Thursday and Friday

Dr Michael CartonFormerly VO’F group, now National Disease Surveillance Centre (NDSC)

Wed (tomorrow) 10am - this suite booked for BLAST searches

TODAY

www.nuigalway.ie/microbiology/bioinformaticsnode/home.html

Lots of definitions - don’t worry!!But, later on, look stuff up on Google

or Scirus

Remember: Homology:- sequences are homologous

if they are related by divergence from a common ancestor

Sequence alignment In order to detect sequence

homology we must first align sequences.

An alignment is a hypothesis of positional homology between nucleotides/amino acids.

Alignment example

Take the case of a hypothetical ancestral sequence (GAATTCGC). Over time mutation may lead to two different forms of this sequence, GAATTCGC and GATTGGC.

Example continued Alignment without gaps

GAATTCGC

GATTGGC

** * Alignments with gaps

GAATTCGC or GAATTC–GC

GA–TTGGC GA–TT–GGC

** ** ** ** ** **

Types of alignment Local

Local alignment finds short regions of similarity between a pair of sequences

Global Global alignments attempts to find

the optimal alignment over the entire length of the sequences.

Local alignment Finds domains and short regions of

similarity between a pair of sequences. The two sequences under comparison do not necessarily need to have high levels of similarity over their entire length in order to receive locally high similarity scores.

Local alignment This feature of local similarity

searches give them the advantage of being useful when looking for domains within proteins or looking for regions of genomic DNA that contain introns. Local similarity searches do not have the constraint that similarity between two sequences needs to be observed over the entire length of each gene

Global alignment Finds the optimal alignment over the entire

length of the two sequences under comparison. Algorithms of this nature are not particularly suited to the identification of genes that have evolved by recombination or insertion of unrelated regions of DNA. In instances such as this, a global similarity score will be greatly reduced. In cases where genes are being aligned whose sequences are of comparable length and also whose entire gene is homologous (descent from a common ancestor), global alignment works well.

PROGRAMS USED Local

Blast Fasta3

Global Clustalw Clustalx

Terminology Exact (Exhaustive):

This is a method of looking at all possibilities for a particular problem and then choosing the best one. It is the most rigorous method.

Heuristic: This class of methods takes short-cuts

and attempts to arrive at an optimal solution by making educated guesses.

Matrices Write one sequence horizontally Write the other sequence vertically

to form a grid:T A T T G

T

A

A

T

G

1 1 0

0 1 0

1 0 1

Calculating an Alignment Score

An alignment’s score is calculated using Scoring matrix Gap Opening Penalty Gap extension penalty

Scoring an alignment

A C T G

A 1

C 0 1

T 0 0 1

G 0 0 0 1

Previous Example Alignment without gaps

GAATTCGC

GATTGGC

** * Alignments with gaps

GAATTCGC or GAATTC–GC

GA–TTGGC GA–TT–GGC

** ** ** ** ** **

Dotplot Matrix I

Dotplot Matrix II

Noise is caused by matches that have occurred by chance without any homology present. Can use a filter to reduce the noise, eg. only place a dot when a specified portion of a smallgroup of successive bases match, eg. window of 10 only highlighted if 6 of the 10 bases match

Chimpanzee haeomoglobin intergenic DNA plotted againstitself c. 400 bases

8 out of 10, even less noise

IDENTITY DOT BLOT-identity blocks-looks for blocks of perfect identity, -reduces time required

Chimp and spider monkey DNA, but c. 4,000 bases this time

Scoring matrix In reality, we know that certain mutations

are more likely to have occurred than others.

Conservation of the secondary structure of proteins is an important consideration.

The mutation of the third base in a codon often results in no change in the amino acid coded for.

Observations of alignments of amino acid sequences have been used to calculate the probability of certain substitutions.

Scoring Matrices Scoring matrices tell how similar

amino acids are. There are two main sets of scoring

matrices: PAM and BLOSUM. PAM is based on evolutionary

distances BLOSUM is based on

structure/function similarities

AA Matrices Assigning a score to all of the 210

possible amino acid substitutions has been done by several authors but 2 are especially noteworthy

Dayhoff et al. (1978) used amino acid alignments of sequences that were 85% similar as a basis for the PAM mutation data matrices

AA Matrices Henikoff and Henikoff (1992) used several

different alignments to produce the BLOSUM matrices.

