Introduction to Genomics and Proteomics

www. .uni-rostock.de

Bioinformatics
Introduction to genomics and proteomics I

Ulf Schmitz
ulf.schmitz@informatik.uni-rostock.de

Bioinformatics and Systems Biology Group
www.sbi.informatik.uni-rostock.de

Ulf Schmitz, Introduction to genomics and proteomics I 1

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.


Genes www. .uni-rostock.de

• 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
Species Chrom. Genes Base pairs
Human 46 28-35,000 3.1 billion
(Homo sapiens) (23 pairs)

Mouse 40 22.5-30,000 2.7 billion
(Mus musculus)

Puffer fish 44 31,000 365 million
(Fugu rubripes)

Malaria mosquito 6 14,000 289 million
(Anopheles gambiae)

Fruit Fly 8 14,000 137 million
(Drosophila melanogaster)

Roundworm 12 19,000 97 million
(C. elegans)

Bacterium 1 5,000 4.1 million
(E. coli)


Genes
exon:
A section of DNA which carries the coding
A section of DNA which carries the coding
sequence for a protein or part of it. Exons
sequence for a protein or part of it. Exons
are separated by intervening, non-coding
are separated by intervening, non-coding
sequences (called introns). In eukaryotes
sequences (called introns). In eukaryotes
most genes consist of a number of exons.
most genes consist of a number of exons.

intron:
An intervening section of DNA which occurs
An intervening section of DNA which occurs
almost exclusively within a eukaryotic gene, but
almost exclusively within a eukaryotic gene, but
which is not translated to amino-acid sequences in
which is not translated to amino-acid sequences in
the gene product.
the gene product.
The introns are removed from the pre-mature
The introns are removed from the pre-mature
mRNA through a process called splicing, which
mRNA through a process called splicing, which
leaves the exons untouched, to form an active
leaves the exons untouched, to form an active
mRNA.
mRNA.


Genes
Examples of the exon:intron mosaic of 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


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…



1. Detection of regions similar to known coding regions from other organisms

• 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

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…


Ab initio gene identification in eukaryotic genomes

• 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

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 www. .uni-rostock.de

• 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.


The human genome
Top categories in a function classification:
Function Number % Function Number %
Nucleic acid binding 2207 14.0 Apoptosis inhibitor 132 0.8
DNA binding 1656 10.5 Signal transduction 1790 11.4
DNA repair protein 45 0.2 Receptor 1318 8.4
DNA replication factor 7 0.0 Transmembrane receptor 1202 7.6
Transcription factor 986 6.2 G-protein link receptor 489 3.1
RNA binding 380 2.4 Olfactory receptor 71 0.0
Structural protein of ribosome 137 0.8
Storage protein 7 0.0
Translation factor 44 0.2
Cell adhesion 189 1.2
Transcription factor binding 6 0.0
Cell Cycle regulator 75 0.4 Structural protein 714 4.5
Cytoskeletal structural protein 145 0.9
Chaperone 154 0.9
Transporter 682 4.3
Motor 85 0.5 Ion channel 269 1.7
Actin binding 129 0.8 Neurotransmitter transporter 19 0.1
Defense/immunity protein 603 3.8 Ligand binding or carrier 1536 9.7
Electron transfer 33 0.2
Enzyme 3242 20.6
Cytochrome P450 50 0.3
Peptidase 457 2.9
Endopeptidase 403 2.5 Tumor suppressor 5 0.0
Protein kinase 839 5.3 Unclassified 30.6
4813
Protein phosphatase 295 1.8
Enzyme activator 3 0.0 Total 15683 100.0


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

Copy Fraction of
Element Size (bp)
number genome %

Short Interspersed Nuclear 100-300 1.500.000 13
Elements (SINEs)
Long Interspersed Nuclear 6000-8000 850.000 21
Elements (LINEs)
Long Terminal Repeats 15.000 -110.000 450.000 8

DNA Transposon fossils 80-3000 300.000 3


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.


Functional Genomics
From gene to function

Genome

Expressome

Proteome

TERTIARY STRUCTURE (fold)
TERTIARY STRUCTURE (fold)

Metabolome


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!



Bioinformatics
Introduction to genomics and proteomics II

ulf.schmitz@informatik.uni-rostock.de

Bioinformatics and Systems Biology Group
www.sbi.informatik.uni-rostock.de

Ulf Schmitz, Introduction to genomics and proteomics II 1

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

DNA Microarray GENOME Genetic Screens

PROTEOME

Protein – Ligand Protein – Protein
Interactions Interactions

Structure


Proteomics

Motivation:
• 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.


Proteomics
Things to consider:
• 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


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
• 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.
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

High-resolution two-dimensional polyacrylamide gel
electrophoresis (2D PAGE) shows the pattern of
protein content in a sample.

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


Proteomics

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..

Typically, a sample is purified to
homogeneity, crystallized, subjected to an X-
ray beam and diffraction data are collected.


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

Following maps are used to find out how hereditary information is
stored, passed on, and implemented.

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)



Linkage map

Variable number tandem repeats (VNTRs, also minisatellites)
• 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

Short tandem repeat polymorphism (STRPs, also microsatellites)
• Regions of 2-7bp, repeated many times
– Usually 10-30 consecutive copies



centromere

3bp

CGTCGTCGTCGTCGTCGTCGTCGT...
GCAGCAGCAGCAGCAGCAGCAGCA...



Banding patterns of
chromosomes



Banding patterns of chromosomes

petite – arm
centromere
queue - arm



Contig map (also contiguous clone map)
• 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

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


Imagine we know that a disease results from a specific
defective protein:
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


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!


Introduction to Genomics and Proteomics

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