the "traditional" comparison techniques i.e. Smith-Waterman Dynamic Programming algorithms.

• The reason for using DP algorithms for comparison was that they are the most standard method for sequence comparison.

• The sequences used in these experiments were amino acid sequences

• In all the experiments, the pair of sequences was compared using both Correlogram method and DP Method.

• The experiments were designed as follows

• Comparing a base sequence with its reverse sequence

• Wrap around the target sequence at different character length and measure the difference with respect to the reference sequence each time

• Delete an amino acid from target sequence and measure the difference with respect to the reference sequence each time

• Replace an amino acid at different location and measure the difference with respect to the reference sequence each time

• Add an amino acid from target sequence and measure the difference with respect to the reference sequence each time

• Comparing a base sequence with its reverse sequence.

• Wrap around the target sequence at different character length and measure the difference with respect to the reference sequence each time.

• Delete an amino acid from target sequence and measure the difference with respect to the reference sequence each time.

• Replace an amino acid at different location and measure the difference with respect to the reference sequence each time.

• Add an amino acid at different location and measure the difference with respect to the reference sequence each time.

• “Alternate circulation of recent equine-2 influenza viruses (H3N8) from two distinct lineages in the United States” By Alexander C.K. Lai, Kristin M. Rogers, Amy Glaser, Lynn Tudor, Thomas Chambers

• hemagglutinin (HA) gene from Different strains of equine-2 influenza viruses.

• GeneTool version 1.1. – Compilation and analysis

• Phylogenetic analysis was performed by using the deduced HA1 amino acid sequence and the PHYLIP software package

• Distance matrix was calculated by using the PROTDIST program, and an unrooted tree generated by using the FITCH program.

• We have done an experiment with the same test data.

• All the protein sequences were searched. http://www.ebi.ac.uk/cgi-bin/expasyfetch

• A distance matrix was created using correlogram distances for every pair among these sequences.

• From this distance matrix, a tree is created using PHYLIP software package.

• The program ‘FITCH’ is used for creating tree whereas the program ‘DRAWTREE’ is used for visualizing the tree.

• Parvovirus causes stomach diseases in children.

• Coat protein – Some coat proteins are important as they are responsible for the resistance.

• Different strains of parvoviri were studied for their VP1 Protein.

• Reference for the test data – Dr. Mavis McKenna and Dr. Rob McKenna from University of Florida, Gainesville.

• From these distance matrices, trees were created using PHYLIP software package.

• The programs ‘NEIGHBOR’ and ‘DRAWTREE’ were used.

A T C G T

A T C G A T C G T T A G C T C C

Target

1st Comparison

Last Comparison

Experiment 3 -Correlogram for Sequence Scanning

• The next experiment was to use correlogram for scanning Sequences i.e. Pattern Finding.

• The algorithm Scan Correlogram was developed for finding the occurrences of a given pattern over a long sequence.

2nd Comparison

• Following Viruses were used in this experiment

• Porcine-parvovirus

• Bovine Parvovirus

• CPV Packaged Strand

• H1 Complementary

• MVM Packaged Strand

• PhiX-Genome

• AAV NC001401

• AAV Complementary

• Astell and Tattersall MVMi Packaged Sequence

• The patterns searched were as follows

• ACACCAAAA

• ATACCTCTTGC

• ATCCTCTATCAC

• Following are the results shown for Bovine Parvovirus.

• The length of sequence was 5517 and cut-off score used was 2.48 for all three patterns.

• Following are the results shown for Bovine Parvovirus for pattern ACACCAAAA.

• This research developed the correlogram comparison method for comparing sequences. Experiments were performed on real sequences and on synthetic sequences to answer the research questions of whether the correlogram biological sequences.

• It was observed that the Dynamic Programming method was more sensitive to the positioning of characters (i.e. amino acids or nucleic acids) in the sequence (sequence alignment), whereas the Correlogram method was found to be more sensitive to the character itself (contents of the sequence)

• The real data experiment was conducted on different strains of the horse influenza virus and the parvovirus. It was observed that the phylogeny was retained in most cases, however there were certain remarkable differences between the two.

• The scan correlogram algorithm was developed and used in this research to find motifs or patterns. The results of this experiment showed that the sub-sequences obtained were very similar to the given pattern.

• The further study can be done to see how the array of distances used for correlogram computations can impact the results.

• It will be interesting to study various delta values for Gapped correlograms and how they affect the scores. This gapped correlogram method can be further researched to see if the delta values are useful in determining global versus local alignments.

• Enhancements can be made to the scan correlogram method to use the gapped correlogram method for finding patterns and also to find the sub-sequences of more or less length than that of the pattern sequence.

• Dr. Kuntal Sengupta suggested that correlogram method can be used for comparison of bio-sequences.

• Dr. Mavis McKenna and Dr. Rob McKenna, University of Florida, Gainesville.

• Mridula Anand, Florida Institute of Technology.

• http://www.ncbi.nlm.nih.gov/BLAST/

• http://highwire.stanford.edu/

• http://au.expasy.org/

• http://evolution.gs.washington.edu/phylip.html

• “Alternate circulation of recent equine-2 influenza viruses (H3N8) from two distinct lineages in the United States” By Alexander C.K. Lai, Kristin M. Rogers, Amy Glaser, Lynn Tudor, Thomas Chambers.

• “Image indexing using color correlograms” By Jing Huang,S Ravi

Kumar, Mandar Mitra, Wei-Jing Zhu, Ramin Zabih

• “Phylogeny of the genus Haemophilus as determined by comparison of partial infB sequences” By Jakob Hedegaard, Henrik Okkels, Brita Bruun, Mogens Kilian, Kim K. Mortensen1 and Niels Nørskov-Lauritsen

Thanks!!