Bioinformatics emerged from the marriage of computer science and molecular biology to analyze massive amounts of biological data, like that produced by the Human Genome Project. It uses algorithms and techniques from computer science to solve problems in molecular biology, like comparing genomic sequences to understand evolution. As genomic data exploded publicly, bioinformatics was needed to efficiently store, analyze, and make sense of this information, which has applications in molecular medicine, drug development, agriculture, and more.

1. Need & Emergence of the Field
Speaker
Shashi Shekhar
Head of computational Section
Biowits Life Sciences

2.  The marriage between computer science and
molecular biology
◦ The algorithm and techniques of computer science
are being used to solve the problems faced by
molecular biologists
 ‘Information technology applied to the
management and analysis of biological data’
◦ Storage and Analysis are two of the important
functions – bioinformaticians build tools for each.

3. Biology Chemistry
Computer
Science Statistics
Bioinformatics

4.  The need for bioinformatics has arisen from the recent
explosion of publicly available genomic information,
such as resulting from the Human Genome Project.
 Gain a better understanding of gene analysis,
taxonomy, & evolution.
 To work efficiently on the rational drug designs and
reduce the time taken for the development of drug
manually.

5.  To uncover the wealth of Biological information hidden
in the mass of sequence, structure, literature and
biological data.
 It is being used now and in the foreseeable future in the
areas of molecular medicine.
 It has environmental benefits in identifying waste and
clean up bacteria.
 In agriculture, it can be used to produce high yield, low
maintenance crops.

6.  Molecular Medicine
 Gene Therapy
 Drug Development
 Microbial genome applications
 Crop Improvement
 Forensic Analysis of Microbes
 Biotechnology
 Evolutionary Studies
 Bio-Weapon Creation

7.  In Experimental Molecular Biology
 In Genetics and Genomics
 In generating Biological Data
 Analysis of gene and protein expression
 Comparison of genomic data
 Understanding of evolutionary aspect of Evolution
 Understanding biological pathways and networks in
System Biology
 In Simulation & Modeling of DNA, RNA & Protein

8. e.g. homology
searches
Bioinformatics lecture
March 5, 2002
organisation of knowledge
(sequences, structures,
functional data)

9.  Prediction of structure from sequence
◦ secondary structure
◦ homology modelling, threading
◦ ab initio 3D prediction
 Analysis of 3D structure
◦ structure comparison/ alignment
◦ prediction of function from structure
◦ molecular mechanics/ molecular dynamics
◦ prediction of molecular interactions, docking
 Structure databases (RCSB)

11.  Sequence Similarity
 Tools used for sequence similarity searching
 There uses in biology or to us
 Databases
 Different types of databases

12.  One could align the sequence so that many
corresponding residues match.
 Strong similarity between two sequences is a strong
argument for their homology.
 Homology: Two(or more) sequences have a common
ancestor.
 Similarity: Two(or more) sequences are similar by some
criterion, and it does not refer to any historical process.

13.  To find the relatedness of the proteins or gene, if they
have a common ancestor or not.
 Mutation in the sequences, brings the changes or
divergence in the sequences.
 Can also reveal the part of the sequence which is crucial
for the functioning of gene or protein.

14.  Optimal Alignment: The alignment that is the best,
given a defined set of rules and parameter values for
comparing different alignments.
 Global Alignment: An alignment that assumes that the
two proteins are basically similar over the entire length
of one another. The alignment attempts to match them
to each other from end to end.
 Local Alignment: An alignment that searches for
segments of the two sequences that match well. There
is no attempt to force entire sequences into an
alignment, just those parts that appear to have good
similarity.
(contd.)

15.  Gaps & Insertions: In an alignment, one may achieve much
better correspondence between two sequences if one allows a
gap to be introduced in one sequence. Equivalently, one
could allow an insertion in the other sequence. Biologically
this corresponds to an mutation event.
 Substitution matrix: A Substitution matrix describes the two
residue types would mutate to each other in evolutionary
time. This is used to estimate how well two residues of given
types would match if they were aligned in a sequence
alignment.
 Gap Penalty: The gap penalty is used to help decide whether
or not to accept a gap or insertion in an alignment when it is
possible to achieve a good alignment residue to residue at
some other neighboring point in the sequence.

