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Bioinformatics
Sean Langford, Larry Hale
What is it?
 Bioinformatics
is a scientific field involving
many disciplines that focuses on the
development of methods for storing,
retrieving, organizing, and analyzing data
from biological sources, usually sources
that are of a cellular or genetic nature.
What is it? (cont.)
A
major focus in bioinformatics is the
production of useful software tools for
generating biological knowledge using
advanced skills in a variety of computer
science, mathematics and engineering
fields.
What it does
 Bioinformatics
uses a variety of techniques
to develop software tools useful in
producing valuable biological
knowledge.
 Similar to Biological Computation.
History
 Bioinformatics
was coined by Paulien
Hogeweg in 1970 and referred to the
study of information processes in
biological systems.
 Computers became necessary in the field
of molecular biology when protein
sequences became available in the 1950s
and in genetics in 1982 as more genome
sequences became available.
Bioinformatics vs. Biological
Computation
 The
two fields have similar aims but the
major difference is in scale.
 Bioinformatics deals with basic biological
data and pays attention to details while
Biological Computation is a subset of CS
that builds large scale theoretical models
of biological systems in an attempt to
expand understanding of these systems in
an abstract view.
Goals
 Bioinformatics
is now focused on the
creation and advancement of
databases, algorithms, computational
and statistical techniques, and theory to
solve formal and practical problems
arising from the management and
analysis of biological data.
Goals (cont.)
 Some
problems thus far addressed in the
pursuit of the current goal of
Bioinformatics involve the production of
GMOs in order to protect crops and
provide gene therapy for a variety of
genetic disorders.
Algorithms
 Rather
than to list specific algorithms
used, it is more appropriate to consider
what algorithms and types of algorithms
that are not used.
 Bioinformatics as a field is considerably
broad and uses a large number of
algorithms to accomplish an extremely
large number of tasks.
Analyzing Data
 Very
important goal of Bioinformatics at
the moment.
 Uses algorithms involving Artificial
Intelligence, Soft Computing, Data
Mining, Image Processing, and Simulation.
 Heavily uses Discrete Mathematics and
Statistics.
Practical Example
A
prime example of an algorithm used in
Bioinformatics is LZW algorithm for the
compression and decompression of
genetic strings in order to more efficiently
store the information.
 A demonstration of this usage was seen in
problem four of our Homework.
Problem 4
 DNA
sequences use the alphabet {A, C,
G, T}. Use LZW algorithm to compress the
following DNA sequence:
 ATGGAAGGAACTAATGGCCACCAAAAC
GGTTCATTTTGCTTGTCCACTGCCAAGGG
AAATAATGATCCCTTGAACTGGGGAGCG
GCGGCGGAGGCA
Compression
 Answer
as best agreed on by presenters:
 0, 3, 2, 2, 0, 0, 6, 8, 1, 3, 8, 5, 2, 1, 1, 0, 17, 8,
11, 6, 3, 3, 18, 24, 24, 16, 28, 25, 18, 12, 16,
18, 9, 14, 7, 31, 12, 5, 11, 15, 10, 16, 6, 1, 48,
16, 0
Practical Example Part 2
 Another
example of a common algorithm
used in Bioinformatics is the use of
algorithms designed to find the longest
common subsequence.
 This is also demonstrated within the
homework in problem 1.
Problem 1
 Given
the following DNA sequences, S1 =
(ATGGAACGAACT) and
S2=(TTGCCAGTAC), find the Longest
Common Subsequence (LCS) of S1 and
S2 using the table below. Show the values
and arrows in each cell and circle the
cells that are part of the LCS. Write the
LCS.
Longest Common
Subsequence
 Answer
Best Agreed on by presenters.
 T- G- - - G- AC
Questions?
 Dost
thou haveth any?