Biotech 2011-01-intro

Ulf Schmitz, Introduction to genomics and proteomics I 1
www. .uni-rostock.de
BioinformaticsBioinformatics
Introduction to genomics and proteomics IIntroduction to genomics and proteomics I
Ulf Schmitz
ulf.schmitz@informatik.uni-rostock.de
Bioinformatics and Systems Biology Group
www.sbi.informatik.uni-rostock.de

Outline
Genomics/Genetics
1. The tree of life
• Prokaryotic Genomes
– Bacteria
– Archaea
• Eukaryotic Genomes
– Homo sapiens
2. Genes
• Expression Data

Genomics - Definitions
Genetics: is the science of genes, heredity, and the variation of organisms.
Humans began applying knowledge of genetics in prehistory with
the domestication and breeding of plants and animals.
In modern research, genetics provides tools in the investigation
of the function of a particular gene, e.g. analysis of genetic
interactions.
Genomics: attempts the study of large-scale genetic patterns across the
genome for a given species. It deals with the systematic use of
genome information to provide answers in biology, medicine, and
industry.
Genomics has the potential of offering new therapeutic methods
for the treatment of some diseases, as well as new diagnostic
methods.
Major tools and methods related to genomics are bioinformatics,
genetic analysis, measurement of gene expression, and
determination of gene function.

www. .uni-rostock.deGenes
• a gene coding for a protein corresponds to a sequence of
nucleotides along one or more regions of a molecule of DNA
• in species with double stranded DNA (dsDNA), genes may appear
on either strand
• bacterial genes are continuous regions of DNA
bacterium:
• a string of 3N nucleotides encodes a string of N amino acids
• or a string of N nucleotides encodes a structural RNA molecule of N
residues
eukaryote:
• a gene may appear split into separated segments in the DNA
• an exon is a stretch of DNA retained in mRNA that the ribosomes translate
into protein

Genomics
Genome size comparison
4.1 million5,0001Bacterium
(E. coli)
19,000
14,000
14,000
31,000
22.5-30,000
28-35,000
Genes
97 million12Roundworm
(C. elegans)
137 million8Fruit Fly
(Drosophila melanogaster)
289 million6Malaria mosquito
(Anopheles gambiae)
365 million44Puffer fish
(Fugu rubripes)
2.7 billion40Mouse
(Mus musculus)
3.1 billion46
(23 pairs)
Human
(Homo sapiens)
Base pairsChrom.Species

Genes
exon:
A section of DNA which carries the coding
sequence for a protein or part of it. Exons
are separated by intervening, non-coding
sequences (called introns). In eukaryotes
most genes consist of a number of exons.
exon:
A section of DNA which carries the coding
sequence for a protein or part of it. Exons
are separated by intervening, non-coding
sequences (called introns). In eukaryotes
most genes consist of a number of exons.
intron:
An intervening section of DNA which occurs
almost exclusively within a eukaryotic gene, but
which is not translated to amino-acid sequences in
the gene product.
The introns are removed from the pre-mature
mRNA through a process called splicing, which
leaves the exons untouched, to form an active
mRNA.
intron:
An intervening section of DNA which occurs
almost exclusively within a eukaryotic gene, but
which is not translated to amino-acid sequences in
the gene product.
The introns are removed from the pre-mature
mRNA through a process called splicing, which
leaves the exons untouched, to form an active
mRNA.

Genes
exon intron
Globin gene – 1525 bp: 622 in exons, 893 in introns
Ovalbumin gene - ~ 7500 bp: 8 short exons comprising 1859 bp
Conalbumin gene - ~ 10,000 bp: 17 short exons comprising ~ 2,200 bp
Examples of the exon:intron mosaic of genes

Picking out genes in genomes
• Computer programs for genome analysis identify ORFs
(open reading frames)
• An ORF begins with an initiation codon ATG (AUG)
• An ORF is a potential protein-coding region
• There are two approaches to identify protein coding
regions…

