Transcript Sequencing genomes

Last lecture summary
New generation sequencing (NGS)
• The completion of human genome was just a start of
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modern DNA sequencing era – “high-throughput next
generation sequencing” (NGS).
New approaches, reduce time and cost.
Holly Grail of sequencing – complete human genome
below $ 1000.
1st generation – Sanger dideoxy method
2nd generation – sequencing by synthesis
(pyrosequencing)
3rd generation – single molecule sequencing
cDNA, EST libraries
• cDNA – reverse transcriptase, contains
only expressed genes (no introns)
cDNA library – a collection of
different DNA sequences that have
been incorporated into a vector
• EST – Expressed Sequence Tag
• short, unedited (single-pass read),
randomly selected subsequence (200-800
bps) of cDNA sequence generated either
from 5’ or from 3’
• higher quality in the middle
• cDNA/EST – direct evidence of transcriptome
What is sequence alignment ?
CTTTTCAAGGCTTA
GGCTTATTATTGC
Fragments overlaps
CTTTTCAAGGCTTA
GGCTATTATTGC
CTTTTCAAGGCTTA
GGCT-ATTATTGC
What is sequence alignment ?
CCCCATGGTGGCGGCAGGTGACAG
CATGGGGGAGGATGGGGACAGTCCGG
TTACCCCATGGTGGCGGCTTGGGAAACTT
TGGCGGCTCGGGACAGTCGCGCATAAT
CCATGGTGGTGGCTGGGGATAGTA
TGAGGCAGTCGCGCATAATTCCG
“EST clustering”
CCCCATGGTGGCGGCAGGTGACAG
CATGGGGGAGGATGGGGACAGTCCGG
TTACCCCATGGTGGCGGCTTGGGAAACTT
TGGCGGCTCGGGACAGTCGCGCATAAT
CCATGGTGGTGGCTGGGGATAGTA
TGAGGCAGTCGCGCATAATTCCG
TTACCCCATGGTGGCGGCTGGGGACAGTCGCGCATAATTCCG
consensus
Sequence alignment
• Procedure of comparing sequences
• Point mutations – easy
ACGTCTGATACGCCGTATAGTCTATCT
ACGTCTGATTCGCCCTATCGTCTATCT
gapless alignment
• More difficult example
ACGTCTGATACGCCGTATAGTCTATCT
CTGATTCGCATCGTCTATCT
• However, gaps can be inserted to get something like this
insertion × deletion
indel
ACGTCTGATACGCCGTATAGTCTATCT
----CTGATTCGC---ATCGTCTATCT
gapped alignment
Why align sequences – continuation
• The draft human genome is available
• Automated gene finding is possible
• Gene:
AGTACGTATCGTATAGCGTAA
• What does it do?
• One approach: Is there a similar gene in another
species?
• Align sequences with known genes
• Find the gene with the “best” match
Flavors of sequence alignment
• gapped x gapless
• pairwise x multiple
• global x local
Evolution of sequences
• The sequences are the products of molecular evolution.
• When sequences share a common ancestor, they tend to
exhibit similarity in their sequences, structures and
biological functions.
DNA1
DNA2
Protein1
Protein2
Sequence
similarity
Similar 3D structure
Similar function
Similar sequences produce similar proteins
However, this statement is not a rule. See Gerlt JA, Babbitt PC. Can sequence determine function? Genome Biol. 2000;1(5) PMID: 11178260
Homology
• Sequences diverge over time
• Common ancestor – homologous sequences
• The variation between sequences – changes occurred
during evolution in the form of substitutions (mutations)
and/or indels.
• Traces of evolution may still remain in certain portions of
the sequences to allow identification of the common
ancestry.
• Residues performing key roles are conserved
(preserved) by natural selection.
• Orthology vs paralogy
New stuff
Scoring systems I
• DNA and protein sequences can be aligned so that the
number of identically matching pairs is maximized.
A T T G - - - T
A – - G A C A T
• Counting the number of matches gives us a score (3 in
this case). Higher score means better alignment.
• This procedure can be formalized using substitution
matrix.
A
Identity
matrix
T
C
A
1
T
0
1
C
0
0
1
G
0
0
0
G
1
Scoring systems II
• identity matrix: NAs – OK, proteins – not enough
• AAs are not exchanged with the same probability as can
be conceived theoretically.
• For example substitution of aspartic acids D by glutamic
acid E is frequently observed. And change from aspartic
acid to tryptophan W is very rare.
D
E
W
Scoring systems II
• Why is that?
1. Triplet-based genetic code
GAT (D) → GAA (E), GAT (D) → TGG (W)
2. Both D and E have similar properties, but D and W differ
considerably. D is hydrophilic, W is hydrophobic, D → W
mutation can greatly alter 3D structure and
consequently function.
Genetic code
http://www.doctortee.com/dsu/tiftickjian/bio100/gene-expression.html
Gaps or no gaps
Scoring DNA sequence alignment (1)
• Match score:
• Mismatch score:
• Gap penalty:
+1
+0
–1
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ACGTCTGATACGCCGTATAGTCTATCT
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----CTGATTCGC---ATCGTCTATCT
• Matches: 18 × (+1)
• Mismatches: 2 × 0
• Gaps: 7 × (– 1)
Score = +11
Length penalties
• We want to find alignments that are evolutionarily likely.
• Which of the following alignments seems more likely to
you?
