8/27/07bcb 444/544 f07 isu dobbs #4 - sequence alignment1 bcb 444/544 finish: lecture 2- biological...

8/27/07BCB 444/544 F07 ISU Dobbs #4 - Sequence Alignment 1

BCB 444/544

Finish: Lecture 2- Biological Databases

Lecture 4

Sequence Alignment

#4_Aug27


Required Reading (before lecture)

Mon Aug 27 - for Lecture #4Pairwise Sequence Alignment • Chp 3 - pp 31-41 Xiong Textbook

Wed Aug 29 - for Lecture #5Dynamic Programming• Eddy: What is Dynamic Programming?

Thurs Aug 30 - Lab #2:Databases, ISU Resources,& Pairwise Sequence

Alignment

Fri Aug 31 - for Lecture #6Scoring Matrices and Alignment Statistics

• Chp 3 - pp 41-49


HW#2:


Back to: Chp 2- Biological Databases

• Xiong: Chp 2

Introduction to Biological Databases

• What is a Database?• Types of Databases• Biological Databases• Pitfalls of Biological Databases• Information Retrieval from Biological Databases


What is a Database?

Duh!!

OK: skip we'll skip that!


Types of Databases

3 Major types of electronic databases:

1. Flat files - simple text files

• no organization to facilitate retrieval

2. Relational - data organized as tables ("relations")

• shared features among tables allows rapid search

• Object-oriented - data organized as "objects"

• objects associated hierarchically


Biological Databases

Currently - all 3 types, but MANY flat files

What are goals of biological databases?

1.Information retrieval

2.Knowledge discovery

Important issue: Interconnectivity


Types of Biological Databases

1- Primary• "simple" archives of sequences, structures, images, etc.

• raw data, minimal annotations, not always well curated!

2- Secondary• enhanced with more complete annotation of sequences,

structures, images, etc.

• usually curated!

3- Specialized• focused on a particular research interest or organism

• usually - not always - highly curated


Examples of Biological Databases

1- Primary

• DNA sequences

• GenBank - US

• European Molecular Biology Lab - EMBL

• DNA Data Bank of Japan - DDBJ

• Structures (Protein, DNA, RNA)

• PDB - Protein Data Bank

• NDB - Nucleic Acid Data Bank


Examples of Biological Databases

2- Secondary

• Protein sequences

• Swiss-Prot, TreEMBL, PIR

• these recently combined into UniProt

3- Specialized

• Species-specific (or "taxonomic"

specific)

• Flybase, WormBase, AceDB, PlantDB

• Molecule-specific,disease-specific


• Errors!

&• Lack of documentation re: quality or reliability of data• Limited mechanisms for "data checking" or preventing

propagation of errors (esp. annotation errors!!)• Redundancy• Inconsistency• Incompatibility (format, terminology, data types, etc.)

Pitfalls of Biological Databases


Information Retrieval from Biological Databases

2 most popular retrieval systems:

• ENTREZ - NCBI

• will use a LOT - was introduced in Lab 1

• SRS - Sequence Retrieval Systems - EBI

• will use less, similar to ENTREZ

Both:

• Provide access to multiple databases

• Allow complex queries


Web Resources: Bioinformatics & Computational Biology

• NCBI - National Center for Biotechnology Information

• ISCB - International Society for Computational Biology• JCB - Jena Center for Bioinformatics• Pitt - OBRC Online Bioinformatics Resources Collection• UBC - Bioinformatics Links Directory• UWash - BioMolecules

• ISU - Bioinformatics Resources - Andrea Dinkelman• ISU - YABI = "Yet Another Bioinformatics Index"

(from BCB Lab at ISU)

• Wikipedia: Bioinformatics


ISU Resources & Experts

ISU Research Centers & Graduate Training Programs:

• LH Baker Center - Bioinformatics & Biological Statistics• BCB - Bioinformatics & Computational Biology• BCB Lab - (Student-Led Consulting & Resources)• CIAG - Center for Integrated Animal Genomics• CCILD - Computational Intelligence, Learning & Discovery• IGERT Training Grant - Computational Molecular Biology

ISU Facilities:

• Biotechnology - Instrumentation Facilities• PSI - Plant Sciences Institute • PSI Centers


BEWARE!