The Blosum 62 Matrix is based on an alignment of sequences that are at least 62% similar

This is possibly the most used of amino acid substitution matrices and is the default matrix used in several applications

Scoring matrices These have been empirically determined

and have been calculated by the direct comparison of related protein sequences.

In general, amino acid substitutions that are seen to occur very rarely are given a negative value.

Conservative substitutions (i.e., isoleucine for leucine) are given a positive value. Identical matches are also given a positive value.

The bottom line on PAMFrequencies of alignmentFrequencies of occurrence

The probability that two amino acids, i and j arealigned by evolutionary descent divided by the

probability that they are aligned by chance

BLOSUM Matrices BLOSUM is built from distantly

related sequences whereas PAM is built from closely related sequences.

BLOSUM is built from conserved blocks of aligned protein segment found in the BLOCKS database.

PAM and BLOSUM Running searches with different matrices

will help find different sorts of hits. PAM30 will preferentially find

homologues that are evolutionarily close PAM250 will tend to find long, weak

diffuse matches typical of distantly related proteins.

BLOSUM62 is based on alignments of proteins that are at least 62% similar.

Evolutionary Basis of Sequence Alignment

1. Similarity: Quantity that relates to how alike two sequences are.2. Identity: Quantity that describes how aliketwo sequences are in the strictest terms.3. Homology: a conclusion drawn from datasuggesting that two genes share a commonevolutionary history.

Evolutionary Basis of Sequence Alignment (Cont. 1)

1. Example: Shown on the next page is a pairwise alignment of two proteins. One is mouse trypsin and the other is crayfish trypsin. They are homologous proteins. The sequences share 41% identity.

2. Underlined residues are identical. Asterisks and diamond represent those residues that participate in catalysis. Five gaps are placed to optimize the alignment.

Evolutionary Basis of Sequence Alignment (Cont. 2)

Why are there regions of identity?

1) Conserved function-residues participate in reaction.

2) Structural-residues participate in maintaining structure of protein. (For example, conserved cysteine residues that

form a disulfide linkage) 3) Historical-Residues that are conserved solely due to a

common ancestor gene.

Sequence Homology Searching

Find related sequences in the database

Original BLAST Segment pair- this is a pair of

subsequences of the same length that form an ungapped alignment.

BLAST searches for all segment pairs between the query sequence and all of the sequences in the database (above a certain threshold).

HSP-High-Scoring Pair.

Original Blast HSPs are derived by first finding

the pairs that satisfy the threshold (T) conditions. Then the alignment is extended in both directions unyil the quality of the alignment drops off dramatically or falls to zero

The HSPs are then sorted according to their score

Gapped BLAST The original BLAST suffered from the

limitation of not being able to introduce gaps into the alignment.

Gapped BLAST is an effort to circumvent this shortcoming.

Experience shows that often several ungapped non-overlapping alignments result from a match to a single database entry.

Two-Hit method Find 2 HSPs within a distance m of

each other on the same diagonal. Do not attempt an HSP extension

unless you find two regions that meet this criterion.

Attempt to generate a single gapped alignment in this region.

FastA algorithm Is the alignment significant? Could we see an alignment like this

purely by chance? What are the statistics involved?

ktups

Sequence X GAATTCGCATCThis 11 base sequence can be divided into six 6-

long segments of DNA GAATTC AATTCG ATTCGC TTCGCA TCGCAT CGCATC

These are known as ‘ktuples’ (ktup Fasta).Sequences in databases are stored in this

form.

Global Alignment vs. Local Alignment Global alignment is used when the overall

gene sequence is similar to another sequence-often used in multiple sequence alignment e.g. Clustal W algorithm

Local alignment is used when only a small portion of one gene is similar to a small portion of another gene. BLAST FASTA

Different forms of BLAST and FASTA

You have a nucleotide sequence. Want to compare with other

nucleotide sequences Blastn Fasta3

Different forms of BLAST and FASTA

To compare the 6-frame conceptual translation of the nucleotide sequence against a protein database Blastx Fastx3 Fasty3

Different forms of BLAST and FASTA

If we translate our nucleotide sequence, we can compare it to the translation of a nucleotide database; tBlastn tFasty3

Homology Search Tools BLAST (Basic Local Alignment

Search Tool) by Stephen Altschul http://www.ncbi.nih.gov/

FASTA by William Pearson http://www.ebi.ac.uk/

Open a new word file and 3 web browser windows