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1-Month Practical Master Course
Genome Analysis
Jaap Heringa
Centre for Integrative Bioinformatics VU (IBIVU)
Vrije Universiteit Amsterdam
The Netherlands
www.ibivu.cs.vu.nl
[email protected]
Chemistry
Biology
Molecular
biology
Mathematics
Statistics
Bioinformatics
Computer
Science
Informatics
Medicine
Physics
Biological Sequence Analysis
Pair-wise sequence alignment
Residue exchange matrices
Multiple sequence alignment
Phylogeny
C E N T R E F O R I N T E G R A T I V E
B I O I N F O R M A T I C S V U
DNA sequence
.....acctc
tcccagatgg
ggcccaggac
ttggtgacac
caaatcttgt
gagcccaaat
gcccagagcc
ccggtgccca
ttcctcttcc
cccggacccc
ccacgaagac
ggcgtggagg
agcagtacaa
cgtcctgcac
tgcaaggtct
cctggtcaaa
tgggagagca
cgcctcccat
cagcaagctc
aacatcttct
accgctacac
ctgtgcaaga
gtcctgtccc
tggggaagcc
aactcacaca
gacacacctc
cttgtgacac
caaatcttgt
gcacctgaac
ccccaaaacc
tgaggtcacg
ccnnnngtcc
tgcataatgc
cagcacgttc
caggactggc
ccaacaaagc
ggcttctacc
atgggcagcc
gctggactcc
accgtggaca
catgctccgt
gcagaagagc
acatgaaaca
aggtgcacct
tccagagctc
tgcccacggt
ccccgtgccc
acctccccca
gacacacctc
tcttgggagg
caaggatacc
tgcgtggtgg
agttcaagtg
caagacaaag
cgtgtggtca
tgaacggcaa
aaccaagtca
ccagcgacat
ggagaacaac
gacggctcct
agagcaggtg
gatgcatgag
ctctc.....
nctgtggttc
gcaggagtcg
aaaaccccac
gcccagagcc
acggtgccca
tgcccacggt
ccccgtgccc
accgtcagtc
cttatgattt
tggacgtgag
gtacgtggac
ctgcgggagg
gcgtcctcac
ggagtacaag
gcctgacctg
cgccgtggag
tacaacacca
tcttcctcta
gcagcagggg
gctctgcaca
Genome size
Organism
Number of base pairs
X-174 virus
5,386
Epstein Bar Virus
172,282
Mycoplasma genitalium
580,000
Hemophilus Influenza
1.8  106
Yeast (S. Cerevisiae)
12.1  106
Human
3.2  109
Wheat
16  109
Lilium longiflorum
90  109
Salamander
100  109
Amoeba dubia
670  109
Three main principles
• DNA makes RNA makes Protein
• Structure more conserved than sequence
• Sequence
Structure
Function
Functional Genomics
Genome
Expressome
Proteome
TERTIARY STRUCTURE (fold)
TERTIARY STRUCTURE (fold)
Metabolome
Regulation, signalling cascades, chaperonins, compartmentalisation
How to go from DNA to protein
sequence
A piece of double stranded DNA:
5’ attcgttggcaaatcgcccctatccggc 3’
3’ taagcaaccgtttagcggggataggccg 5’
DNA direction is from 5’ to 3’
How to go from DNA to protein
sequence
6-frame translation using the codon table (last lecture):
5’ attcgttggcaaatcgcccctatccggc 3’
3’ taagcaaccgtttagcggggataggccg 5’
Evolution and three-dimensional protein structure
information
Isocitrate
dehydrogenase:
The distance from
the active site
(in yellow) determines
the rate of evolution
(red = fast evolution,
blue = slow evolution)
Dean, A. M. and G. B.
Golding: Pacific Symposium
on Bioinformatics 2000
Protein Sequence-Structure-Function
Sequence
Threading
Homology
searching
(BLAST)
Ab initio
prediction
and folding
Structure
Function
Function
prediction
from
structure
Widely used tool for homology
detection: PSI-BLAST
• Heuristic tool to cut down computations
required for database searching (~1M
sequences in DB)
• Sensitivity gained by iteratively finding hits
(local alignments) and repeating search
Q
hits
T
PSSM
DB
Threading
Template
sequence
Compatibility
score
+
Query
sequence
Template
structure
Threading
Template
sequence
Compatibility
score
+
Query
sequence
Template
structure
Fold recognition by threading
Fold 1
Fold 2
Query
sequence
Fold 3
Compatibility
scores
Fold N
Bioinformatics
“Nothing in Biology makes sense except in
the light of evolution” (Theodosius
Dobzhansky (1900-1975))
“Nothing in bioinformatics makes sense
except in the light of Biology”
Divergent evolution
Ancestral sequence: ABCD
ACCD (B C)
ACCD
AB─D
or
ABD (C ø)
mutation
deletion
ACCD
A─BD
Pairwise Alignment
Divergent evolution
Ancestral sequence: ABCD
ACCD (B C)
ACCD
AB─D
true alignment
or
ABD (C ø)
mutation
deletion
ACCD
A─BD
Pairwise Alignment
Mutations under divergent evolution
(a)
G
(b)
G
Ancestral sequence
G
Sequence 1
A
One substitution one visible
Sequence 2
1: ACCTGTAATC
2: ACGTGCGATC
* **
D = 3/10 (fraction different
sites (nucleotides))
C
(c)
G
C
Two substitutions one visible
(d)
G
G
A
A
Two substitutions none visible
A
Back
mutation not visible
G
Convergent evolution
• Often with shorter motifs (e.g. active sites)
• Motif (function) has evolved more than once
independently, e.g. starting with two very different
sequences adopting different folds
• Sequences and associated structures remain
different, but (functional) motif can become
identical
• Classical example: serine proteinase and
chymotrypsin
Serine proteinase (subtilisin) and
chymotrypsin
• Different evolutionary origins, no sequence similarity
• Similarities in the reaction mechanisms. Chymotrypsin,
subtilisin and carboxypeptidase C have a catalytic triad of
serine, aspartate and histidine in common: serine acts as a
nucleophile, aspartate as an electrophile, and histidine as a
base.