SUMMARY:#2- Biological Databases


Chp 3- Sequence Alignment

SECTION II SEQUENCE ALIGNMENT

Xiong: Chp 3

Pairwise Sequence Alignment

• Evolutionary Basis • Sequence Homology versus Sequence Similarity • Sequence Similarity versus Sequence Identity • Methods • Scoring Matrices• Statistical Significance of Sequence Alignment

Adapted from Brown and Caragea, 2007, with some slides from: Altman, Fernandez-Baca, Batzoglou, Craven, Hunter, Page.


Motivation for Sequence Alignment

"Sequence comparison lies at the heart of bioinformatics analysis." Jin Xiong

Sequence comparison is important for drawing functional & evolutionary inferences re: new genes/proteins

Pairwise sequence alignment is fundamental; it used to:• Search for common patterns of characters • Establish pair-wise correspondence between related

sequences

Pairwise sequence alignment is basis for:• Database searching (e.g., BLAST)• Multiple sequence alignment (MSA)


Why Align Sequences?

Databases contain many sequences with known functions & many sequences with unknown functions.

Genes (or proteins) with similar sequences may have similar structures and/or functions.

Sequence alignment can provide important clues to the function of a novel gene or protein


Examples of Bioinformatics Tasks that

Rely on Sequence Alignment

• Genomic sequencing (> 500 complete genomes sequenced!)

• Assembling multiple sequence reads into contigs, scaffolds

• Aligning sequences with chromosomes• Finding genes and regulatory regions• Identifying gene products• Identifying function of gene products• Studying the structural organization of genomes• Comparative genomics


Evolutionary Basis

• DNA, RNA and proteins are "molecular fossils" • they encode the history of millions of years of evolution

• During evolution, molecular sequences accumulate random changes (mutations/variants)

• some of which provide a selective advantage or disadvantage, and some of which are neutral

• Sequences that are structurally and/or functionally important tend to be conserved

• (e.g., chromosomal telomeric sequences; enzyme active sites)

• Significant sequence conservation allows inference of evolutionary relatedness


Homology

Homology has a very specific meaning in evolutionary & computational biology - & the term is often used incorrectly

For us:

Homology = similarity due to descent from a common evolutionary ancestor

But, HOMOLOGY ≠ SIMILARITY

When 2 sequences share a sufficiently high degree of sequence similarity (or identity), we may infer that they are homologous

We can infer homology from similarity (can't prove it!)


Orthologs vs Paralogs

2 types of homologous sequences:• Orthologs - "same genes" in different species;

result of common ancestry; corresponding proteins have "same" functions

(e.g., human -globin & mouse -globin) • Paralogs - "similar genes" within a species;

result of gene duplication events; corresponding proteins may (or may not) have similar functions

(e.g., human -globin & human -globin)

A is the parent geneSpeciation leads to B & CDuplication leads to C’

B and C are OrthologousC and C’ are Paralogous

Speciation

Duplication

B

A

C C'


Sequence Homology vs Similarity

• Homologous sequences - sequences that share a common evolutionary ancestry

• Similar sequences - sequences that have a high percentage of aligned residues with similar physicochemical properties

(e.g., size, hydrophobicity, charge)

IMPORTANT:• Sequence homology:• An inference about a common ancestral relationship, drawn

when two sequences share a high enough degree of sequence similarity

• Homology is qualitative

• Sequence similarity:• The direct result of observation from a sequence alignment • Similarity is quantitative; can be described using percentages


Sequence Similarity vs Identity

For nucleotide sequences (DNA & RNA), sequence similarity and identity have the "same" meaning: • Two DNA sequences can share a high degree of sequence identity

(or similarity) -- means the same thing• Drena's opinion: Always use "identity" when making

quantitative comparisons re: DNA or RNA sequences (to avoid confusion!)

For protein sequences, sequence similarity and identity have different meanings:• Identity = % of exact matches between two aligned sequences• Similarity = % of aligned residues that share similar

characteristics (e.g, physicochemical characteristics, structural propsensities, evolutionary profiles)

• Drena's opinion: Always use "identity" when making quantitative comparisons re: protein sequences (to avoid confusion!)


What is Sequence Alignment?

Given 2 sequences of letters, and a scoring scheme for evaluating matching letters, find an optimal pairing of letters in one sequence to letters of other sequence.

Align:

1: THIS IS A RATHER LONGER SENTENCE THAN THE NEXT. 2: THIS IS A SHORT SENTENCE.

