Welcome toIntroduction to Bioinformatics Computing

aka

BIC1

Team taught by

• Rhys Price Jones, Ph.D.– rpjavp@rit.edu– Bldg. 7B-2250; 5-5866– Office Hours: Monday, Wednesday, Friday 10-

11am

• Anne R. Haake, Ph.D.– arh@it.rit.edu– Bldg. 70-2325; 5-5365– Office Hours: Tuesday 2-4 p.m; Friday 10-noon

The Focus of Bioinformatics

• Using computers to answer biological questions– Storage– Visualization– Analysis

• Using computers to figure which biological questions to ask

What is this course about?

• We will focus on analysis:

– We will study techniques for quickly and effectively commandeering computing resources to the solution of problems raised in the realm of biology

– We will study algorithms (more on this later..) that underlie many of the popular bioinformatics software packages

• The majority of these algorithms are concerned with sequence analysis (more on this, too…)

The Context of Bioalgorithms

• It is important to keep in mind that a mathematically perfect solution to an ideally posed problem may not be the most biologically relevant

• We need a flexibility, a willingness to rephrase the question, to rethink the process, to adapt and re-adapt

Course Structure

• 3 Classroom sessions each week to introduce the biological perspective and computational approaches for each biological problem

• 1 Laboratory session to give you hands-on experience in applying and refining computational methods in the context of biology

Readings

• Textbook:– Algorithms on Strings Trees and Sequences, Computer

Science and Computational Biology, Dan Gusfield, Cambridge University Press, 1997, ISBN 0 521 58519 8

• Papers from the current literature, as assigned• Lecture notes and lab manuals as posted and linked

to from the course home page• Note that, unless otherwise noted, net-based

resources should be accessed using Netscape. Other browsers may not be able to correctly interpret the JavaScript code.

Expectations – Computing Background

• There are skills you should possess in part already, but which will be significantly enhanced by being exercised in this course:– identifying and clearly phrasing a computational

problem from a general biological query– rapidly developing, testing and analyzing tools for

the solution of such problems if necessary– locating existing tools if not– understanding the capabilities and limitations of

such tools

Computing Background – Specific skills

• Programming in a language such as Lisp, Perl, Scheme, Java, C, Python, etc. (if in doubt, ask!)

• Static and dynamic data structures – arrays, lists, trees, etc.

• Control structures, especially recursion• Rapid prototyping, careful version control• Understanding of mathematics for:

– analysis– proof– modeling

Biological Motivation

• The fundamental building blocks of life are proteins– Enzymes, structural proteins, transport molecules,

antibodies

• 100,000 or so different proteins in a human• Their properties and interactions are what

make us what we are

Biological Motivation

• What are proteins?– Polymers of amino acids (20 different)– Sequence of these amino acids (primary structure)

determines the protein’s shape (secondary and tertiary structures)

– Protein shape and chemical composition it’s amino acids determine protein function

Figure from W. Gilbert, Ph.D New Hampshire Biotech. Center

So…in theory, we can infer protein function if we know the protein sequence

Biological Motivation

• How do we find out protein sequence?– Can sequence proteins directly but this has been

technically difficult– Determine protein sequence from the DNA

sequences that encode them

The Central Dogma

Hereditary information for a complete individual stored in the DNA,which is self-replicating, and is organized into units of expression (genes)

A gene is expressed in 2 steps:

DNA is transcribed into RNA

RNA is translated into protein

Most Protein Sequences Are Determined From DNA Sequence

• Why?• Availability of DNA sequence information

– Rapid development of DNA sequencing technology

– Genomes of many different species have now been sequenced

• Difficulties?– Data sets are large– Cellular pathway from DNA to RNA to protein can

be complicated

Some Genomes

• E. coli 4.6 x 106 bases– Approx. 4,000 genes

• Yeast15 x 106 bases– Approx. 6,000 genes

• Smallest human chromosome 50 x 106 bases• Human 3 x 109 bases

– Approx. 30,000 genes ?

The Computational Approach

• The nucleotide sequence of a genome contains all information necessary to produce a functional organism

• Therefore, we should, in theory, be able to duplicate this decoding using computers

Why Use Computational Techniques?

• The datasets are too large to analyze by hand

• Efficient algorithms are the only way to perform the analyses that we need to answer the biological questions

Common Biological Questions Answered Through Sequence Analysis

• Determine if an interesting DNA sequence has been seen by anyone else

• Find all the protein coding regions in a genome• Infer the function of a new gene from a known one by

matching two amino acid sequences• Measure the evolutionary distance between species• Predict local secondary structure of a peptide

sequence, predict protein conformation, predict function

• Study protein families

Many Molecular Biology Problems on Sequences Can Be Formulated As String

Matching Problems• Comparing two or more strings for similarities• Searching databases for related strings• Looking for new patterns occurring frequently

in DNA• Reconstructing long strings of DNA from

overlapping string fragments• And more…

We Will Be Studying Algorithms For:

• Exact string matching• Inexact string matching• Sequence alignment problems• Multiple alignment problems• And more…

Role of Evolutionary Theory

• Central to computational biology• Evolution is descent with modification, driven

by:– Diversity: different individuals carry different

variants of the basic blueprint– Mutations: DNA sequence can be changed – Selection bias

Role of Evolutionary Theory

• Related organisms have:– similar DNA– similar protein sequences– similar organization of genes

• Similar structures tend to have similar functions

• The bottom line:– evolution is the reason that we can assume

similarity is meaningful in computational biology