• Regions may encode amino acid sequences similar to known proteins
• Or may be similar to ESTs (correspond to genes known to be
expressed)
• Few hundred initial bases of cDNA are sequenced to identify a gene
1. Detection of regions similar to known coding regions from other organisms
2. Ab initio methods, seek to identify genes from the properties of the
DNA sequence itself
• Bacterial genes are easy to identify, because they are contiguous
• They have no introns and the space between genes is small
• Identification of exons in higher organisms is a problem, assembling
them another…

• The initial (5´) exon starts with a transcription start
point, preceded by a core promoter site such as the
TATA box (~30bp upstream)
– Free of stop codons
– End immediately before a GT splice-signal
Ab initio gene identification in eukaryotic genomes
binds and directs RNA polymerase
to the correct transcriptional start site

5' splice signal
3' splice signal

• Internal exons are free of stop codons too
– Begin after an AG splice signal
– End before a GT splice signal

• The final (3´) exon starts after a an AG splice signal
– Ends with a stop codon (TAA,TAG,TGA)
– Followed by a polyadenylation signal sequence

Humans have
spliced genes…

DNA makes RNA makes Protein

Tree of life
Prokaryotes

Genomics – Prokaryotes
• the genome of a prokaryote comes
as a single double-stranded DNA
molecule in ring-form
– in average 2mm long
– whereas the cells diameter is only
0.001mm
– < 5 Mb
• prokaryotic cells can have plasmids
as well (see next slide)
• protein coding regions have no
introns
• little non-coding DNA compared to
eukaryotes
– in E.coli only 11%

Genomics - Plasmids
• Plasmids are circular double stranded DNA molecules that are separate
from the chromosomal DNA.
• They usually occur in bacteria, sometimes in eukaryotic organisms
• Their size varies from 1 to 250 kilo base pairs (kbp). There are from one
copy, for large plasmids, to hundreds of copies of the same plasmid
present in a single cell.

Prokaryotic model organisms
E.coli (Escherichia coli)
Methanococcus jannaschii (archaeon)
Mycoplasma genitalium
(simplest organism known)

Genomics
• DNA of higher organisms is organized into chromosomes
(human – 23 chromosome pairs)
• not all DNA codes for proteins
• on the other hand some genes exist in multiple copies
• that’s why from the genome size you can’t easily estimate
the amount of protein sequence information

Genomes of eukaryotes
• majority of the DNA is in the nucleus, separated into
bundles (chromosomes)
– small amounts of DNA appear in organelles (mitochondria and
chloroplasts)
• within single chromosomes gene families are common
– some family members are paralogues (related)
• they have duplicated within the same genome
• often diverged to provide separate functions in descendants
(Nachkommen)
• e.g. human α and β globin
– orthologues genes
• are homologues in different species
• often perform the same function
• e.g. human and horse myoglobin
– pseudogenes
• lost their function
• e.g. human globin gene cluster
pseudogene

Eukaryotic model organisms
• Saccharomyces cerevisiae (baker’s yeast)
• Caenorhabditis elegans (C.elegans)
• Drosophila melanogaster (fruit fly)
• Arabidopsis thaliana (flower)
• Homo sapiens (human)

The human genome
• ~3.2 x 109 bp (thirty time larger than C.elegans or D.melongaster)
• coding sequences form only 5% of the human genome
• Repeat sequences over 50%
• Only ~32.000 genes
• Human genome is distributed over 22 chromosome pairs plus X and
Y chromosomes
• Exons of protein-coding genes are relatively small compared to
other known eukaryotic genomes
• Introns are relatively long
• Protein-coding genes span long stretches of DNA (dystrophin,
coding a 3.685 amino acid protein, is >2.4Mbp long)
• Average gene length: ~ 8,000 bp
• Average of 5-6 exons/gene
• Average exon length: ~200 bp
• Average intron length: ~2,000 bp
• ~8% genes have a single exon
• Some exons can be as small as 1 or 3 bp.