ACGTCTGATACGCCGTATAGTCTATCT
ACGTCTGAT-------ATAGTCTATCT

ACGTCTGATACGCCGTATAGTCTATCT
AC-T-TGA--CG-CGT-TA-TCTATCT 
• We can achieve this by penalizing more for a new gap,
than for extending an existing gap
Scoring DNA sequence alignment (2)
• Match/mismatch score:
• Origination/length penalty:
+1/+0
–2/–1
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ACGTCTGATACGCCGTATAGTCTATCT
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----CTGATTCGC---ATCGTCTATCT
• Matches: 18 × (+1)
• Mismatches: 2 × 0
• Origination: 2 × (–2)
• Length: 7 × (–1)
Score = +7
Substitution matrices
• Substitution (score) matrices show scores for amino acids
substitution. Higher score means higher probability of
mutation.
• Conservative substitutions – conserve the physical and
chemical properties of the amino acids, limit
structural/functional disruption
• Substitution matrices should reflect:
• Physicochemical properties of amino acids.
• Different frequencies of individual amino acids occuring in proteins.
• Interchangeability of the genetic code.
PAM matrices I
• How to assign scores? Let’s get nature – evolution –
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involved!
If you choose set of proteins with very similar sequences,
you can do alignment manually.
Also, if sequences in your set are similar, then there is
high probability that amino acid difference are due to
single mutation.
From the frequencies of mutations in the set of similar
protein sequences probabilities of substitutions can be
derived.
This is exactly the approach take by Margaret Dayhoff in
1978 to construct PAM (Accepted Point Mutation)
matrices.
Dayhoff, M.O., Schwartz, R. and Orcutt, B.C. (1978). "A model of Evolutionary Change in Proteins". Atlas of protein sequence and structure
(volume 5, supplement 3 ed.). Nat. Biomed. Res. Found.. pp. 345–358.
PAM matrices II
• Alignments of 71 groups of very similar (at least 85% identity)
protein sequences. 1572 substitutions were found.
• These mutations do not significantly alter the protein function.
Hence they are called accepted mutations (accepted by
natural selection).
• Probabilities that any one amino acid would mutate into any
other were calculated.
• If I know probabilities of individual amino acids, what is the
probability for the given sequence?
• Product
• But to calculate the score, we would like to sum probabilities,
not multiply. How to achieve this?
• Logarithm
Excellent discussion of the derivation and use of PAM matrices: George DG, Barker WC, Hunt LT. Mutation data matrix and its
uses. Methods Enzymol. 1990,183:333-51. PMID: 2314281.
PAM matrices III
• Dayhoff’s definition of accepted mutation was thus based
on empirically observed amino acids substitutions.
• The used unit is a PAM. Two sequences are 1 PAM apart
if they have 99% identical residues.
• PAM1 matrix is the result of computing the probability of
one substitution per 100 amino acids.
• PAM1 matrix represents probabilities of point mutations
over certain evolutionary time.
• in Drosophila 1 PAM corresponds to ~2.62 MYA
• in Human 1 PAM corresponds to ~4.58 MYA
PAM1 matrix
numbers are multiplied by 10 000
Higher PAM matrices
• What to do if I want get probabilities over much longer
evolutionary time?
• Dayhoff proposed a model of evolution that is a Markov
process.
• A case of Markov process is a linear dynamical system.
Linear dynamical system I
A new species of frog has been introduced into an area where it
has too few natural predators. In an attempt to restore the
ecological balance, a team of scientists is considering
introducing a species of bird which feeds on this frog.
Experimental data suggests that the population of frogs and
birds from one year to the next can be modeled by linear
relationships. Specifically, it has been found that if the quantities
Fk and Bk represent the populations of the frogs and birds in the
kth year, then
𝐵𝑘+1 = 0.6𝐵𝑘 + 0.4𝐹𝑘
𝐹𝑘+1 = −0.35𝐵𝑘 + 1.4𝐹𝑘
The question is this: in the long run, will the introduction of the
birds reduce or eliminate the frog population growth?
Linear dynamical system II
𝐹𝑘+1
0.6
0.4 𝐹𝑘
=
𝐵𝑘+1
−0.35 1.4 𝐵𝑘
• So this system evolves in time according to x(k+1) = Ax(k).
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Such a system is called discrete linear dynamical
system, matrix A is called transition matrix.
If we need to know the state of the system in time k = 50,
we have to compute x(50) = A50 x(0).
And the same is true for Dayhoff’s model of evolution.
If we need to obtain probability matrices for higher
percentage of accepted mutations (i.e. covering longer
evolutionary time), we do matrix powers.
Let’s say we want PAM120 – 120 mutations fixed on
average per 100 residues. We do PAM1120.
Higher PAM matrices
• Biologically, the PAM120 matrix means that in 100 amino
acids there have been 50 substitutions, while in PAM250
there have been 2.5 amino acid mutation at each side.
• This may sound unusual, but remember, that over
evolutionary time, it is possible that an alanine was
changed to glycine, then to valine, and then back to
alanine.
• These are called silent substituions.
Zvelebil, Baum, Understanding bioinformatics.
PAM 120
Positive score – frequency of
substitutions is greater than would
have occurred by random chance.
Zero score – frequency is equal to
that expected by chance.
small, polar
Negative score – frequency is less
than would have occurred by random
chance.
small, nonpolar
polar or acidic
basic
large, hydrophobic
aromatic
PAM matrices assumptions
• Mutation of amino acid is independent of previous
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mutations at the same position (Markov process
requirement).
Only PAM1 was “measured”, all other are extrapolations
(i.e. predictions based on some model).
Each amino acid position is equally mutable.
Mutations are assumed to be independent of surrounding
residues.
Forces responsible for sequence evolution over short time
are the same as these over longer times.
PAM matrices are based on protein sequences available
in 1978 (bias towards small, globular proteins)
• New generation of Dayhoff-type – e.g. PET91
Selzer, Applied bioinformatics.
How to calculate score?
2
substitution matrix