1: THIS IS A RATHER LONGER SENTENCE THAN THE NEXT. 2: THIS IS A ######SHORT## SENTENCE##############.

OR

1: THIS IS A RATHER LONGER SENTENCE THAN THE NEXT. 2: THIS IS A ##SHORT###SENT#EN###CE##############.

Is one of these alignments "optimal"? Which is better?


Goal of Sequence Alignment

Find the best pairing of 2 sequences, such that there is maximum correspondence between residues

• DNA 4 letter alphabet (+ gap)

TTGACACTTTACAC

• Proteins 20 letter alphabet (+ gap)

RKVA-GMA RKIAVAMA


Statement of Problem

Given: • 2 sequences• Scoring system for evaluating match (or

mismatch) of two characters • Penalty function for gaps in sequences

Find: Optimal pairing of sequences that• Retains the order of characters• Introduces gaps where needed• Maximizes total score


Types of Sequence Variation

• Sequences can diverge from a common ancestor through various types of mutations:

• Substitutions ACGA AGGA• Insertions ACGA ACCGA • Deletions ACGA AGA

• Insertions or deletions ("indels") result in gaps in alignments• Substitotions result in mismatches• No change? match


Gaps

Indels of various sizes can occur in one sequence relative to the other e.g., corresponding to a shortening of the polypeptide chain in a protein


Avoiding Random Alignments with a Scoring Function

• Introducing too many gaps generates nonsense alignments:

s--e-----qu---en--cesometimesquipsentice

• Need to distinguish between alignments that occur due to homology and those that occur by chance

• Define a scoring function that accounts for mismatches and gaps

Scoring Function (F): e.g. Match: + m +1 Mismatch: - s -1 Gap: - d -2

F = m(#matches) + s(#mismatches) + d(#gaps)


Not All Mismatches are the Same

• Some amino acids are more "exchangeable" than others; e.g., Ser and Thr are more similar than Trp and Ala

• A substitution matrix can be used to introduce "mismatch costs" for handling different types of substitutions

• Mismatch costs are not usually used in aligning DNA or RNA sequences, because no substitution is "better" than any other (in general)


Substitution Matrix

s(a,b) corresponds to score of aligning character a with character b

Match scores are often calculated based on frequency of mutations in very similar sequences

(more details later)


Methods

• Global and Local Alignment• Alignment Algorithms• Dot Matrix Method• Dynamic Programming Method

• Gap penalities• DP for Global Alignment• DP for Local Alignment

• Scoring Matrices• Amino acid scoring matrices• PAM• BLOSUM• Comparisons between PAM & BLOSUM

• Statistical Significance of Sequence Alignment


Global vs Local Alignment

Local alignment

• Finds local regions with highest similarity between 2 sequences

• Aligns these without regard for rest of sequence

• Sequences are not assumed to be similar over entire length

Global alignment

• Finds best possible alignment across entire length of 2 sequences

• Aligned sequences assumed to be generally similar over entire length


Global vs Local Alignment - example

S = CTGTCGCTGCACGT = TGCCGTG

CTGTCG-CTGCACG

-TGCCG--TG----

Global alignment

CTGTCG-CTGCACG

-TGC-CG-TG----CTGTCGCTGCACG---------TGC-CGTG

Local alignment

Which is better?


Global vs Local Alignment When use which?

Both are important but it is critical to use right method for a given task!

Global alignment:• Good for: aligning closely related sequences of approx. same

length• Not good for: divergent sequences or sequences with different

lengths

Local Alignment:• Good for: searching for conserved patterns (domains or motifs) in

DNA or protein sequences• Not good for: generating alignment of closely related sequences

Global and local alignments are fundamentally similar and differ only in optimization strategy used in aligning similar residues


Alignment Algorithms

3 major methods for alignment:

1. Dot matrix analysis 2. Dynamic Programming 3. Word or k-tuple methods (later, in Chp 4)


Dot Matrix Method (Dot Plots)

• Place 1 sequence along top row of matrix

• Place 2nd sequence along left column of matrix

• Plot a dot each time there is a match between an element of row sequence and an element of column sequence• For proteins, usually use more

sophisticated scoring schemes than "identical match"

• Diagonal lines indicate areas of match • Reverse diagonals (perpendicular to

diagonal) indicate inversions

ACACG

A CC G G

Exploring Dot Plots

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