0.03Enzyme activator
20.6
2.9
2.5
5.3
1.8
3242
457
403
839
295
Enzyme
Peptidase
Endopeptidase
Protein kinase
Protein phosphatase
3.8603Defense/immunity protein
0.8129Actin binding
0.585Motor
0.9154Chaperone
0.475Cell Cycle regulator
0.06Transcription factor binding
14.0
10.5
0.2
0.0
6.2
2.4
0.8
0.2
2207
1656
45
7
986
380
137
44
Nucleic acid binding
DNA binding
DNA repair protein
DNA replication factor
Transcription factor
RNA binding
Structural protein of ribosome
Translation factor
%NumberFunction
100.015683Total
30.64813Unclassified
0.05Tumor suppressor
9.7
0.2
0.3
1536
33
50
Ligand binding or carrier
Electron transfer
Cytochrome P450
4.3
1.7
0.1
682
269
19
Transporter
Ion channel
Neurotransmitter transporter
4.5
0.9
714
145
Structural protein
Cytoskeletal structural protein
1.2189Cell adhesion
0.07Storage protein
11.4
8.4
7.6
3.1
0.0
1790
1318
1202
489
71
Signal transduction
Receptor
Transmembrane receptor
G-protein link receptor
Olfactory receptor
0.8132Apoptosis inhibitor
%NumberFunction
The human genomeThe human genome
Top categories in a function classification:

The human genome
• Repeated sequences comprise over 50% of the genome:
– Transposable elements, or interspersed repeats include LINEs and
SINEs (almost 50%)
– Retroposed pseudogenes
– Simple ‘stutters’ - repeats of short oligomers (minisatellites and
microsatellites)
– Segment duplication, of blocks of ~10 - 300kb
– Blocks of tandem repeats, including gene families
3300.00080-3000DNA Transposon fossils
8450.00015.000 -110.000Long Terminal Repeats
21850.0006000-8000Long Interspersed Nuclear
Elements (LINEs)
131.500.000100-300Short Interspersed Nuclear
Elements (SINEs)
Fraction of
genome %
Copy
number
Size (bp)Element

The human genome
• All people are different, but the DNA of different
people only varies for 0.2% or less.
• So, only up to 2 letters in 1000 are expected to be
different.
• Evidence in current genomics studies (Single
Nucleotide Polymorphisms or SNPs) imply that on
average only 1 letter out of 1400 is different
between individuals.
• means that 2 to 3 million letters would differ
between individuals.

TERTIARY STRUCTURE (fold)
TERTIARY STRUCTURE (fold)
Genome
Expressome
Proteome
Metabolome
Functional Genomics
From gene to functionFrom gene to function

DNA makes RNA makes Protein:
Expression data
• More copies of mRNA for a gene leads to more
protein
• mRNA can now be measured for all the genes in a
cell at ones through microarray technology
• Can have 60,000 spots (genes) on a single gene
chip
• Color change gives intensity of gene expression
(over- or under-expression)

Genes and regulatory regions
regulatory mechanisms organize the
expression of genes
– genes may be turned on or off in response to
concentrations of nutrients or to stress
– control regions often lie near the segments
coding for proteins
– they can serve as binding sites for molecules
that transcribe the DNA
– or they bind regulatory molecules that can
block transcription

Expression data

Outlook – coming lecture
Proteomics
– Proteins
– post-translational modification
– Key technologies
• Maps of hereditary information
• SNPs (Single nucleotide polymorphisms)
• Genetic diseases

Thanks for your
attention!

Ulf Schmitz, Introduction to genomics and proteomics II 1
BioinformaticsBioinformatics
Introduction to genomics and proteomics IIIntroduction to genomics and proteomics II
ulf.schmitz@informatik.uni-rostock.de
Bioinformatics and Systems Biology Group
www.sbi.informatik.uni-rostock.de

Outline
1. Proteomics
• Motivation
• Post -Translational Modifications
• Key technologies
• Data explosion
2. Maps of hereditary information
3. Single nucleotide polymorphisms

Protomics
Proteomics:
• is the large-scale study of proteins, particularly their structures
and functions
• This term was coined to make an analogy with genomics, and
is often viewed as the "next step",
• but proteomics is much more complicated than genomics.
• Most importantly, while the genome is a rather constant entity,
the proteome is constantly changing through its biochemical
interactions with the genome.
• One organism will have radically different protein expression in
different parts of its body and in different stages of its life cycle.
Proteome:
The entirety of proteins in existence in an organism are
referred to as the proteome.

