2. 2
GENERAL INFORMATION
Course Methodology
The course consists of the following components;
i. a series of 10 lectures and 10 mini-exams,
ii. 7 skills classes, each with one programming task,
iii. one final written exam.
•In the lectures the main theoretical aspects will be presented.
•Each lecture starts with a "mini-exam" with three short questions belonging
to the previous lecture.
•In the skills classes (SCs) several programming tasks are performed, one of
which has to be submitted until next SC.
•Finally ,the course terminates with a open-book exam.
3. 3
GENERAL INFORMATION
10 lectures and 10 mini-exams
Prologue (In praise of cells)
Chapter 1. The first look at a genome (sequence statistics)
Chapter 2. All the sequence's men (gene finding)
Chapter 3. All in the family (sequence Alignment)
Chapter 4. The boulevard of broken genes (hidden Markov models)
Chapter 5. Are Neanderthals among us? (variation within and between
species)
Chapter 6. Fighting HIV (natural selection at the molecular level)
Chapter 7. SARS: a post-genomic epidemic (phylogenetic analysis)
Chapter 8. Welcome to the hotel Chlamydia (whole genome comparisons)
Chapter 9. The genomics of wine-making (Analysis of gene expression)
Chapter 10. A bed-time story (identification of regulatory sequences)
4. 4
GENERAL INFORMATION
mini-exams
* First 15 minutes of the lecture
* Closed Book
* Three short questions on the previous lecture
* Counts as bonus points for the final mark …
* There is a resit, where you can redo individual mini’s
you failed to attend with a legitimate leave
9. 9
GENERAL INFORMATION
Grading:
The relative weights of the components are:
i. 10 mini-exam: B1 bonus points (max 1)
ii. 7 skills class programming task: B2 bonus points (max 1)
iii. final written exam (open-book, three hours): E points (max 10)
Final grade = min(E + (B1 + B2), 10)
Study Points:
6 ECTS/ 4 NSP
15. 15
GENOMICS and PROTEOMICS
Genomics is the study of an organism's genome and the use of the genes.
It deals with the systematic use of genome information, associated with other
data, to provide answers in biology, medicine, and industry.
Proteomics is the large-scale study of proteins, particularly their structures
and functions.
Proteomics is much more complicated than genomics. Most importantly,
while the genome is a rather constant entity, the proteome differs from cell to
cell and is constantly changing through its biochemical interactions with the
genome and the environment. One organism will have radically different
protein expression in different parts of its body, in different stages of its life
cycle and in different environmental conditions.
17. 17
modern map-makers
have mapped the entire
human genome
Hurrah – we know the
entire 3.3 billion bps of the
human genome !!!
… but what does it mean
???
24. 24
Until recently we lacked tools to measure
gene activity
1989 saw the introduction of the
microarray technique by Stephen Fodor
But only in 1992 this technique became
generally available – but still very costly
25. 25
Until recently we lacked tools to measure
gene activity
1989 saw the introduction of the
microarray technique by Stephen Fodor
But only in 1992 this technique became
generally available – but still very costly
Stephen Fodor
Microarray
Microarray-ontwikkelaar
Ontwikkelde microarray
30. 30
Using the microarray technology we can
now make time series of the activity of
our 22.000 genes – so-called
genome wide expression profiles
31. 31
The identification of genetic pathways
from Microarray Timeseries
Sequence of genome-
wide expression profiles
at consequent instants
become more realistic
with decreasing costs …
33. 33
Now the problem is to map these
microarray-series of genome-wide
expression profiles into something that
tells us what the genes are actually doing
… for instance a network representing
their interaction
36. 36
DNA
Deoxyribonucleic acid (DNA) is a nucleic acid that
contains the genetic instructions specifying the biological
development of all cellular forms of life (and most viruses).
DNA is a long polymer of nucleotides and encodes the
sequence of the amino acid residues in proteins using the
genetic code, a triplet code of nucleotides.
42. 42
Genetic code
The genetic code is a set of rules that maps DNA sequences
to proteins in the living cell, and is employed in the process of
protein synthesis.
Nearly all living things use the same genetic code, called the
standard genetic code, although a few organisms use minor
variations of the standard code.
