Bioinformatics 01 part 3 pairwise alignments and database searches
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Bioinformatics 01 Part 3: Pairwise Alignments and Database Searches. Similarity and homology Gap penalties and scoring matrices in pairwise alignments Alignment algorithms Database searching: BLAST and FASTA. Similarity and Homology.

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Bioinformatics 01 Part 3: Pairwise Alignments and Database Searches

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Bioinformatics 01 part 3 pairwise alignments and database searches

Bioinformatics 01Part 3: Pairwise Alignments and Database Searches

  • Similarity and homology

  • Gap penalties and scoring matrices in pairwise alignments

  • Alignment algorithms

  • Database searching: BLAST and FASTA


Similarity and homology

Similarity and Homology

  • If proteins that are similar share a common ancestor, they are said to be homologous

  • Homology can be inferred, but not confirmed, from similarity

  • Biological data can be used to support the case that two or more similar proteins arose from a common ancestor and are therefore homologous

  • Proteins can be similar but not homologous, but homologous proteins always show similarity


Examples of simple pairwise alignments

Sequence 1 VLKAHLIDGGSKLTS

||||| |||

Sequence 2 VLKAHIDGGSRLTS

ungapped alignment

Score: 8

Identity: 53%

Sequence 1 VLKAHLIDGGSKLTS

||||| ||||| |||

Sequence 2 VLKAH-IDGGSRLTS

gapped alignment

Score: 13

Identity: 86.7%

Examples of Simple Pairwise Alignments


Scoring penalties in pairwise alignments

Scoring Penalties in Pairwise Alignments

  • Penalties are imposed to prevent the unrestricted insertion of gaps

  • Gap penalty: a penalty for introducing a gap

  • Extension penalty: a penalty for extending a gap

  • In protein evolution, it is more likely that an existing gap would be extended than a new gap introduced

  • Consequently, the score for a gap penalty is greater than the score for an extension penalty


Dot matrix analysis and dot plots

Dot Matrix Analysis and Dot Plots

  • Compares two sequences in the form of a matrix, with each sequence lying along one axis

  • A match between residues is indicated by a dot

  • A sliding window is used to cut down “noise” and produce clearer results

  • Dot plot reveals diagonal lines where there is sufficient similarity between the sequences


Dot plot human and globin

Dot Plot: Human - and -Globin


Scoring matrices in pairwise alignments

Scoring Matrices in Pairwise Alignments

  • A scoring matrix takes into account the significance of matches and mismatches between aligned amino acids

  • In theory, a scoring matrix could be based on the different chemical and physical properties of amino acids

  • In practice, scoring matrices are based on observed differences between proteins (or parts of proteins)


Pam scoring matrices

PAM Scoring Matrices

  • Based on the analysis of 1,572 changes in 71 groups of closely related proteins (>85% identity)

  • Mutation probabilities were determined for each amino acid based on a substitution rate of 1%

  • These were used to construct the PAM 1 (point [or percent] accepted mutation) matrix

  • The PAM 250 matrix (often used as a default in pairwise alignments) provides scores equivalent to about 20% matches remaining between two sequences


Blosum scoring matrices

BLOSUM Scoring Matrices

  • Based on amino acid substitutions in a large set of amino acid patterns called blocks, derived from several hundred groups of related proteins

  • BLOSUM matrices take distant but significant relationships between proteins into account, because only protein segments are considered

  • Over-representation of amino acid substitutions in closely related protein segments was reduced by combining those segments into one sequence

  • Example: proteins showing 62% or more identity were grouped to produce the BLOSUM62 matrix


Alignments and dynamic programming

Alignments and Dynamic Programming

  • Complete search of all possible alignments is computationally demanding and frequently impossible

  • Algorithms that use dynamic programming have been developed to obtain alignments between sequences

  • Algorithms may produce either global or local alignments


Global alignment needleman wunsch

Global Alignment: Needleman-Wunsch

  • A matrix is constructed that shows matches between the two sequences

  • Moving from the top left of the matrix, a process of summation is carried out taking penalties into account

  • For any given cell in the matrix, the maximum score for that cell is entered

  • Needleman-Wunsch attempts to align all residues in the two sequences, and is therefore a global alignment algorithm


Local alignment smith waterman

Local Alignment: Smith-Waterman

  • Takes into account that two relatively dissimilar sequences may exhibit short regions of local similarity

  • Smith-Waterman uses a local alignment algorithm to detect these similarities

  • Each cell in the matrix is considered as the end point of a potential alignment

  • A value for each cell is calculated using a similarity score, taking matches, mismatches and gaps into account

  • A backtracking procedure from the highest scoring cell is then used to trace the alignment through the matrix


Pairwise database searching

Pairwise Database Searching

  • Use of the Needleman-Wunsch or Smith-Waterman algorithms in pairwise database searching requires enormous computational power

  • Heuristic approximations of these algorithms are therefore used in database searches

  • Examples of search tools are BLAST and FASTA

  • Both BLAST and FASTA aim to identify short identical matches, which are then extended to produce local alignments


Blast

BLAST

  • Search is made for regions of short length (words or k-tuples) obtained from the query sequence that match a database sequence = high scoring pairs (HSPs)

  • HSPs are extended in both directions to produce optimal alignments above a certain score

  • A scoring matrix (default is BLOSUM62), gap and gap extension penalties are taken into account in determining alignments

  • Optimal alignments are then reported in order of decreasing score


Bioinformatics 01 part 3 pairwise alignments and database searches

http://www.ncbi.nlm.nih.gov/BLAST/


Fasta

FASTA

  • Regions of short length (words) in the query that match a target sequence are determined

  • High scoring regions (best initial regions) are used to rank matches for further analysis

  • Longer high scoring regions, including gaps, are generated by joining best initial regions

  • A full Smith-Waterman alignment is then performed between the high scoring regions

  • FASTA is slower than BLAST but may, in some cases, be more sensitive


Bioinformatics 01 part 3 pairwise alignments and database searches

http://www.ebi.ac.uk/fasta33/


A final few words of advice

A Final Few Words of Advice

  • Protein-protein searches are more informative than nucleotide-nucleotide searches (when the query is known to contain a protein-coding nucleotide sequence)

  • When performing a pairwise database search with a new, protein-coding nucleotide sequence, always use a translation of the nucleotide sequence in all six frames as the query

  • This can be done by using, for example, a translated BLAST search (such as tblastx, which translates both the query sequence and a nucleotide database)


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