This document provides an introduction and overview of the field of bioinformatics. It discusses how bioinformatics combines computer science and biology to analyze large amounts of biological data. Specifically, it mentions that bioinformatics uses algorithms and techniques from computer science to solve complex biological problems related to areas like molecular biology, genomics, drug discovery, and more. It also outlines some of the key applications of bioinformatics like sequence analysis, protein structure prediction, genome annotation, and comparative genomics. Finally, it provides brief descriptions of important biological databases and resources that bioinformaticians use to store and analyze genomic and protein sequence data.

1. Concepts of
Bioinformatics

2. Introduction to Bioinformatics
 Combined
to solve
complex
Biological problems
 Algorithms and techniques of computer science 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 important functions – bioinformaticians build tools for
each
 Bio IT market has observed significant growth in genomic era
Biology
Chemistry
Statistics
Computer
science
Bioinformatics

3. Fields of Bioinformatics
 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
development of drug manually.
 Unravel the wealth of Biological information hidden in mass of sequence,
structure, literature and biological data
 Has environmental-clean up benefits
 In agriculture, it can be used to produce high productivity crops
 Gene Therapy
 Forensic Analysis
 Understanding biological pathways and networks in System Biology

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

5. Applications of Bioinformatics
 Provides central, globally accessible databases that enable scientists to submit, search and analyze
information and offers software for data studies, modelling and interpretation.
 Sequence Analysis:-
The application of sequence analysis determines those genes which encode regulatory
sequences or peptides by using the information of sequencing. These computational tools
also detect the DNA mutations in an organism and identify those sequences which are related.
Special software is used to see the overlapping of fragments and their assembly.
 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

6.  Comparative Genomics:-
Comparative genomics is the branch of
bioinformatics which determines the
genomic structure and function relation
between different biological species
which enable the scientists to trace the
processes of evolution that occur in
genomes of different species.
 Pharmaceutical Research:-
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. Accurate
prediction in screening.

7. S.
No
Unix Windows Linux
1. Open source Close source Open source
2. Very high security system Low security system High security system
3. Command-line GUI Hybrid
4. File system is arranged in
hierarchical manner
File system is arranged in parallel
manner
File system is arranged in
hierarchical manner
5. Not user friendly User friendly User friendly
6. Single tasking Multi tasking Multi tasking

9. Biological databanks and databases
 Very fast growth of biological
data
 Diversity of biological data:
o Primary sequences
o 3D structures
o Functional data
 Database entry usually
required for publication
o Sequences
o Structures
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
Major primary databases

10. Sequence Databases
 Three databanks exchange data on a daily basis
 Data can be submitted and accessed at either location
Nucleotides db:
 GenBank - https://www.ncbi.nlm.nih.gov/
 EMBL - https://www.ebi.ac.uk/
 DDBJ - https://www.ddbj.nig.ac.jp/index-e.html
Bibliographic db:
 PubMed , Medline
Specialized db:
 RDP, IMGT, TRANSFAC, MitBase
Genetic db:
 SGD – https://www.yeastgenome.org/
ACeDB, OMIM

11. Composite Databases Secondary Databases
 Swiss Prot
 PIR
 GenBank
 NRL-3D
 Store structure info or results
of searches of the primary
databases
Composite Databases Primary Source
PROSITE
https://prosite.expasy.org/
SWISS-PROT
PRINTS
http://130.88.97.239/PRINTS/index.p
hp
OWL

12. Biological Database
Carbohydrate Structure Database:
 CCSD – https://cordis.europa.eu/project/id/BIOT0184
https://www.genome.jp/dbget-bin/www_bfind?carbbank
 Glycome DB – http://www.glycome-db.org/
Metabolic or Enzyme Databases
 BRENDA – https://www.brenda-enzymes.org/
 KEGG – https://www.genome.jp/kegg/
Structure Databases:
 CATH – https://www.cathdb.info/
 SCOP- http://scop.mrc-lmb.cam.ac.uk/

13. SCOP
 Structural Classification of Proteins
 http://scop.mrc-lmb.cam.ac.uk/
 SCOP database aims to provide a detailed and comprehensive description of
structural and evolutionary relationship between all proteins
 Levels of hierarchy
 Family : Pairwise residue identities of aa 30% or greater
 Superfamily : Eventhough low seq identities, should have common
evolutionary origin
Eg: ATPase domain of HSP and HK
 Fold : Major structural similarity
 Class : all α , all β, α or β, α and β, Multidomain

