Download Molecular Data - Bilkent University

Molecular Data
DNA/RNA
Protein
Expression
Interaction
A sequence

A sequence is a linear set of
characters (sequence elements)
representing nucleotides or amino
acids
http://www.cmu.edu/bio/education/courses/03310/LectureNotes/
Character representation of
sequences

DNA or RNA


use 1-letter codes (e.g., A,C,G,T)
protein

use 1-letter codes

can convert to/from 3-letter codes
http://www.cmu.edu/bio/education/courses/03310/LectureNotes/
The I.U.B. Code proposed by
International Union of Biochemistry
A, C, G, T, U
R = A, G (puRine)
Y = C, T (pYrimidine)
S = G, C (Strong hydrogen bonds)
W = A, T (Weak hydrogen bonds)
M = A, C (aMino group)
K = G, T (Keto group)
B = C, G, T (not A)
D = A, G, T (not C)
H = A, C, T (not G)
V = A, C, G (not T/U)
N = A, C, G, T/U (iNdeterminate) X or - are sometimes
used
DNA code
Amino Acid
Abbreviation
DNA Codons
Alanine
Ala
GCA, GCC, GCG, GCT
Cysteine
Cys
TGC, TGT
Aspartic Acid
Asp
GAC, GAT
Glutamic Acid
Glu
GAA, GAG
Phenylalanine
Phe
TTC, TTT
Glycine
Gly
GGA, GGC, GGG, GGT
Histidine
His
CAC, CAT
Isoleucine
Ile
ATA, ATC, ATT
Lysine
Lys
AAA, AAG
Leucine
Leu
TTA, TTG, CTA, CTC, CTG, CTT
Methionine
Met
ATG
Asparagine
Asn
AAC, AAT
Proline
Pro
CCA, CCC, CCG, CCT
Glutamine
Gln
CAA, CAG
Arginine
Arg
CGA, CGC, CGG, CGT
Serine
Ser
TCA, TCC, TCG, TCT, AGC, AGT
Threonine
Thr
ACA, ACC, ACG, ACT
Valine
Val
GTA, GTC, GTG, GTT
Tryptophan
Trp
TGG
Tyrosine
Tyr
TAC, TAT
Stop
.
TAA, TAG, TGA
Fasta format
>gi|17978494|ref|NM_078467.1| Homo sapiens cyclin-dependent kinase inhibitor
AGCTGAGGTGTGAGCAGCTGCCGAAGTCAGTTCCTTGTGGAGCCGGAGCTGGGCGCGGATTCGCCGAGGC
ACCGAGGCACTCAGAGGAGGTGAGAGAGCGGCGGCAGACAACAGGGGACCCCGGGCCGGCGGCCCAGAGC
CGAGCCAAGCGTGCCCGCGTGTGTCCCTGCGTGTCCGCGAGGATGCGTGTTCGCGGGTGTGTGCTGCGTT
CACAGGTGTTTCTGCGGCAGGCGCCATGTCAGAACCGGCTGGGGATGTCCGTCAGAACCCATGCGGCAGC
AAGGCCTGCCGCCGCCTCTTCGGCCCAGTGGACAGCGAGCAGCTGAGCCGCGACTGTGATGCGCTAATGG
CGGGCTGCATCCAGGAGGCCCGTGAGCGATGGAACTTCGACTTTGTCACCGAGACACCACTGGAGGGTGA
CTTCGCCTGGGAGCGTGTGCGGGGCCTTGGCCTGCCCAAGCTCTACCTTCCCACGGGGCCCCGGCGAGGC
CGGGATGAGTTGGGAGGAGGCAGGCGGCCTGGCACCTCACCTGCTCTGCTGCAGGGGACAGCAGAGGAAG
ACCATGTGGACCTGTCACTGTCTTGTACCCTTGTGCCTCGCTCAGGGGAGCAGGCTGAAGGGTCCCCAGG
TGGACCTGGAGACTCTCAGGGTCGAAAACGGCGGCAGACCAGCATGACAGATTTCTACCACTCCAAACGC
CGGCTGATCTTCTCCAAGAGGAAGCCCTAATCCGCCCACAGGAAGCCTGCAGTCCTGGAAGCGCGAGGGC
CTCAAAGGCCCGCTCTACATCTTCTGCCTTAGTCTCAGTTTGTGTGTCTTAATTATTATTTGTGTTTTAA
TTTAAACACCTCCTCATGTACATACCCTGGCCGCCCCCTGCCCCCCAGCCTCTGGCATTAGAATTATTTA
AACAAAAACTAGGCGGTTGAATGAGAGGTTCCTAAGAGTGCTGGGCATTTTTATTTTATGAAATACTATT
TAAAGCCTCCTCATCCCGTGTTCTCCTTTTCCTCTCTCCCGGAGGTTGGGTGGGCCGGCTTCATGCCAGC
TACTTCCTCCTCCCCACTTGTCCGCTGGGTGGTACCCTCTGGAGGGGTGTGGCTCCTTCCCATCGCTGTC
ACAGGCGGTTATGAAATTCACCCCCTTTCCTGGACACTCAGACCTGAATTCTTTTTCATTTGAGAAGTAA
ACAGATGGCACTTTGAAGGGGCCTCACCGAGTGGGGGCATCATCAAAAACTTTGGAGTCCCCTCACCTCC
TCTAAGGTTGGGCAGGGTGACCCTGAAGTGAGCACAGCCTAGGGCTGAGCTGGGGACCTGGTACCCTCCT
GGCTCTTGATACCCCCCTCTGTCTTGTGAAGGCAGGGGGAAGGTGGGGTCCTGGAGCAGACCACCCCGCC
TGCCCTCATGGCCCCTCTGACCTGCACTGGGGAGCCCGTCTCAGTGTTGAGCCTTTTCCCTCTTTGGCTC
CCCTGTACCTTTTGAGGAGCCCCAGCTACCCTTCTTCTCCAGCTGGGCTCTGCAATTCCCCTCTGCTGCT
GTCCCTCCCCCTTGTCCTTTCCCTTCAGTACCCTCTCAGCTCCAGGTGGCTCTGAGGTGCCTGTCCCACC
CCCACCCCCAGCTCAATGGACTGGAAGGGGAAGGGACACACAAGAAGAAGGGCACCCTAGTTCTACCTCA
GGCAGCTCAAGCAGCGACCGCCCCCTCCTCTAGCTGTGGGGGTGAGGGTCCCATGTGGTGGCACAGGCCC
CCTTGAGTGGGGTTATCTCTGTGTTAGGGGTATATGATGGGGGAGTAGATCTTTCTAGGAGGGAGACACT
GGCCCCTCAAATCGTCCAGCGACCTTCCTCATCCACCCCATCCCTCCCCAGTTCATTGCACTTTGATTAG
CAGCGGAACAAGGAGTCAGACATTTTAAGATGGTGGCAGTAGAGGCTATGGACAGGGCATGCCACGTGGG
CTCATATGGGGCTGGGAGTAGTTGTCTTTCCTGGCACTAACGTTGAGCCCCTGGAGGCACTGAAGTGCTT
AGTGTACTTGGAGTATTGGGGTCTGACCCCAAACACCTTCCAGCTCCTGTAACATACTGGCCTGGACTGT
TTTCTCTCGGCTCCCCATGTGTCCTGGTTCCCGTTTCTCCACCTAGACTGTAAACCTCTCGAGGGCAGGG
ACCACACCCTGTACTGTTCTGTGTCTTTCACAGCTCCTCCCACAATGCTGAATATACAGCAGGTGCTCAA
TAAATGATTCTTAGTGACTTTAAAAAAAAAAAAAAAAAAAA
Sequence Content