16.  Similarity indicates conserved function
 Human and mouse genes are more than 80% similar at
sequence level
 But these genes are small fraction of genome
 Most sequences in the genome are not recognizably similar
 Comparing sequences helps us understand function
◦ Locate similar gene in another species to understand your new
gene

17.  Match score: +1
 Mismatch score: +0
 Gap penalty: –1
ACGTCTGATACGCCGTATAGTCTATCT
||||| ||| || ||||||||
----CTGATTCGC---ATCGTCTATCT
 Matches: 18 × (+1)
 Mismatches: 2 × 0
 Gaps: 7 × (– 1)
Score = +11

18.  We want to find alignments that are evolutionarily likely.
 Which of the following alignments seems more likely to
you?
ACGTCTGATACGCCGTATAGTCTATCT
ACGTCTGAT-------ATAGTCTATCT 
ACGTCTGATACGCCGTATAGTCTATCT
AC-T-TGA--CG-CGT-TA-TCTATCT 
 We can achieve this by penalizing more for a new gap,
than for extending an existing gap

19.  Match/mismatch score: +1/+0
 Origination/length penalty: –2/–1
ACGTCTGATACGCCGTATAGTCTATCT
||||| ||| || ||||||||
----CTGATTCGC---ATCGTCTATCT
 Matches: 18 × (+1)
 Mismatches: 2 × 0
 Origination: 2 × (–2)
 Length: 7 × (–1)
Score = +7

20.  Alignment scoring and substitution matrices
 Aligning two sequences
◦ Dotplots
◦ The dynamic programming algorithm
◦ Significance of the results
 Heuristic methods
◦ FASTA
◦ BLAST
◦ Interpreting the output

21.  Examples:
 Staden: simple text file, lines <= 80 characters
 FASTA: simple text file, lines <= 80 characters, one line
header marked by ">"
 GCG: structured format with header and formatted
sequence
 Sequence format descriptions e.g. on
http://www.infobiogen.fr/doc/tutoriel/formats.html

22.  Local sequence comparison:
 assumption of evolution by point mutations
◦ amino acid replacement (by base replacement)
◦ amino acid insertion
◦ amino acid deletion
 scores:
◦ positive for identical or similar
◦ negative for different
◦ negative for insertion in one of the two sequences

23.  Simple comparison without alignment
 Similarities between sequences show up in 2D diagram

24. identity (i=j)
similarity of sequence
with other parts of itself

25.  The 1st alignment: highly significant
 The 2nd: plausible
 The 3rd: spurious
 Distinguish by alignment score
 Similarities increase score
substitution matrix
 Mismatches decrease score
 Gaps decrease score gap penalties

26.  Substitution matrix weights replacement of one residue
by another:
◦ Similar -> high score (positive)
◦ Different -> low score (negative)
 Simplest is identity matrix (e.g. for nucleic acids)
A C G T
A 1 0 0 0
C 0 1 0 0
G 0 0 1 0
T 0 0 0 1

27.  PAM matrix series (PAM1 ... PAM250):
◦ Derived from alignment of very similar sequences
◦ PAM1 = mutation events that change 1% of AA
◦ PAM2, PAM3, ... extrapolated by matrix multiplication
e.g.: PAM2 = PAM1*PAM1; PAM3 = PAM2 * PAM1 etc
 Problems with PAM matrices:
◦ Incorrect modelling of long time substitutions, since
conservative mutations dominated by single nucleotide
change
◦ e.g.: L <–> I, L <–> V, Y <–> F
long time: any Amino Acid change

28. positive and negative values
identity score depends on residue

29.  BLOSUM series (BLOSUM50, BLOSUM62, ...)
 derived from alignments of distantly related sequence
 BLOCKS database:
◦ ungapped multiple alignments of protein families
at a given identity
 BLOSUM50 better for gapped alignments
 BLOSUM62 better for ungapped alignments

30. Blosum62 substitution matrix

31.  Significance of alignment:
 Depends critically on gap penalty
 Need to adjust to given sequence
 Gap penalties influenced by knowledge of structure
etc.
 Simple rules when nothing is known (linear or affine)

32.  Dynamic programming = build up optimal alignment
using previous solutions for optimal alignments of
subsequences.
 The dynamic programming relies on a principle of
optimality. This principle states that in an optimal
sequence of decisions or choices, each subsequence
must also be optimal.
 The principle can be related as follows: the optimal
solution to a problem is a combination of optimal
solutions to some of its sub-problems.

33.  Construct a two-dimensional matrix whose axes are the
two sequences to be compared.
 The scores are calculated one row at a time. This starts
with the first row of one sequence, which is used to
scan through the entire length of the other sequence,
followed by scanning of the second row.
 The scanning of the second row takes into account the
scores already obtained in the first round. The best
score is put into the bottom right corner of an
intermediate matrix.
 This process is iterated until values for all the cells are
filled.

34. Contd.

35. Contd.

36.  The results are traced back through the matrix in
reverse order from the lower right-hand corner of the
matrix toward the origin of the matrix in the upper left-
hand corner.
 The best matching path is the one that has the
maximum total score.
 If two or more paths reach the same highest score, one
is chosen arbitrarily to represent the best alignment.
 The path can also move horizontally or vertically at a
certain point, which corresponds to introduction of a
gap or an insertion or deletion for one of the two
sequences.

