2. outline Introduction to bioinformatics Biological databases
Sequence alignment and their algorithms Structural prediction
Web-based tools Stand-alone software 3. Introduction to
bioinformatics What is the bioinformatics? Bioinformatics is an
interdisciplinary research area at the interface between computer
science and biological science. 4. Introduction to bioinformatics
What are differences between bioinformatics andinformatics? What
are differences between bioinformatics and computational biology?
What is the algorithm? 5. What is the proteomics!? 6. Biological
databases DatabaseA database is a computerized archive used to
store and organize data in such a way that information can be
retrieved easily via a variety of search criteria Entry Each record
should contain a number of fields that hold the actual data items
Value a particular piece of information Making a query To retrieve
a particular record from the database, a user can specify a value
to be found in a particular field and expect the computer to
retrieve the whole data record 7. Biological databases Primary
databases Gen bank (NCBI) EMBL DDBJwww.ncbi.nlm.nih.gov
www.ebi.ac.uk/embl/index.html www.ddbj.nig.ac.jp Secondary
databases ExPASY PIR SWISS-Prothttp://web.expasy.org
http://pir.georgetown.edu/pirwww/pirhome3.shtml
www.ebi.ac.uk/swissprot/access.html 8. Biological databases
Interconnection between Biological Databases 9. Biological
databases Pitfalls of biological databases The causes of redundancy
include: repeated submission of identical oroverlapping sequences
by the same or different authors, revision of annotations, dumping
of expressed sequence tags (EST) data Redundant sequences
Non-redundant sequences (Ref Seq) 10. Biological databases Further
databases NCBI www.ncbi.nlm.nih.gov Uniprot http://www.uniprot.org
ExPASY http://web.expasy.org PIR http://pir.georgetown.edu/
SWISS-Prot http://swissmodel.expasy.org/ PDB
http://www.rcsb.org/pdb/home/home.do Enzyme structure
http://www.ebi.ac.uk/thornton-srv/databases/enzymes 11. Biological
databases NCBIwww.ncbi.nlm.nih.gov 12. Biological databases
Uniprothttp://www.uniprot.org 13. Biological databases
ExPASYhttp://web.expasy.org 14. Biological databases
PIRhttp://pir.georgetown.edu/ 15. Biological databases
SWISS-Prothttp://swissmodel.expasy.org/ 16. Biological databases
PDBhttp://www.rcsb.org/pdb/home/home.do 17. Biological databases
Enzyme structurehttp://www.ebi.ac.uk/thornton-srv/databases/enzymes
18. Sequence alignment and their algorithms Pairwise sequence
alignment Pairwise sequence alignment is the process of aligning
two sequences and is the basis of database similarity searching and
multiple sequence alignment Sequence similarity versus sequence
homology When two sequences are descended from a common
evolutionary origin, they are said to have a homologous
relationship or share homology. A related but different term is
sequence similarity, which is the percentage of aligned residues
that are similar in physiochemical properties such as size, charge,
and hydrophobicity Sequence similarity versus sequence identity In
a protein sequence alignment, sequence identity refers to the
percentage of matches of the same amino acid residues between two
aligned sequences. Similarity refers to the percentage of aligned
residues that have similar physicochemical characteristics and can
be more readily substituted for each other 19. Sequence alignment
and their algorithms Sequence alignment strategies Global
alignmentIn global alignment, two sequences to be aligned are
assumed to be generally similar over their entire length. Alignment
is carried out from beginning to end of both sequences to find the
best possible alignment across the entire length between the two
sequences Local alignment In local alignment does not assume that
the two sequences in question have similarity over the entire
length. It only finds local regions with the highest level of
similarity between the two sequences and aligns these regions
without regard for the alignment of the rest of the sequence
regions 20. Sequence alignment and their algorithms 21. Sequence
alignment and their algorithms Linear gap penalty: The cost for
creation and extension of gaps are the sameW(I)= gI, g is the cost
for each gap and I is the lengthAffine gap penalty: different cost
for creation and extension W(I)=gopen + gext (I-1) and gopen <
GextSS,W I 22. Sequence alignment and their algorithms Alignment
Algorithms And Methodes The dot matrix method The word method The
dynamic programming method 23. Sequence alignment and their
algorithms Alignment Algorithms The dot matrix methodThe most basic
sequence alignment method is the dot matrix method, also known as
the dot plot method 24. Sequence alignment and their algorithms
Alignment Algorithms The word methodIt works by finding short
stretches of identical or nearly identical letters in two
sequences. These short strings of characters are called words,
which are similar to the windows used in the dot matrix method 25.
Sequence alignment and their algorithms Alignment Algorithms The
word method 26. Sequence alignment and their algorithms Alignment
Algorithms The dynamic programming methodDynamic programming is a
method that determines optimal alignment by matching two sequences
for all possible pairs of characters between the two sequences 27.
Sequence alignment and their algorithms Alignment Algorithms The
dynamic programming method Global alignmentThe classical global
pairwise alignment algorithm using dynamic programming is the
NeedlemanWunsch algorithm. In this algorithm, an optimal alignment
is obtained over the entire lengths of the two sequences Local
alignmentThe first application of dynamic programming in local
alignment is the SmithWaterman algorithm. In this algorithm,
positive scores are assigned for matching residues and zeros for
mismatches. No negative scores are used 28. Sequence alignment and
their algorithms substitution matrix PAM matrices (point accepted
mutation)The PAM matrices were subsequently derived based on the
evolutionary divergence between sequences of the same cluster. One
PAM unit is defined as 1% of the amino acid positions that have
been changed. Because of the use of very closely related homologs,
the observed mutations were not expected to significantly change
the common function of the proteins 29. Sequence alignment and
their algorithms substitution matrix PAM matrices (point accepted
mutation) 30. Sequence alignment and their algorithms substitution
matrix BLOSUM matricesThis is the series of blocks amino acid
substitution matrices (BLOSUM), all of which are derived based on
direct observation for every possible amino acid substitution in
multiple sequence alignments 31. Sequence alignment and their
algorithms substitution matrix BLOSUM matrices 32. Sequence
alignment and their algorithms What Matrices should be used and
when? Matrix PAM40Best use Similarity (%) Short alignment that are
70-90 highly similar PAM160 Detecting members of a 50-60 protein
family PAM250 Longer alignments of more App. 30 divergent sequences
BLUSOM90 Short alignment that are 70-90 highly similar BLUSOME80
Detecting members of a 50-60 protein family BLUSOME62 Most
effective in finding 30-40 all potential similarities BLUSOME30
Longer alignments of more
