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Sources Page & Holmes Vladimir Likic presentation: http :// 20show.pdf Wikipedia Lecture at : http:// / usman /courses/bnfo601_fall08/ AffineGap.pdf.

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  • Sources

  • Page & Holmes

  • Vladimir Likic presentation:

  • Wikipedia

  • Lecture at :

Homoplasy – structural or DNA resemblance due to parallelism or convergent evolution rather than to common ancestry

Which are homoplasious?

Problem: which base positions share common descent?





Alignment – residue to residue correspondence between 2 or more sequences such that the order of residues in each sequence is preserved.





Indels make alignment trickier





Alignment problems (examples)

1) different sequences of the same allele from the same locus within the same individual

2) sequences of different alleles from the same locus within the same individual

3) same locus from different individuals

Assembly – (from ensembl) - When the genome of a species is to be sequenced, the chromosomes from many cells are broken at random positions into small fragments, which are sequenced, and reassembled into long sequences (contigs). Contigs may be assembled into longer sequences called scaffolds and sometimes, if the depth of sequencing is high enough, there may be enough information to assemble most of the scaffolds into chromosomes. The resulting collection of sequences after assembly is called a genome assembly.

  • Alignment Methods

  • Dot plot – qualitative

  • Sequence alignment – quantitative; constructing the best alignment using a scoring scheme

  • Types of Alignment

  • Global – best alignment over the entire length

  • Local – best alignment in small region; used when comparing sequences of different lengths

  • Multiple – beyond pairwise

cagcacttggattctgg & cagcgtgg




Global (best depending on gap penalties)



  • Gaps

  • residue to nothing match that can be inserted in either sequence

  • are not part of the DNA sequence, only a construct for alignment

  • Gap to gap match is meaningless and not allowed

  • Scoring scheme

  • A set of rules that assigns a score to a particular alignment between two sequences

  • Goal is to maximize score

  • Score is sum of residue substitution scores and gap penalties

+1 for match diagonals

-1 for mismatch

No gap penalty

atggcgt +1+1+1-1+1+1 = 4


atggcgt +1-1+1-1+1+1 = 2


Substitution matrix:

c t a g

c 1 -1 -1 -1

t-1 1 -1 -1

a -1 -1 1 -1

g -1 -1 -1 1

What if we want to penalize transitions less than diagonalstransversions?

Substitution matrix:

c t a g

c 2 1 -1 -1

T 1 2 -1 -1

a -1 -1 2 1

g -1 -1 1 2

  • Protein substitution matrices diagonals

  • More complex than DNA scoring matrices.

    • Proteins are composed of twenty amino acids, and physical-chemical properties of individual amino acids vary considerably.

    • can be based on any property of amino acids: size, polarity, charge, hydrophobicity.

    • Evolutionary substitution matrices – empirically derived by assessment of frequencies of changes at particular levels of divergence

  • Evolutionary substitution matrices diagonals

  • PAM ("point accepted mutation") family PAM250, PAM120, etc.

  • BLOSUM ("Blocks substitution matrix") family BLOSUM62, BLOSUM50, etc.

  • The BLOSUM matrices were developed more recently and considered better.

Blosum62 diagonals

Blosum80 is used for less divergent sequences

Blosum45 is used for more divergent sequences


Gaps diagonals

  • Because gaps often result in radical protein changes (frame shifts, premature stop), the penalty for a gap is usually several times greater than the penalty for a mutation.

  • Once created, gaps of more than one residue might be less expensive than a completely new gap - in other words gap opening penalties and gap extension penalties are often definedseparately

Affine gap penalty function W( diagonalsi)


(for i>= 1, where i = gaplength )

•g: gapopeningpenalty

•h: gapextensionpenalty

•The ratio betweengandh determines the relative weight for opening versus extension

–Small g, Large h: gaplengthmore important

–Large g, Small h: gaplengthlessimportant

Substitution matrix: diagonals

c t a g

c 2 1 -1 -1

T 1 2 -1 -1

a -1 -1 2 1

g -1 -1 1 2


G = -3

H = -1





26 – 3 – 1(7) = 16

26 – 3 (3) – 1(7) =10

How do we diagonalsfind the best alignment?

Brute-force approach:

Generate the list all possible alignments between two sequences, score them, select the alignment with the best score

The number of possible global alignments between two sequences of length N is

For two sequences of 250 residues this is ~10149

Needleman- diagonalsWunschand Smith-Waterman are both algorithms that find the best alignment through breaking the problem down into sub problems using dynamic programming

…however, it is only the best based on the scoring matrix and the gap opening and extension penalities

These methods are computationally expensive

  • BLAST – Basic Local Alignment Search Tool diagonals

  • Tries to find the highest scoring ungapped local alignment between a query and a database

  • Uses a word length (w) and scans for matches with a higher threshold (T) when aligned with words in the query

  • The local alignment is then extended in both directions until the score falls below the best score reached so far.

  • Many types of blast can be found at