Bioinformatics Course - Mars at UMHB

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1 Οκτ 2013 (πριν από 4 χρόνια και 1 μήνα)

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SYNOPSIS OF BIOINFORMATICS COURSE

INTRODUCTION


Significant discoveries of m
olecular
data

(both

DNA and protein
s) have been made
in recent years. As a consequence, c
omputational methods
have become

essential

t
o molecular biology research

into the s
tructure, mechanism and function

of
biolog
ical systems
.
This course seeks to provide
an

introduction to the subject

of Bioinformatics
. Emphasis will be

on concepts and princ
iples, combined with
hands
-
on (
keyboard)

applications.




PREREQUISITES


This interdisciplinary course is directed at studen
ts of biology, medicine,

computer science, statistics, operations research, or

mathematics. There are no
formal prerequisites as all

n
ecessary knowledge will be developed in the course.
However,

some knowledge of the fundamental concepts of molecular

biol
ogy and
statistical analysis will be helpful
.


COURSE
OBJECTIVES



The main goal of this course
will be

to provide
students of
biology an initial
exposure to informatics
. Given that numerous

bioinformatics tools
have become
available, it
is
increasingly im
portant for
students of

biology to be able to
manage
molecular
data, implement
new

ideas, and judge the usefulness of new
algorithms and software.



This course will present an overview of important applications of computers to
solve problems in biology. T
o that end, this course
will emphasize fundamental
aspects of
a specific computational tool essential for molecular biology
research, i.e.
Basic Local Alignment Search Tool (BLAST)
.
Utilizing a Beowulf
Linux Clustered Computer
,

students will learn to f
ind

regions of loca
l
similarity between sequences,
compare nucleotide or protein sequences to
se
quence databases and calculate
the statisti
cal significance of matches
.

Sequence alignments provide a powerful way to compare novel sequences with
previously charac
terized genes.


Topics
to be covered
will include:


-
information retrieval via BLAST

-
statistics of sequence patterns

-
basics of machine learning for molecular biology

-
pairwise sequence alignment

-
comparison with sequence databases

-
finding sequence
motifs

-
finding protein coding regions

-
finding genes

-
clustering genes by expression

-
prediction of macromolecular properties

-
retrieving and displaying macromolecular structures