14. CATH
 https://www.cathdb.info/
 Class : 2º structure
 Architecture : Gross orientation of 2º structure, independent of connectivities
 Topology (fold family) : topological connection of super families
 S level : Sequence and structural identities

15. Basis of Sequence Alignment
1. Aligning sequences
2. To find the relatedness of the proteins or gene, if they have a
common ancestor or not.
3. Mutation in the sequences, brings the changes or divergence in the
sequences.
4. Can also reveal the part of the sequence which is crucial for the
functioning of gene or protein.
 Similarity indicates conserved function
 Human and mouse genes are more than 80% similar
 Comparing sequences helps us understand function

16. Sequence Alignment
 After obtaining nuc/aa sequences, first thing is to compare with the known sequences.
Comparison is done at the level of constituents. Then finding of conserved residues to predict
the nature and function of the protein. This process of mapping is called
Sequence Alignment
1. Local alignment – Smith & Waterman Algorithm
2. Global alignment – Needleman & Wunch Algorithm
 Gapped Alignment
 Ungapped Alignment
 Terms to Know - Homolog, Ortholog, Paralog, Xenolog, Similar and Identical
Alignment scoring and substitution matrices
Dot plots
Dynamic programming algorithm
Heuristic methods (In order to reduce time)
FASTA
BLAST
Pairwise sequence alignment
Multiple sequence alignment

17. Scoring a sequence alignment
Match score:
Mismatch score:
Gap penalty:
+ 1
+ 0
–1
Matches: 18 × (+1)
Mismatches: 2 × 0
Gaps: 7 × (– 1)
Score = +11
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
 Maximum no of matches gives high similarity – Optimum Alignment
ACGTCTGATACGCCGTATAGTCTATCT
||||| ||| || ||||||||
----CTGATTCGC---ATCGTCTATCT

18.  Scores:
 positive for identical or similar
 negative for different
 negative for insertion in one of the two sequences
 Substitution matrices – weights replacement of one residue by another
 assumption of evolution by point mutations
 amino acid replacement (by base replacement)
 amino acid insertion
 amino acid deletion
 Significance of alignment
 Depends critically on gap penalty
 Need to adjust to given sequence

19. Derivation of substitution matrices
PAM matrices
 First substitution matrix; Developed by Dayhoff (1978) based on Point
Accepted Mutation (PAM) model of evolution
 1PAM (without sub) is a unit of evolutionary divergence in which 1% of the aa
have been changed
 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
 Lower distance PAM matrix for closely related proteins eg., PAM30
 Higher distance PAM matrix for highly diverged sequences eg., PAM250
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

20. positive and negative values identity score depends on residue
 positive and negative
values identity score
depends on residue

21. BLOSUM matrices
 BLOCKSAmino acid Substitution Matrices
 Similar as PAM; however the data were derived from local alignments for distantly related proteins
deposited in BLOCKS db
 Unlike PAM there is no evolutionary basis
 BLOSUM series (BLOSUM50, BLOSUM62, ...)
 BLOCKS database:
 ungapped multiple alignments of protein families at a given identity
E.g.,
BLOSUM 30 better for gapped alignments – for comparing highly diverged seq
 BLOSUM 90 better for ungapped alignments – for very close seq
 BLOSUM 62 was derived from a set of sequences which are 62% or less similar

22. DOT Plot
 Simple comparison without alignment
 2D graphical representation method primarily used for finding regions of
local matches between two sequences
 DOTTER, PALIGN, DOTLET (https://dotlet.vital-it.ch/)
 Distinguish by alignment score
 Similarities increase score (positive)
 Mismatches decrease score (Negative)
 Gaps decrease score
Number of possible dots = (probability of pair) x (length of seq A) x (length of seq B)
Disadv – No direct seq homology & Statistically weak

23. Dynamic programming algorithm
 To build up optimal alignment which maximizes the similarity we need some scoring
methods
 The dynamic programming relies on a principle of optimality.
PROCEDURE
 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.