Fundamental code in DNA: {x(i)|i=1..N,x(i) in {C,A,T,G}}
Human: N = 3.3 billion
45. 45
Genetic code: TRANSCRIPTION
DNA → RNA
Transcription is the process through which a DNA sequence is enzymatically
copied by an RNA polymerase to produce a complementary RNA. Or, in other
words, the transfer of genetic information from DNA into RNA. In the case of
protein-encoding DNA, transcription is the beginning of the process that
ultimately leads to the translation of the genetic code (via the mRNA
intermediate) into a functional peptide or protein. Transcription has some
proofreading mechanisms, but they are fewer and less effective than the
controls for DNA; therefore, transcription has a lower copying fidelity than
DNA replication.
Like DNA replication, transcription proceeds in the 5' → 3' direction (ie the old
polymer is read in the 3' → 5' direction and the new, complementary
fragments are generated in the 5' → 3' direction).
IN RNA Thymine (T) → Uracil (U)
46. 46
Genetic code: TRANSLATION
DNA-triplet → RNA-triplet = codon → amino acid
RNA codon table
There are 20 standard amino acids used in proteins,
here are some of the RNA-codons that code for each amino acid.
Ala A GCU, GCC, GCA, GCG
Leu L UUA, UUG, CUU, CUC, CUA, CUG
Arg R CGU, CGC, CGA, CGG, AGA, AGG
Lys K AAA, AAG
Asn N AAU, AAC
Met M AUG
Asp D GAU, GAC
Phe F UUU, UUC
Cys C UGU, UGC
Pro P CCU, CCC, CCA, CCG
...
Start AUG, GUG
Stop UAG, UGA, UAA
57. 57
Unsolved problems in biology
Life. How did it start? Is life a cosmic phenomenon? Are the conditions necessary for the origin of
life narrow or broad? How did life originate and diversify in hundred millions of years? Why, after
rapid diversification, do microorganisms remain unchanged for millions of years? Did life start on
this planet or was there an extraterrestrial intervention (for example a meteor from another planet)?
Why have so many biological systems developed sexual reproduction? How do organisms
recognize like species? How are the sizes of cells, organs, and bodies controlled? Is immortality
possible?
DNA / Genome. Do all organisms link together to a primary source? Given a DNA sequence, what
shape will the protein fold into? Given a particular desired shape, what DNA sequence will produce
it? What are all the functions of the DNA? Other than the structural genes, which is the simpler part
of the system? What is the complete structure and function of the proteome proteins expressed by
a cell or organ at a particular time and under specific conditions? What is the complete function of
the regulator genes? The building block of life may be a precursor to a generation of electronic
devices and computers, but what are the electronic properties of DNA? Does Junk DNA function as
molecular garbage?
Viruses / Immune system. What causes immune system deficiencies? What are the signs of
current or past infection to discover where Ebola hides between human outbreaks? What is the
origin of antibody diversity? What leads to the complexity of the immune system? What is the
relationship between the immune system and the brain?
Humanity: Why are there drastic changes in hominid morphology? Why are there giant hominid
skeletons and very small hominid skeletons? Is hominid evolution static? Is hominid devolution
possible? Are there Human-Neanderthal hybrids? What explains the differences between Human
and Neanderthal Fossils?
58. 58
Introduction to Bioinformatics.
LECTURE 1:
CHAPTER 1:
The first look at a genome (sequence statistics)
* A mathematical model should be as simple as possible, but not
too simple!
(A. Einstein)
* All models are wrong, but some are useful. (G. Box)
59. 59
Introduction to Bioinformatics.
The first look at a genome (sequence statistics)
• Genome and genomic sequences
• Probabilistic models and sequences
• Statistical properties of sequences
• Standard data formats and databases
60. 60
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.1 Genomic era, year zero
• 1958: Fred Sanger (Cambridge, UK): Nobel prize for
developing protein sequencing techniques
• 1978: Fred Sanger: First complete viral genome
• 1980: Fred Sanger: First mitochrondrial genome
• 1980: Fred Sanger: Nobel prize for developing DNA
sequencing techniques
•1995: Craig Venter (TIGR): complete geneome of
Haemophilus influenza
• 2001: entire genome of Homo sapiens sapiens
• Start of post-genomic era (?!)
