Tentative definition of bioinformatics

1
Tentative definition of bioinformatics

• Bioinformatics, often also called genomics,
  computational genomics, or computational biology,
  is a new interdisciplinary field at the
  intersection of biology, computer science,
  statistics, and mathematics. Its subject matter
  is the extraction of biologically useful
  information from large sets of molecular data,
  such as DNA or protein sequence data or gene
  expression data. The term bioinformatics is
  currently used mainly to refer to the extraction
  of information from sequence data, while the
  creation and analysis of gene expression data is
  called functional genomics.

2
Biologys dilemma There is too much to know
about living things

• Roughly 1.5 million species of organisms have
  been
• described and given scientific names to date.
  Some
• biologists estimate that the total number of all
  living
• species may be several times higher. It is
  impossible to
• learn everything about all these organisms.
  Biologists
• solve the dilemma by focusing on some species,
  so-called
• model organisms, and trying to find out as much
  as they
• can about these model organisms.

3
Some important model organisms

• Mammals Human, chimpanzee, mouse, rat
• Fish Zebrafish, Pufferfish
• Insects Fruitfly (Drosophila melanogaster)
• Roundworms Ceanorhabditis elegans
• Protista Malaria parasite (Plasmodium
  falciparum)
• Fungi Bakers yeast (Saccharomyces cerevisiae)
• Plants Thale cress (Arabidopsis thaliana),
  corn, rice
• Bacteria Escherichia coli, Mycoplasma genitalis
• Archea Methanococcus janaschii

4
Lets find out everything about some species

• What would it mean to learn everything about a
  given
• species? All available evidence indicates that
  the complete
• blueprint for making an organism is encoded in
  the
• organisms genome. Chemically, the genome
  consists of
• one or several DNA molecules. These are long
  strings
• composed of pairs of nucleotides. There are only
  four
• different nucleotides, denoted by A, C, G, T.
  The
• information about how to make the organism is
  encoded
• by the order in which the nucleotides appear.

5
Some genome sizes

• HIV2 virus
  9671 bp
• Mycoplasma genitalis 5.8 105
  bp
• Haemophilus influenzae 1.83 106 bp
• Saccharomyces cerevisiae 1.21 107 bp
• Caenorhabditis elegans 108
  bp
• Drosophila melanogaster 1.65 108 bp
• Homo sapiens 3.14 109
  bp
• Some amphibians 8 1010
  bp
• Amoeba dubia 6.7
  1011 bp

6
Sequencing Genomes

• Contemporary technology makes it possible to
  completely
• sequence entire genomes, that is, determine the
  sequence
• of As, Cs, Gs, and Ts in the organisms
  genome. The
• first virus was sequenced in the 1980s, the
  first
• bacterium (Haemophilus influenzae) in 1995, the
  first
• multicellular organism (Caenorhabditis elegans)
  in 1998.
• A draft of the human genome was announced in
  2000.

7
Where to store all these data?

• In databases of course. Some of the sequence
  data are
• stored in proprietary data bases, but most of
  them are
• stored in the public data base Genbank and an be
• accessed via the World Wide Web. In fact, most
  relevant
• journals require proof of submission to Genbank
  before an
• article discussing sequence data will be
  published.
• The URL for Genbank is
• http//www.ncbi.nlm.nih.gov/Genbank/

8
Whats in the databases?

• In 1981, Genbank contained less than 500,000 bp
  of info.
• In 1986, Genbank contained 9,615,371 bp of info.
• In 1991, Genbank contained 71,947,426 bp of info.
• In 1996, Genbank contained 651,972,984 bp of
  info.
• In 2001, Genbank contained 15,849,921,438 bp of
  info.
• In 2004, Genbank contained 37,893,844,733 bp of
  info.
• In 2009, Genbank contained 106,533,156,756 bp of
  info.

9
Whats in the databases?

• On March 18, 2005 there were 1791 completely
  sequenced
• viruses, 204 completely sequenced bacteria,
• 21 completely sequenced archaea, and 9 complete
• genomes of Eukaryotes, among them two yeasts, the
  
• roundworm C. elegans, the fruitfly Drosophila
• melanogaster, the mosquito A. gambiae, the
  malaria
• parasite P. falciparum, and the plant Arabidopsis
  thaliana
• (thale cress). There are also drafts of 11 other
  genomes
• of eukaryotes, most notably of the human genome.

10
Whats in the databases?

• On December 17, 2010 there were
• 3518 completely sequenced viruses,
• 952 completely sequenced bacteria,
• 68 completely sequenced archaea,
• and 73 complete genomes of Eukaryotes,
• among them cow, wolf, horse, human, a
• monkey, pig, chimpanzee.

11
First challengeSequencing large genomes

• Currently, much of the sequencing process is
  automated.
• However, contemporary sequencing machines can
  only
• sequence stretches of DNA that are a few hundred
  base
• pairs long at a time. The process of assembling
  these
• stretches of sequence into a whole genome poses
  some
• interesting mathematical problems.

12
First challengeSequencing large genomes

• For example, the publicly financed Human Genome
  Project
• uses an approach called genome mapping to
  facilitate
• sequence assembly. Celera Genomics, a private
• enterprise, announced that they will be able to
  complete
• the sequencing of the entire human genome much
  faster
• by using an approach called shotgun sequencing.
  There
• was much debate over the feasibility of the
  latter
• approach, but it apparently worked. At its core,
  this was a
• debate over the mathematics of sequence assembly.