Proteomics
If the genome is a list of the instruments in an orchestra, the
proteome is the orchestra playing a symphony.
R.Simpson

Proteomics
• Describing all 3D structures of proteins in the cell is called Structural
Genomics
• Finding out what these proteins do is called Functional Genomics
GENOME
PROTEOME
DNA Microarray Genetic Screens
Protein – Ligand
Interactions
Protein – Protein
Interactions
Structure

Proteomics
• What kind of data would we like to measure?
• What mature experimental techniques exist to
determine them?
• The basic goal is a spatio-temporal description of
the deployment of proteins in the organism.
Motivation:

Proteomics
• the rates of synthesis of different proteins vary among
different tissues and different cell types and states of activity
• methods are available for efficient analysis of transcription
patterns of multiple genes
• because proteins ‘turn over’ at different rates, it is also
necessary to measure proteins directly
• the distribution of expressed protein levels is a kinetic
balance between rates of protein synthesis and degradation
Things to consider:

Why do Proteomics?
• are there differences between amino acid sequences determined
directly from proteins and those determined by translation from
DNA?
– pattern recognition programs addressing this questions have following
errors:
• a genuine protein sequence may be missed entirely
• an incomplete protein may be reported
• a gene may be incorrectly spliced
• genes for different proteins may overlap
• genes may be assembled from exons in different ways in different tissues
– often, molecules must be modified to make a mature protein that differs
significantly from the one suggested by translation
• in many cases the missing post-translational- modifications are quite
important and have functional significance
• post-transitional modifications include addition of ligands, glycosylation,
methylation, excision of peptides, etc.
– in some cases mRNA is edited before translation, creating changes in
the amino acid sequence that are not inferrable from the genes
• a protein inferred from a genome sequence is a hypothetical object
until an experiment verifies its existence

Post-translational modification
• a protein is a polypeptide chain composed of 20 possible amino acids
• there are far fewer genes that code for proteins in the human genome than there
are proteins in the human proteome (~33,000 genes vs ~200,000 proteins).
• each gene encodes as many as six to eight different proteins
– due to post-translational modifications such as phosphorylation, glycosylation or cleavage
(Spaltung)
• posttranslational modification extends the range of possible functions a protein can
have
– changes may alter the hydrophobicity of a protein and thus determine if the modified
protein is cytosolic or membrane-bound
– modifications like phosphorylation are part of common mechanisms for controlling the
behavior of a protein, for instance, activating or inactivating an enzyme.

Post-translational modification
• phosphorylation is the addition of a phosphate (PO4) group to a protein
or a small molecule (usual to serine, tyrosine, threonine or histidine)
• In eukaryotes, protein phosphorylation is probably the most important
regulatory event
• Many enzymes and receptors are switched "on" or "off" by
phosphorylation and dephosphorylation
• Phosphorylation is catalyzed by various specific protein kinases,
whereas phosphatases dephosphorylate.
Phosphorylation
Acetylation
• Is the addition of an acetyl group, usually at the N-terminus of the protein
Farnesylation
• farnesylation, the addition of a farnesyl group
Glycosylation
• the addition of a glycosyl group to either asparagine, hydroxylysine,
serine, or threonine, resulting in a glycoprotein

Proteomics

Key technologies for proteomics
1. 1-D electrophoresis and 2-D electrophoresis
• are for the separation and visualization of proteins.
2. mass spectrometry, x-ray crystallography, and NMR
(Nuclear magnetic resonance )
• are used to identify and characterize proteins
3. chromatography techniques especially affinity
chromatography
• are used to characterize protein-protein interactions.
4. Protein expression systems like the yeast two-
hybrid and FRET (fluorescence resonance energy
transfer)
• can also be used to characterize protein-protein interactions.

Key technologies for proteomics
Reference map of lympphoblastoid
cell linePRI, soluble proteins.
• 110 µg of proteins loaded
• Strip 17cm pH gradient 4-7, SDS
PAGE gels 20 x 25 cm, 8-18.5% T.
• Staining by silver nitrate method
(Rabilloud et al.,)
• Identification by mass spectrometry.
The pinks labels on the spots indicate
the ID in Swiss-prot database
browse the SWISS-2DPAGE database for more 2d PAGE images
High-resolution two-dimensional polyacrylamide gel
electrophoresis (2D PAGE) shows the pattern of
protein content in a sample.

