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 alphabet
side chain charge at physiological
pH 7.4
Positively charged
side chains
Negatively charged
side chains
Polar uncharged side
chains
Special
Hydrophobic side
chains
Name
Arginine
Histidine
Lysine
Aspartic Acid
Glutamic Acid
Serine
Threonine
Asparagine
Glutamine
Cysteine
Selenocysteine
Glycine
Proline\
Alanine
Leucine
Isoleucine
Methionine
Phenylalanine
Tryptophan
Tyrosine
Valine
3 letters
Arg
His
Lys
Asp
Glu
Ser
Thr
Asn
Gln
Cys
Sec
Gly
Pro
Ala
Leu
Ile
Met
Phe
Trp
Tyr
Val
1 letter
R
H
K
D
E
S
T
N
Q
C
U
G
P
A
L
I
M
F
W
Y
V
Adenine
A
Thymine
T
Cytosine
G
Guanine
C
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
pair-wise alignment × multiple sequence alignment
Flavors of sequence alignment
global alignment × local alignment
global
local
align entire sequence
stretches of sequence with
the highest density of
matches are aligned,
generating islands of
matches or subalignments in
the aligned sequences
New stuff
Evolution
common
ancestors
wikipedia.org
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
• During the time period, the molecular sequences undergo
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random changes, some of which are selected during the
process of evolution.
Selected sequences accumulate mutations, they diverge
over time.
Two sequences are homologous when they are
descended from a common ancestor sequence.
Traces of evolution may still remain in certain portions of
the sequences to allow identification of the common
ancestry.
Residues performing key roles are preserved by natural
selection, less crucial residues mutate more frequently.
Orhology, paralogy I
• Orthologs – homologous proteins from different species
that possess the same function (e.g. corresponding
kinases in signal transduction pathway in humans and
mice)
• Paralogs – homologous proteins that have different
function in the same species (e.g. two kinases in different
signal transduction pathways of humans)
However, these terms are controversially discussed:
Jensen RA. Orthologs and paralogs - we need to get it right. Genome Biol. 2001;2(8), PMID: 11532207 and references therein
Orthology, paralogy II
• Orthologs – genes separated by the
event of speciation
• Sequences are direct descendants of a
common ancestor.
• Most likely have similar domain structure, 3D structure and
biological function.
• Paralogs – genes separated by the event of genetic
duplication
• Gene duplication: An extra copy of a gene. Gene duplication is a
key mechanism in evolution. Once a gene is duplicated, the
identical genes can undergo changes and diverge to create two
different genes.
http://www.globalchange.umich.edu/globalchange1/current/lectures/speciation/speciation.html
Gene duplication
1. Unequal cross-over
2. Entire chromosome is replicated twice
• This error will result in one of the daughter cells having an extra
copy of the chromosome. If this cell fuses with another cell during
reproduction, it may or may not result in a viable zygote.
3. Retrotransposition
• Sequences of DNA are copied to RNA and then back to DNA
instead of being translated into proteins resulting in extra copies of
DNA being present within cell.
Unequal cross-over
Homologous chromosomes are
misaligned during meiosis.
The probability of misalignment is
a function of the degree of sharing
of repetitive elements.
• Comparing sequences through alignment – patterns of
conservation and variation can be identified.
• The degree of sequence conservation in the alignment
reveals evolutionary relatedness of different sequences
• The variation between sequences reflects the changes
that have occurred during evolution in the form of
substitutions and/or indels.
• Identifying the evolutionary relationships between
sequences helps to characterize the function of unknown
sequences.
• Protein sequence comparison can identify homologous
sequences from common ancestor 1 billions year ago
(BYA). DNA sequences typically only 600 MYA.
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
• For nucleotide sequences identity matrix is usually good
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enough.
For protein sequences identity matrix is not sufficient to
describe biological and evolutionary proceses.
It’s because amino acids 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.
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 hydrophylic, 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
• Thus probabilities are converted to logarithms, and an
alignment score can be calculated by summation.
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?
• i.e. I want to align sequences with far less than 85%
identity.
• Dayhoff proposed a model of evolution that is a Markov
process.
• We already met (in Lin Alg lecture) linear dynamical
system, which is a case of Markov process.
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 on 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?
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substitution matrix
Protein vs. DNA sequences
• Given the choice of aligning DNA or protein, it is often
more informative to compare protein sequences.
• There are several reasons for this:
• Many changes in DNA do not change the amino acid that is
specified.
• Many amino acids share related biophysical properties. Though
these amino acids are not identical, they can be more easily
substituted each with other. These relationships can be accounted
for using scoring systems.
• When is it appropriate to compare nucleic sequences?
• confirming the identity of DNA sequence in database search,
searching for polymorphisms, confirming identity of cloned cDNA