13
You have sequenced your genome - what do you do
with it?

• This is known as genome analysis or sequence
  analysis.
• At present, most of bioinformatics is concerned
  with
• sequence analysis. Here are some of the
  questions
• studied in sequence analysis
• gene finding
• protein 3D structure prediction
• gene function prediction
• prediction of important sites in proteins
• reconstruction of phylogenies

14
Genes and proteins

• The genome controls the making and workings of an
  
• organism by telling the cell which proteins to
  manufacture
• under which conditions. Proteins are the
  workhorses of
• biochemistry and play a variety of roles.
• A gene is a stretch of DNA that codes a given
  protein.

15
Where are the genes?

• The objective of gene finding is to identify the
  regions of
• DNA that are genes. Ideally, we want to make
  statements
• like Positions 28,354 through 29,536 of this
  genome code
• a protein.
• The mathematical challenge here is to identify
  patterns in
• DNA that reliably indicate where a gene starts
  and ends,
• especially in eukaryotes.

16
Protein structure prediction

• When a protein is manufactured in the cell, it
  assumes a
• characteristic 3D structure or fold. It is very
  costly to
• determine the 3D structure of a protein
  experimentally (by
• NMR or X-ray crystallography). It would be much
  cheaper
• if we could predict the 3D structure of a protein
  directly
• from its primary structure, i.e., from the
  sequence of its
• amino acids. This is known as the protein
  folding problem.
• Many approaches have been proposed to develop
• algorithms for solving this problem so far
  results are
• mixed.

17
Prediction of protein function

• Suppose you have identified a gene. What is its
  role in the
• biochemistry of its organism? Sequence databases
  can
• help us in formulating reasonable hypotheses.
• Search the database for proteins with similar
  amino acid sequences in other organisms.
• If the functions of the most similar proteins are
  known and if they tend to be the same function
  (e.g., enzyme involved in glucose metabolism),
  then it is reasonable to conjecture that your
  gene also codes an enzyme involved in glucose
  metabolism.

18
Prediction of protein function homology searches

• Given a nucleotide or DNA sequence, searching the
  data
• base(s) for similar sequences is known as
  homology
• searches. The most popular software tool for
  performing
• these searches is called BLAST therefore
  biologists often
• speak of BLAST searches. There are two
  interesting
• problems here
• How to measure similarity of two sequences.
• How much similarity constitutes evidence of
  biologically meaningful homology as opposed to
  random chance?

19
Prediction of important sites in proteins

• Not all parts of a protein are equally important
  the
• function of most of its amino acids is often just
  to maintain
• an appropriate 3D structure, and mutations of
  those less
• crucial amino acids often don't have much effect.
  
• However, most proteins have crucial parts such as
• binding sites. Mutations occurring at binding
  sites tend to
• be lethal and will be weeded out by evolution.

20
How to predict binding sites from sequence data

• Get a collection of proteins of similar amino
  acid sequences and analogous biochemical function
  from your database.
• Align these sequences amino acid by amino acid.
• Check which regions of the protein are highly
  conserved in the course of evolution.
• The binding site should be in one of the highly
  conserved regions.

21
The importance of being aligned

• DNA and protein molecules evolve mostly by three
• processes point mutations (exchange of a single
  letter for
• another), insertions, and deletions. If a group
  of
• homologuous proteins from different organisms has
  been
• identified, it is assumed that these proteins
  have evolved
• from a common ancestor. The process of multiple
• sequence alignment aims at identifying loci in
  the
• individual molecules that are derived from a
  common
• ancestral locus. These form the columns of the
  alignment.

22
Example of a multiple alignment

• A T G - - T T C G G A C T
• A C G A A T C C A G - C T
• - C G A A T C C T A A C C
• - T G A G C A C T A A C C

23
Reconstruction of phylogenetic trees

• A phylogenetic tree depicts the evolutionary
  history of a
• group of species. By observing similarities and
  differences
• between species, we may be able to reconstruct
  their
• phylogeny. Classically, the degree of similarity
  between
• two species has been assessed from morphological
• characters. By comparing genomic sequence data,
  we
• actually can quantify the degree of similarity
  between any
• two species, and use these degrees of similarity
  as a basis
• for reconstructing phylogenetic trees.

24
Reconstruction of phylogenetic trees

• The most common approach to using genomic data
  for
• reconstruction of phylogenetic trees is to look
  at genes
• with analogous function and thus supposedly
  common
• ancestry and see how far the genes taken from the
  extant
• organisms have diverged.
• The observed differences in the amino acid
  composition
• are then used to reconstruct the phylogeny. The
  current
• partition of organisms into eubacteria, archaea
  and
• eukaria was discovered in this way by analyzing
  rRNA.

25
The new frontier Functional genomics

• It is fashionable nowadays to talk about
  functional
• genomics. Many people use this term as if it
  were a new
• discipline separate from bioinformatics, but I
  think it is
• more appropriate to consider it a new subfield of
  
• bioinformatics.
• The ultimate aim of functional genomics is to
  understand
• what genes do, when they do it, and how they do
  it.
• Ideally, we would like to understand the cell, or
  organism,
• as a giant network of chemical pathways that
  regulate
• each other.

26
Microarrays (gene chips)

• Microarrays or Gene Chips allow to monitor the
  level of
• activity of all the gene represented on the chip
• simultaneously under a variety of environmental
• conditions, in various organs, and at various
  stages of
• development.
• There are two types of challenges here To
  determine
• when a change in activity level detected by the
  chip is
• statistically significant, and to use the data so
  obtained to
• make inferences about gene regulation.

27
What do we do with all these data?

• The bread and butter method of microarray data
• analysis is clustering. This allows to identify,
  for
• a sequence of experiments on the same set of
  genes
• under various conditions, groups of genes that
  are
• up- or down-regulated simultaneously. It is
  believed
• that genes acting in the same chemical pathway
• would normally belong to the same cluster. Some
• algorithms for clustering will be discussed in
  this course.