Molekuláris bionika és infobionika szakok tananyagának komplex

Introduction to Bioinformatics 

A Course Material 

András Budinszky, Péter Gál, Sándor Pongor 

Pázmány Péter Catholic University 

Faculty of Information Technology 

Budapest, Hungary 

[e-mail], [e-mail], [e-mail] 

Summary – In this paper we discuss the de-

velopment of an introductory course for bioin-

formatics. We list the necessary requirements, 

the competency aimed to achieve, the topics 

covered by the chapters, and finally some con-

siderations for teaching. 

Keywords - molecular biology; component; 

bioinformatics; course 

I.

I

NTRODUCTION

One way bioinformatics can be broadly de-

fined as the management of the life sciences. 

As  an  applied  science  it  uses  computer  pro-

grams  to  process  data  archived  by  modern 

molecular  biology  and  thus  to  derive  useful 

new information. 

The  importance  of  bioinformatics  has 

grown  enormously  in  the  last  decade  due  to 

the  advance  and  development  of  high-

throughput data acquisition methods primarily 

that of sequencing. High-throughput sequenc-

ing  techniques  (e.g.  next  generation  DNA 

sequencing)  generate  a  flood  of  valuable  se-

quence data which is a challenge for the scien-

tists. 

The aim of developing this course material 

is to provide the students basic knowledge in 

bioinformatics.  The  subject  of  this  course  is 

meant  to  strengthen  the  bioinformatics  prob-

lem  solving  competency  of  the  students  as 

well as their ability to communicate with life 

science  professionals  who  are  the  ultimate 

users of bioinformatics. By taking this course, 

the  students  should  be  able  to  determine  the 

types  of  questions  the  computer  programs 

(“tools”)  –  developed  to  work  with  genome 

and protein data archives – can answer, and to 

use these tools to gain answers to such ques-

tions.

In addition, our teaching material can also 

be  useful  for  biologists  who  want  to  under-

stand  the  algorithms  that  are  behind  the  fre-

quently  used  applications  of  the  net  (e.g. 

BLAST). 

A.

Prerequisites 

Students  are  supposed  to  have  taken  a 

course  on  molecular  biology  and  have  some 

basic knowledge of biochemistry and molecu-

lar biology. Nevertheless, at the beginning of 

the  course  a  biology  primer  summarizes  the 

biological  fundamentals  necessary  for  this 

course so the students can start from an equal 

level. 

They should have also completed an intro-

ductory  database  course  since  the  data  to  be 

processed  by  the  bioinformatics  tools  are 

stored in databases.

In  addition,  the  students  definitely  should 

be competent computer users; however, we do 

not  require  knowledge  of  any  specific  pro-

gramming  language,  because  during  the 

presentations  very  few  algorithm  details  are 

discussed and – when they are – they are pro-

vided  in  a  so-called  pseudo  language  which 

can  be  understood  without  any  programming 

background.

B.

Some considerations 

Bioinformatics  is  a  relatively  new  area  of 

science; consequently it is a novel subject of 

teaching. 

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In  developing  the  teaching  material  we 

used  the  latest  editions  of  standard  bioinfor-

matics textbooks, and the numerous websites 

of  universities,  research  institutes  and  public 

databases  (e.g.  NCBI)  related  to  this  subject. 

Of  course  we  also  used  our  experience  in 

teaching bioinformatics which has accumulat-

ed during the last decade.  

Our  approach  is  somewhat  different  from 

the conventional way of teaching bioinformat-

ics. As our referee wrote “… this is the first 

comprehensive bioinformatics course in Hun-

gary  which  is  suitable  for  teaching  students 

who  have  only  basic  knowledge  of  biology. 

For  the  first  time  the  teaching  material  col-

lects the algorithms used in bioinformatics in 

a  way  which  is  understandable  not  only  for 

mathematicians. After the course the students 

will  be  able  to  understand,  apply  and  even 

further develop the most frequently used bio-

informatics algorithms.” 

The  choice  of  topics  in  bioinformatics  is 

very wide. Since this course is limited to one 

semester,  we  had  to  restrict  ourselves  to  an 

essential  core  of  material  covering  the  most 

standard bioinformatics tasks and had to leave 

some  areas  untouched  (e.g.  drug  discovery, 

protein structure). 

Another  but  smaller  scale  problem:  bioin-

formatics is not standardized and – depending 

on the authors – the meaning of terms might 

change somewhat. In the associated terminol-

ogy file we provide the meanings for terms we 

found most commonly accepted. 

II.

R

ESULT

During the development of the course ma-

terial we created 12 chapters with 465 slides. 

A  number  of  the  chapters  were  necessary 

to  be  developed  for  providing  background 

information: 

either 

biology/database 

knowledge, or detailed method descriptions of 

various biological data collections. 

The  first  two  chapters  provide  reviews  of 

molecular  biology  and  databases.  This  helps 

students  to  get  on  an  equal  level  of  the  pre-

requisites.

Chapter  3  and  4  cover  the  most  widely 

known  areas  of  bioinformatics,  namely  the 

sequence alignment algorithms and the strate-

gies  of  BLAST  in  details.  The  students  are 

thought a couple of particularly key points in 

these chapters: 



The  cost  and  the  importance  of  ex-

pected execution time is introduced. 



The difference between exhaustive al-

gorithms  (Needleman-Wunsch  and 

Smith-Waterman)  and  heuristic  algo-

rithms  (FASTA  and  BLAST)  is  em-

phasized.

The fifth chapter deals with the generation 

of  DNA  databases:  DNA  cloning  and  se-

quencing.  The  students  can  have  an  insight 

into the most frequently used molecular biol-

ogy methods to manipulate DNA.  

The  sixth  chapter  summarizes  our  current 

knowledge  of  proteomics.  Proteomics  is  a 

brand  new  subject  since  the  high-throughput 

methods  for  analyzing  proteomes  are  lagged 

behind the methods of DNA analysis. Howev-

er, it is not difficult to predict that proteomics 

will  be  one  of  the  most  important  areas  of 

bioinformatics in the future 

Chapter 7 discusses the different DNA and 

protein sequencing algorithms. 

In  Chapter  8  we  give  a  picture  about  the 

methods  suitable  for  analyzing  gene  expres-

sion. The most important method is the DNA 

microarray  which  is  discussed  in  detail.  We 

also deal with the more conventional methods 

(e.g.  EST  databases)  and  the  application  of 

gene expression data. 

The  ninth  chapter  details  different  algo-

rithms that can be used for gene prediction in 

DNA sequences.  

Chapter  10  discusses  how  various  data 

mining  techniques  are  used  for  clustering 

genes based on their functionalities.  

12

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