61. 61
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.1 Genomic era, year zero
ORGANISM DATE SIZE DESCRIPTION
Phage phiX 74 1978 5,368 bp 1st viral genome
Human mtDNA 1980 16,571 bp 1st organelle genome
HIV 1985 9,193 bp AIDS retrovirus
H. influenza 1995 1,830 Kb 1st bacterial genome
H. sapiens 2001 3,500 Mb complete human
genome
62. 62
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.2 The anatomy of a genome
• Definition of genome
• Prokaryotic genomes
• Eukaryotic genomes
• Viral genomes
• Organellar genomes
63. 63
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.3 Probabilistic models of
genome sequences
• Alphabets, sequences, and sequence space
• Multinomial sequence model
• Markov sequence model
64. 64
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.3 Probabilistic models of
genome sequences
Alphabets, sequences, and sequence space
4-letter alphabet N = {A,C,G,T} (= nucleoitides)
* sequence: s = s1s2…sn e.g.: s = ATATGCCTGACTG
* sequence space: the space of all sequences (up to a certain
length)
65. 65
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.3 Probabilistic models of
genome sequences
Multinomial sequence model
* Nucleotides are independent and identically distributed
(i.i.d),
* p = {pA,pC,pG,pT}, pA + pC + pG + pT = 1
*
n
i
i
p
P
1
))
(
(
)
( s
s
67. 67
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.3 Probabilistic models of genome sequences
Markov sequence model
* Probability start state π
* State transition matrix T
*
n
i
i
i
p
P
1
1 ))
(
),
1
(
(
)
(
)
( s
s
s
s
68. 68
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.4 Annotating a genome:
statistical sequence analysis
• Base composition & sliding window plot
• GC content & change point analysis
• Finding unusual DNA words
• Biological relevance of unusual motifs
• Pattern matching versus pattern discovery
69. 69
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.4 Annotating a genome: statistical sequence analysis
Base composition H. influenzae
BASE AMOUNT FREQUENCY
A 567623 0.3102
C 350723 0.1916
G 347436 0.1898
T 564241 0.3083
71. 71
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.4 Annotating a genome: statistical sequence analysis
Base composition & sliding window plot
72. 72
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.4 Annotating a genome: statistical sequence analysis
Base composition & sliding window plot
73. 73
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.4 Annotating a genome: statistical sequence analysis
Base composition & sliding window plot
74. 74
Evidence for co-evolution of gene order and recombination rate
Csaba Pál & Laurence D. Hurst
Nature Genetics 33, 392 - 395 (2003)
Figure 3. Sliding-window plot of the number of essential genes
(black line) and standard deviation from chromosomal mean
recombination rate (gray line) along chromosome 9.
Dot indicates the centromere. The windows were each ten genes long, and
one gene jump was made between windows.
75. 75
GC content
Organism GC content
H. influenzae 38.8
M. tuberculosis 65.8
S. enteridis 49.5
GC versus AT
76. 76
GC content
•Detect foreign genetic material
•Horizontal gene transfer
•Change point analysis
• AT denatures (=splits) at lower temperatures
• Thermophylic Archaeabacteriae: high CG
• Evolution:
Archaea > Eubacteriae > Eukaryotes
80. 80
k-mer frequency motif bias
• dimer, trimer, k-mer: nucleotide word of length 2, 3, k
• “unusual” k-mers
• 2-mer in H. influenzae
81. 81
k-mer frequency motif bias
2-mer (dinucleotide) density in H. influenzae
*A C G T
A* 0.1202 0.0505 0.0483 0.0912
C 0.0665 0.0372 0.0396 0.0484
G 0.0514 0.0522 0.0363 0.0499
T 0.0721 0.0518 0.0656 0.1189
NB: freq(‘AT’) freq(A or T)
82. 82
k-mer frequency motif bias
Most frequent 10-mer (dinucleotide) density
in H. influenzae:
AAAGTGCGGT
ACCGCACTTT
Why?
85. 85
Unusual DNA-words
Compare OBSERVED with EXPECTED frequency
of a word using multinomial model
Observed/expected ratio:
*A C G T
A* 1.2491 0.8496 0.8210 0.9535
C 1.1182 1.0121 1.0894 0.8190
G 0.8736 1.4349 1.0076 0.8526
T 0.7541 0.8763 1.1204 1.2505
This takes also into account the relative
proportionality pA, pC, pG, pT.