Proteomics
Typically, a sample is purified to
homogeneity, crystallized, subjected to an X-
ray beam and diffraction data are collected.
X-ray crystallography is a means to
determine the detailed molecular
structure of a protein, nucleic acid or
small molecule.
With a crystal structure we can explain the
mechanism of an enzyme, the binding of an
inhibitor, the packing of protein domains, the
tertiary structure of a nucleic acid molecule
etc..

High-throughput Biological Data
• Enormous amounts of biological data are being
generated by high-throughput capabilities; even
more are coming
– genomic sequences
– gene expression data (microarrays)
– mass spec. data
– protein-protein interaction (chromatography)
– protein structures (x-ray christallography)
– ......

Protein structural data explosion
Protein Data Bank (PDB): 33.367 Structures (1 November 2005)
28.522 x-ray crystallography, 4.845 NMR

Maps of hereditary information
1. Linkage maps of
genes
mini- / microsatellites
2. Banding patterns of chromosomes
physical objects with visible landmarks called banding patterns
3. DNA sequences
Contig maps (contigous clone maps)
Sequence tagged site (STS)
SNPs (Single nucloetide polymorphisms)
Following maps are used to find out how hereditary information is
stored, passed on, and implemented.

Linkage map

• regions, 8-80bp long, repeated a variable number of times
• the distribution and the size of repeats is the marker
• inheritance of VNTRs can be followed in a family and
mapped to a pathological phenotype
• first genetic data used for personal identification
– Genetic fingerprints; in paternity and in criminal cases
Variable number tandem repeats (VNTRs, also minisatellites)
Short tandem repeat polymorphism (STRPs, also microsatellites)
• Regions of 2-7bp, repeated many times
– Usually 10-30 consecutive copies

centromere
CGTCGTCGTCGTCGTCGTCGTCGT...
GCAGCAGCAGCAGCAGCAGCAGCA...
3bp

Banding patterns of
chromosomes

Banding patterns of chromosomes
petite – arm
centromere
queue - arm

• Series of overlapping DNA clones of known
order along a chromosome from an organism
of interest, stored in yeast or bacterial cells as
YACs (Yeast Artificial Chromosomes) or
BACs (Bacterial Artificial Chromosomes)
• A contig map produces a fine mapping (high
resolution) of a genome
• YAC can contain up to 106bp, a BAC about
250.000bp
Contig map (also contiguous clone map)
Sequence tagged site (STS)
• Short, sequenced region of DNA, 200-600bp
long, that appears in a unique location in the
genome
• One type arises from an EST (expressed
sequence tag), a piece of cDNA

1. if we know the protein involved, we can pursue
rational approaches to therapy
2. if we know the gene involved, we can devise
tests to identify sufferers or carriers
3. wereas the knowledge of the chromosomal
location of the gene is unnecessary in many
cases for either therapy or detection;
• it is required only for identifying the gene, providing a
bridge between the patterns of inheritance and the
DNA sequence
Imagine we know that a disease results from a specific
defective protein:

Single nucleotide polymorphisms (SNPs)Single nucleotide polymorphisms (SNPs)
• SNP (pronounced ‘snip’) is a genetic
variation between individuals
• single base pairs that can be substituted,
deleted or inserted
• SNPs are distributed throughout the
genome
– average every 2000bp
• provide markers for mapping genes
• not all SNPs are linked to diseases

Single nucleotide polymorphisms (SNPs)
• nonsense mutations:
– codes for a stop, which can truncate the
protein
• missense mutations:
– codes for a different amino acid
• silent mutations:
– codes for the same amino acid, so has no
effect

Outlook – coming lecture
• Bioinformatics Information Resources And Networks
– EMBnet – European Molecular Biology Network
• DBs and Tools
– NCBI – National Center For Biotechnology Information
• DBs and Tools
– Nucleic Acid Sequence Databases
– Protein Information Resources
– Metabolic Databases
– Mapping Databases
– Databases concerning Mutations
– Literature Databases

Thanks for your
attention!

Biotech 2011-01-intro

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