24. Depicting the results:
 Back tracing
 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.

25. BLAST
 Basic Local Alignment search tool
 https://blast.ncbi.nlm.nih.gov/Blast.cgi
 Multi-step approach to find high-scoring local alignments between
two sequences
 List words of fixed length (3AA) (11nuc) expected to give score larger
than threshold (seed alignment)
 For every word, search database and extend ungapped alignment in
both directions upto a certain length to get HSPs
 New versions of BLAST allow gaps
Blastn:
Blastp:
tBlastn:
Blastx:
tBlastx:
nucleotide sequences
protein sequences
protein query - translated database
nucleotide query - protein database
nucleotide query - translated database

27. Interpretation
 Rapid and easier to find homolog by scanning huge db
 Search against specialized db
 Blast program employ SEG program to filter low complexity regions before
executing db search
 Quality of the alignment is represented by score (to identify hits)
 Significance of the alignment is represented as e-value (Expected value)
 E-value decreases exponentially as the score increases
 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 and therefore more significant the match is.
 If E is between 0.01 and 10, the match is considered not significant.

28. FASTA
 More sensitive than BLAST
 Table to locate all identically matching words of
length Ktup between two sequences
 Blast – Hit extension step
 Fasta – Exact word match
 As the high value of Ktup increases the search
becomes slow
 FASTA also uses E-values and bit scores. The FASTA
output provides one more statistical parameter,
the Z-score.
 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

29. Phylogenetics
 Phylogenetics is the study of evolutionary relatedness among various groups of
organisms (e.g., species, populations).
 Methods of Phylogenetic Analysis:
 Monophyletic group – all taxa share by one common ancestor
 Paraphyletic groping – share common ancestor but not all
 Errors in alignment mislead tree
Phenetic
NJ,
UPGMA
Cladistic
MP
ML

30.  A phylogenetic tree is a tree showing the
evolutionary interrelationships among various
species or other entities that are believed to
have a common ancestor. A phylogenetic tree
is a form of a cladogram. In a phylogenetic
tree, each node with descendants represents
the most recent common ancestor of the
descendants, and edge lengths correspond to
time estimates.
 Each node in a phylogenetic tree is called a
taxonomic unit. Internal nodes are generally
referred to as Hypothetical Taxonomic Units
(HTUs) as they cannot be directly observed
 Distances – no of changes
Parts of a phylogenetic tree
Node
Root
Outgroup
Ingroup
Branch

31. Phenetic Method of analysis:
 Also known as numerical taxonomy
 Involves various measures of overall similarity for ranking species
 All the data are first converted to a numerical value without any character
(weighing). Then no of similarities / differences is calculated.
 Then clustering or grouping close together
 Lack of evolutionary significance in phenetics
Cladistic method of analysis:
 Alternative approach
 Diagramming relationship between taxa
 Basic assumption – members of the group share a common evolutionary
history
 Typically based on morphological data

32. Distance and Character
A tree can be based on
 1. quantitative measures like the distance or similarity between species, or
 2. based on qualitative aspects like common characters.
 Molecular clock assumption – substitution in nu / aa are being compared at constant rate

33. Maximum Parsimony:
 Finds the optimum tree by minimizing the number of evolutionary changes
 No assumptions on the evolutionary pattern
 MSA then scoring
 Rather time consuming works well if seq have strong similarity
 May oversimplify evolution
 May produce several equally good trees
 PAUP, MacClade
Maximum Likelihood:
 The best tree is found based on assumptions on evolution model
 Nucleotide models more advanced at the moment than aminoacid models
 Programs require lot of capacity from the system

34. Neighbour Joining:
 The sequences that should be joined are chosen to give the best least-squares estimates of the
branch length that most closely reflect the actual distances between the sequences
 NJ method begins by creating a star topology in which no neighbours are connected
 Then tree is modified by joining pair of sequences. Pair to be joined is chosen by calculating
the sum of branch length
 Distance table
 No molecular clock assumed
UPGMA
 Unweighted Pair Group method with Arithmetic Mean
 Works by clustering, starting with more similar towards distant
 Dot representation
 Molecular clock assumed

35. PHYLIP (Phylogeny Inference Package)
 Available free in Windows/MacOS/Linux systems
 Parsimony, distance matrix and likelihood methods (bootstrapping and
consensus trees)
 Data can be molecular sequences, gene frequencies, restriction sites and
fragments, distance matrices and discrete characters