88. 88
Introduction to Bioinformatics
LECTURE 1: The first look at a genome (sequence statistics)
1.5 Finding data: GenBank,
EMBL, and DDBJ
• Online databases
•FASTA: a standard data format
89. 89
DATABASES
Generalized (DNA, proteins and carbohydrates, 3D-
structures)
Specialized (EST, STS, SNP, RNA, genomes, protein
families, pathways, microarray data ...)
90. 90
OVERVIEW OF DATABASES
1. Database indexing and specification of search terms
(retrieval, follow-up, analysis)
2. Archives (databases on: nucleic acid sequences, genome,
protein sequences, structures, proteomics, expression,
pathways)
3. Gateways to Archives (NCBI, Entrez, PubMed, ExPasy,
Swiss-Prot, SRS, PIR, Ensembl)
91. 91
Generalized DNA, protein
and carbohydrate databases
Primary sequence databases
EMBL (European Molecular Biology Laboratory nucleotide sequence
database at EBI, Hinxton, UK)
GenBank (at National Center for Biotechnology information, NCBI,
Bethesda, MD, USA)
DDBJ (DNA Data Bank Japan at CIB , Mishima, Japan)
92. 92
NCBI: National Center for
Biotechnology information
Established in 1988 as a national resource for molecular biology information,
NCBI creates public databases, conducts research in computational biology,
develops software tools for analyzing genome data, and disseminates
biomedical information - all for the better understanding of molecular processes
affecting human health and disease.
94. 94
The EMBL Nucleotide Sequence Database (also known as EMBL-Bank) constitutes
Europe's primary nucleotide sequence resource. Main sources for DNA and RNA
sequences are direct submissions from individual researchers, genome
sequencing projects and patent applications.
95. 95
EBI: European
Bioinformatics Institute
The European Bioinformatics Institute (EBI) is a non-profit academic organisation that
forms part of the European Molecular Biology Laboratory (EMBL).
The EBI is a centre for research and services in bioinformatics. The Institute manages
databases of biological data including nucleic acid, protein sequences and
macromolecular structures.
Our mission
To provide freely available data and bioinformatics services to all facets of the scientific
community in ways that promote scientific progress
To contribute to the advancement of biology through basic investigator-driven research
in bioinformatics
To provide advanced bioinformatics training to scientists at all levels, from PhD students
to independent investigators
To help disseminate cutting-edge technologies to industry
96. 96
What is DDBJ
DDBJ (DNA Data Bank of Japan) began DNA data bank activities in earnest in
1986 at the National Institute of Genetics (NIG).
DDBJ has been functioning as the international nucleotide sequence database
in collaboration with EBI/EMBL and NCBI/GenBank.
DNA sequence records the organismic evolution more directly than other
biological materials and ,thus, is invaluable not only for research in life
sciences, but also human welfare in general. The databases are, so to speak, a
common treasure of human beings. With this in mind, we make the databases
online accessible to anyone in the world
97. 97
The ExPASy (Expert Protein Analysis System) proteomics
server of the Swiss Institute of Bioinformatics (SIB) is
dedicated to the analysis of protein sequences and
structures as well as 2-D PAGE
ExPASy Proteomics Server
(SWISS-PROT)
98. 98
Generalized DNA, protein
and carbohydrate databases
Protein sequence databases
SWISS-PROT (Swiss Institute of Bioinformatics, SIB, Geneva, CH)
TrEMBL (=Translated EMBL: computer annotated protein sequence
database at EBI, UK)
PIR-PSD (PIR-International Protein Sequence Database, annotated
protein database by PIR, MIPS and JIPID at NBRF, Georgetown
University, USA)
UniProt (Joined data from Swiss-Prot, TrEMBL and PIR)
UniRef (UniProt NREF (Non-redundant REFerence) database at EBI, UK)
IPI (International Protein Index; human, rat and mouse proteome database
at EBI, UK)
99. 99
Generalized DNA, protein
and carbohydrate databases
Carbohydrate databases
CarbBank (Former complex carbohydrate structure database, CCSD,
discontinued!)
