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Bioinformatics

Bioinformatics. Ayesha M. Khan. DNA RNAProtein. Sequence comparison and alignment is a central problem in computational biology. The most basic task is: given two known sequences (DNA, RNA or amino acids) and a scoring model, determine if they are related or not. Sequence alignment.

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Bioinformatics

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  1. Bioinformatics Ayesha M. Khan

  2. DNARNAProtein • Sequence comparison and alignment is a central problem in computational biology. The most basic task is: given two known sequences (DNA, RNA or amino acids) and a scoring model, determine if they are related or not. Lec-6

  3. Sequence alignment • When we align sequences, we assume that they share a common ancestor • -They are then homologous • Protein fold is much more conserved than protein sequence • DNA sequences tend to be less informative than protein sequences  ATTGCGC ATTGCGC ATTGCGC  AT-CCGC ATTGCGC  ATCCGC C • An Alignment is a hypothesis of positional homology between bases/Amino Acids. Lec-6

  4. Sequence alignment • The alignment of two sequences (DNA or protein) is a relatively straightforward computational problem. • There are lots of possible alignments. • Two sequences can always be aligned. • Sequence alignments have to be scored. • Often there is more than one solution with the same score. Lec-6

  5. Identity vs. Similarity • Identity refers to an exact match between two nucleotides or amino acids • Similarity refers to a resemblance between two residues that is greater than one would expect at random. • Percent Sequence Identity • The extent to which two nucleotide or amino acid sequences are invariant. • 70% identical A C C T G A G – A G A C G T G – G C A G Lec-6

  6. Alignment methods • By hand - slide sequences on two lines of a word processor • Dot plot • with windows • Rigorous mathematical approach • Dynamic programming (slow, optimal) • Heuristic methods (fast, approximate) • BLAST and FASTA Lec-6

  7. Global and Local Alignment • Global alignment algorithms start at the beginning of two sequences and add gaps to each until the end of one is reached. Used when an objective and optimal measure is needed to compare two sequences and it is valid to assume that the length of the sequences is equivalent • Local alignment algorithms finds the region (or regions) of highest similarity between two sequences and build the alignment outward from there. Lec-6

  8. Global and Local Alignment Lec-6

  9. Global alignment • The the Needleman-Wunsch algorithm (1970) creates a global alignment over the length of both sequences. • Global algorithms are often not effective for highly diverged sequences - do not reflect the biological reality that two sequences may only share limited regions of conserved sequence. • Sometimes two sequences may be derived from ancient recombination events where only a single functional domain is shared. • Global methods are useful when you want to force two sequences to align over their entire length Lec-6

  10. Local Alignment • This method identify the most similar sub-region shared between two sequences • Smith-Waterman algorithm (1981) Lec-6

  11. Alignment parameters • Scoring Systems: • Each symbol pairing is assigned a numerical value, based on a symbol comparison table. • Gap Penalties: • Opening: The cost to introduce a gap • Extension: The cost to elongate a gap Lec-6

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