• The geometric orientations of the catalytic residues are
similar between families, despite different protein folds.
• The linear arrangements of the catalytic residues reflect
different family relationships. For example the catalytic
triad in the chymotrypsin clan (SA) is ordered HDS, but is
ordered DHS in the subtilisin clan (SB) and SDH in the
carboxypeptidase clan (SC).
A protein sequence alignment
MSTGAVLIY--TSILIKECHAMPAGNE-------GGILLFHRTHELIKESHAMANDEGGSNNS
* *
* **** ***
A DNA sequence alignment
attcgttggcaaatcgcccctatccggccttaa
att---tggcggatcg-cctctacgggcc---***
**** **** **
******
What can sequence tell us about structure
(HSSP)
Sander & Schneider, 1991
Searching for similarities
What is the function of the new gene?
The “lazy” investigation (i.e., no biologial
experiments, just bioinformatics techniques):
– Find a set of similar protein sequences to the
unknown sequence
– Identify similarities and differences
– For long proteins: identify domains first
Evolutionary and functional relationships
Reconstruct evolutionary relation:
•Based on sequence
-Identity (simplest method)
-Similarity
•Homology (common ancestry: the ultimate goal)
•Other (e.g., 3D structure)
Functional relation:
Sequence Structure
Function
Searching for similarities
Common ancestry is more interesting:
Makes it more likely that genes share
the same function
Homology: sharing a common ancestor
– a binary property (yes/no)
– it’s a nice tool:
When (an unknown) gene X is homologous to (a
known) gene G it means that we gain a lot of
information on X: what we know about G can be
transferred to X as a good suggestion.
Biological definitions for
related sequences
 Homologues are similar sequences in two different
organisms that have been derived from a common ancestor
sequence. Homologues can be described as either
orthologues or paralogues.
 Orthologues are similar sequences in two different
organisms that have arisen due to a speciation event.
Orthologs typically retain identical or similar functionality
throughout evolution.
 Paralogues are similar sequences within a single organism
that have arisen due to a gene duplication event.
 Xenologues are similar sequences that do not share the
same evolutionary origin, but rather have arisen out of
horizontal transfer events through symbiosis, viruses, etc.
How to evolve
Important distinction:
• Orthologues: homologous proteins in different
species (all deriving from same ancestor)
• Paralogues: homologous proteins in same species
(internal gene duplication)
• In practice: to recognise orthology, bi-directional
best hit is used in conjunction with database
search program (this is called an operational
definition)
So this means …
Source: http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/Orthology.html
Example today: Pairwise sequence
alignment needs sense of evolution
Global dynamic programming
MDAGSTVILCFVG
M
D
A
A
S
T
I
L
C
G
S
Evolution
Search matrix
MDAGSTVILCFVGMDAAST-ILC--GS
Amino Acid Exchange
Matrix
Gap penalties
(open,extension)
How to determine similarity
Frequent evolutionary events at the
DNA level:
1. Substitution
2. Insertion, deletion
3. Duplication
4. Inversion
We will restrict
ourselves to these
events
nucleotide oneletter code
A DNA sequence alignment
attcgttggcaaatcgcccctatccggccttaa
att---tggcggatcg-cctctacgggcc---***
**** **** **
******
A protein sequence alignment
MSTGAVLIY--TSILIKECHAMPAGNE-------GGILLFHRTHELIKESHAMANDEGGSNNS
* *
* **** ***
amino acid oneletter code
Dynamic programming
Scoring alignments
– Substitution (or match/mismatch)
• DNA
• proteins
– Gap penalty
• Linear: gp(k)=ak
• Affine: gp(k)=b+ak
• Concave, e.g.: gp(k)=log(k)
The score for an alignment is the sum of the scores over
all alignment columns
Dynamic programming
Scoring alignments
Sa,b =
 s(a , b ) - 
l
i
j
gp(k) = gapinit + kgapextension
k
Nk  gp(k )
affine gap penalties
DNA: define a score for match/mismatch of letters
Simple:
A
C
G
T
A
1
-1
-1
-1
C
-1
1
-1
-1
G
-1
-1
1
-1
T
-1
-1
-1
1
Used in genome alignments:
A
C
G
T
A
91
-114
-31
-123
C
-114
100
-125
-31
G
-31
-125
100
-114
T
-123
-31
-114
91
Dynamic programming
Scoring alignments
T D W V T A L K
T D W L - - I K
2020
10
Amino Acid Exchange Matrix
1
Affine gap penalties (open,
extension)
Score: s(T,T)+s(D,D)+s(W,W)+s(V,L)-Po-2Px +
+s(L,I)+s(K,K)