3D structure databases
PDB (Protein Data Bank cured by RCSB, USA)
EBI-MSD (Macromolecular Structure Database at EBI, UK )
NDB (Nucleic Acid structure Datatabase at Rutgers State University of
New Jersey , USA)
102. 102
Search across databases Help
Welcome to the Entrez cross-database search page
PubMed: biomedical literature citations and abstracts PubMed Central: free, full
text journal articles Site Search: NCBI web and FTP sites Books: online books
OMIM: online Mendelian Inheritance in Man OMIA: online Mendelian Inheritance in
Animals
Nucleotide: sequence database (GenBank) Protein: sequence database Genome:
whole genome sequences Structure: three-dimensional macromolecular structures
Taxonomy: organisms in GenBank SNP: single nucleotide polymorphism Gene:
gene-centered information HomoloGene: eukaryotic homology groups PubChem
Compound: unique small molecule chemical structures PubChem Substance:
deposited chemical substance records Genome Project: genome project information
UniGene: gene-oriented clusters of transcript sequences CDD: conserved protein
domain database 3D Domains: domains from Entrez Structure UniSTS: markers
and mapping data PopSet: population study data sets GEO Profiles: expression
and molecular abundance profiles GEO DataSets: experimental sets of GEO data
Cancer Chromosomes: cytogenetic databases PubChem BioAssay: bioactivity
screens of chemical substances GENSAT: gene expression atlas of mouse central
nervous system Probe: sequence-specific reagents
103. 103
New! Assembly Archive recently created at NCBI links together trace data and finished sequence providing
complete information about a genome assembly. The Assembly Archive's first entries are a set of closely related
strains of Bacillus anthracis. The assemblies are avalaible at TraceAssembly
See more about Bacillus anthracis genome Bacillus licheniformis ATCC
14580Release Date: September 15, 2004
Reference: Rey,M.W.,et al.
Complete genome sequence of the industrial bacterium Bacillus licheniformis and
comparisons with closely related Bacillus species (er) Genome Biol. 5, R77 (2004)
Lineage: Bacteria; Firmicutes; Bacillales; Bacillaceae; Bacillus.
Organism: Bacillus licheniformis ATCC 14580
Genome sequence information
chromosome - CP000002 - NC_006270
Size: 4,222,336 bp Proteins: 4161
Sequence data files submitted to GenBank/EMBL/DDBJ can be found at NCBI FTP:
GenBank or RefSeq Genomes
Bacillus cereus ZKRelease Date: September 15, 2004
Reference: Brettin,T.S., et al. Complete genome sequence of Bacillus cereus ZK
Lineage: Bacteria; Firmicutes; Bacillales; Bacillaceae; Bacillus; Bacillus cereus group.
Organism:
104. 104
NCBI → BLAST Latest news: 6 December 2005 : BLAST 2.2.13 released About
Getting started / News / FAQs
More info
NAR 2004 / NCBI Handbook / The Statistics of Sequence Similarity Scores
Software
Downloads / Developer info
Other resources
References / NCBI Contributors / Mailing list / Contact us
The Basic Local Alignment Search Tool (BLAST) finds regions of local similarity
between sequences. The program compares nucleotide or protein sequences to
sequence databases and calculates the statistical significance of matches. BLAST can
be used to infer functional and evolutionary relationships between sequences as well
as help identify members of gene families. Nucleotide
Quickly search for highly similar sequences (megablast)
Quickly search for divergent sequences (discontiguous megablast)
Nucleotide-nucleotide BLAST (blastn)
Search for short, nearly exact matches
Search trace archives with megablast or discontiguous megablast
Protein
Protein-protein BLAST (blastp)
BLAST
105. 105
Fasta Protein Database Query
Provides sequence similarity searching against nucleotide and protein databases
using the Fasta programs.
Fasta can be very specific when identifying long regions of low similarity especially for
highly diverged sequences.
You can also conduct sequence similarity searching against complete proteome or
genome databases using the Fasta programs.
Download Software
106. 106
Kangaroo
MOTIV BASED SEARCH
Kangaroo is a program that facilitates searching for gene and protein patterns
and sequences
Kangaroo is a pattern search program. Given a sequence pattern the program
will find all the records that contain that pattern.
To use this program, simply enter a sequence of DNA or Amino Acids in the
pattern window, choose the type of search, the taxonomy and submit your
request.
107. 107
ANALYSIS TOOLS
DNA sequence analysis tools
RNA analysis tools
Protein sequence and structure analysis tools (primary, secondary, tertiary
structure)
Tools for protein Function assignment
Phylogeny
Microarray analysis tools
