Pairwise Sequence Alignment (PSA) Flashcards

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1
Q

The process of lining up two or more sequences to achieve maximal levels of identity (and conservation, in the case of amino acid sequences) for the purpose of assessing the degree of similarity and the possibility of homology.

A

Pairwise Sequence Alignment

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2
Q

share a common evolutionary ancestry

A

Homology

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3
Q

extent to which two amino acid (or nucleotide) sequences are invariant (unchanged) = exact matching

A

Identity

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4
Q

general description of a relationship = optimal matching

A

Similarity

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5
Q

Basis of similarity in Proteins

A

Hydroxylic
Tiny
Small
Acidic
Positive (Basic)
Polar
Charged
Hydrophobic
Aromatic
Sulphur containing
Aliphatic

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6
Q

homologous sequences in different species that arose from a common ancestral gene during speciation

A

Orthologs

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7
Q

homologous sequences that arose by a mechanism such as gene duplication

A

Paralogs

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8
Q

Scoring Matrices

A

Perfect match = +1
Mismatch = 0
Gap opening = -2
Gap = -1

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9
Q

Why penalize gaps?

A

✓ maximizes the number of matches and
✓ minimizes the number of gaps

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10
Q

Protein Sequence Alignment matrices(4)

A

Identity matrix
Mutation data matrix
Physical properties matrix
Genetic code matrix

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11
Q

Protein sequence alignment matrix

o Exact matches receive one score and non-exact matches a different score (1 on the diagonal 0 everywhere else)

A

Identity matrix

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12
Q

Protein sequence alignment matrix

o a scoring matrix compiled based on observation of protein mutation rates: some mutations are observed more often than others (PAM, BLOSUM)

A

Mutation data matrix

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13
Q

Protein sequence alignment matrix

o amino acids with similar biophysical properties receive a high score.

A

Physical properties matrix

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14
Q

Protein sequence alignment matrix

o amino acids are scored based on similarities in the coding triple

A

Genetic code matrix

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15
Q

Basis of Scoring Matrices

A

Accepted Point Mutation (PAM)
Block Substitution Matrix (BLOSUM)

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16
Q

Basis of Scoring Matrices

a replacement of one amino acid in a protein by another residue that has been accepted by natural selection

A

Accepted Point Mutation (PAM)

17
Q

Basis of Scoring Matrices

By Henikoff and Henikoff (1992, 1996)
They focused on conserved regions (blocks) of proteins that are distantly related to each other

A

Block Substitution Matrix (BLOSUM)

18
Q

Methods of alignment

A

By Hand
Dot Plot
Rigorous Algorithm
Heuristic Methods

19
Q

Method of alignment

slide sequences on two lines of a word processor

A

By Hand

20
Q

Method of alignment

Graphical matrix

A

Dot Plot

21
Q

Method of alignment

Dynamic programming (slow, optimal)

A

Rigorous Algorithm

22
Q

Method of alignment

fast, appropriate
BLAST and FASTA = word matching and hash tables

A

Heuristic methods

23
Q

a step-by-step set of instructions designed to solve a specific problem

A

Algorithm

24
Q

a set of instructions that uses an algorithm to solve a task

A

Program

25
Q

finding optimal alignments between sequences by considering all possible alignments and scoring them based on a scoring system

A

Dynamic Programming

26
Q

makes approximations of the best solution without exhaustively considering every possible outcome

A

Heuristic Programming

27
Q

Dynamic programming examples

Needleman and Wunsch (1970)

contains the entire sequence of each protein or DNA molecule

start at the beginning of two sequences and add gaps to each until the end of one is reached (end-to-end)

A

Global Alignment

28
Q

Dynamic Programming Examples

Smith and Waterman (1981)

focuses on the regions of greatest similarity between two sequences

finds the region (or regions) of highest similarity between two sequences and build the alignment outward from there

A

Local Alignment