38.  Global alignment (ends aligned)
◦ Needleman & Wunsch, 1970
 Local alignment (subsequences aligned)
◦ Smith & Waterman, 1981
 Searching for repetitions
 Searching for overlap

40.  Multi-step approach to find high-scoring alignments
 Exact short word matches
 Maximal scoring ungapped extensions
 Identify gapped alignments

41. Contd.

43.  FASTA also uses E-values and bit scores. The FASTA output
provides one more statistical parameter, the Z-score.
 This describes the number of standard deviations from the
mean score for the database search.
 Most of the alignments with the query sequence are with
unrelated sequences, the higher the Z-score for a reported
match, the further away from the mean of the score
distribution, hence, the more significant the match.
 For a Z-score > 15, the match can be considered extremely
significant, with certainty of a homologous relationship.
 If Z is in the range of 5 to 15, the sequence pair can be
described as highly probable homologs.
 If Z < 5, their relationships is described as less certain.

44.  Multi-step approach to find high-scoring alignments
 List words of fixed length (3AA) expected to give score
larger than threshold
 For every word, search database and extend ungapped
alignment in both directions
 New versions of BLAST allow gaps

45. Contd.

47.  The E-value provides information about the likelihood that a
given sequence match is purely by chance. The lower the E-
value, the less likely the database match is a result of random
chance and therefore the more significant the match is.
 If E < 1e − 50 (or 1 × 10−50), there should be an extremely
high confidence that the database match is a result of
homologous relationships.
 If E is between 0.01 and 1e − 50, the match can be considered
a result of homology.
 If E is between 0.01 and 10, the match is considered not
significant, but may hint at a tentative remote homology
relationship. Additional evidence is needed.
 If E > 10, the sequences under consideration are either
unrelated or related by extremely distant relationships that fall
below the limit of detection with the current method.

48.  Various versions:
 Blastn: nucleotide sequences
 Blastp: protein sequences
 tBlastn: protein query - translated database
 Blastx: nucleotide query - protein database
 tBlastx: nucleotide query - translated database

49.  Very fast growth of biological data
 Diversity of biological data:
◦ Primary sequences
◦ 3D structures
◦ Functional data
 Database entry usually required for publication
◦ Sequences
◦ Structures
 Database entry may replace primary publication
◦ Genomic approaches

50. Nucleic Acid Protein
EMBL (Europe) PIR -
Protein Information
Resource
GenBank (USA) MIPS
DDBJ (Japan) SWISS-PROT
University of Geneva,
now with EBI
TrEMBL
A supplement to SWISS-
PROT
NRL-3D

51.  Three databanks exchange data on a daily basis
 Data can be submitted and accessed at either location
 GenBank
◦ www.ncbi.nlm.nih.gov/Genbank/GenbankOverview.html
 EMBL
◦ www.ebi.ac.uk/embl/index.html
 DNA Databank of Japan (DDBJ)
◦ www.nig.ac.jp/home.html

52.  As there are many databases which one to search? Some
are good in some aspects and weak in others?
 Composite databases is the answer – which has several
databases for its base data
 Search on these databases is indexed and streamlined
so that the same stored sequence is not searched twice
in different databases.

53.  OWL has these as their primary databases.
◦ SWISS PROT (top priority)
◦ PIR
◦ GenBank
◦ NRL-3D

54.  Store secondary structure info or results
of searches of the primary databases.
Composite Primary Source
Databases
PROSITE SWISS-PROT
PRINTS OWL

55.  We have sequenced and identified genes. So we
know what they do.
 The sequences are stored in databases.
 So if we find a new gene in the human genome we
compare it with the already found genes which are
stored in the databases.
 Since there are large number of databases we cannot
do sequence alignment for each and every sequence
 So heuristics must be used again.

56.  Applications:-
Bioinformatics joins mathematics, statistics, and computer
science and information technology to solve complex
biological problems.
 Sequence Analysis:-
The application of sequence analysis determines those genes
which encode regulatory sequences or peptides by using the
information of sequencing. These computers and tools also
see the DNA mutations in an organism and also detect and
identify those sequences which are related. Special software
is used to see the overlapping of fragments and their
assembly.
Contd.

57.  Prediction of Protein Structure:-
It is easy to determine the primary structure of proteins
in the form of amino acids which are present on the
DNA molecule but it is difficult to determine the
secondary, tertiary or quaternary structures of proteins.
Tools of bioinformatics can be used to determine the
complex protein structures.
 Genome Annotation:-
In genome annotation, genomes are marked to know
the regulatory sequences and protein coding. It is a very
important part of the human genome project as it
determines the regulatory sequences.

58.  Comparative Genomics:-
Comparative genomics is the branch of bioinformatics
which determines the genomic structure and function
relation between different biological species. For this
purpose, intergenomic maps are constructed which
enable the scientists to trace the processes of evolution
that occur in genomes of different species.
 Health and Drug discovery:-
The tools of bioinformatics are also helpful in drug
discovery, diagnosis and disease management.
Complete sequencing of human genes has enabled the
scientists to make medicines and drugs which can
target more than 500 genes.