Mononucleotide frequencies


GC content
Dinucleotide frequencies

CpG islands
GC content is non-random
Lander et al
GC content and expression
Determining mononucleotide
frequencies



Alphabet: A T C G
Count how many times each nucleotide
appears in sequence
Divide (normalize) by total number of
nucleotides


fA  mononucleotide frequency of A (frequency
that A is observed)
pAmononucleotide probability that a nucleotide
will be an A
http://www.cmu.edu/bio/education/courses/03310/LectureNotes/
Determining dinucleotide
frequencies





Make 4 x 4 matrix, one element for each ordered
pair of nucleotides
Set all elements to zero
Go through sequence linearly, adding one to matrix
entry corresponding to the pair of sequence
elements observed at that position
Divide by total number of dinucleotides
fAC  dinucleotide frequency of AC (frequency that
AC is observed out of all dinucleotides)
http://www.cmu.edu/bio/education/courses/03310/LectureNotes/
Dinucleotide counts
A
T
C
G
A
0
0
0
0
T
0
0
0
0
C
0
0
0
0
G
0
0
0
0
Create a 4 x 4 matrix
Set all cells to zeros
Use a window of size 2 and add
1 to each cell of the matrix
when encountering the
specified dinucleotide
ATTCGACCAGAG
Dinucleotide counts
A
T
C
G
A
0
1
1
2
T
0
1
1
0
C
1
0
1
1
G
2
0
0
0
ATTCGACCAGAG
Observed and expected
frequencies
http://www.maths.lth.se/bioinformatics/publications/BasicE_2005.pdf
Observed and expected
frequencies
http://www.maths.lth.se/bioinformatics/publications/BasicE_2005.pdf
Dinucleotide frequencies in
genome
http://www.lapcs.univ-lyon1.fr/~piau/mps/Poster-CpG.pdf
Sequence features

A sequence feature is a pattern that is
observed to occur in more than one
sequence and (usually) to be
correlated with some function
http://www.cmu.edu/bio/education/courses/03310/LectureNotes/
Sequence features






promoters
transcription initiation sites
transcription termination sites
polyadenylation sites
ribosome binding sites
protein features
http://www.cmu.edu/bio/education/courses/03310/LectureNotes/
Consensus sequences


A consensus sequence is a
sequence that summarizes or
approximates the pattern observed in
a group of aligned sequences
containing a sequence feature
Consensus sequences are regular
expressions
http://www.cmu.edu/bio/education/courses/03310/LectureNotes/
Occurences

Example: recognition site for a restriction enzyme



EcoRI recognizes GAATTC
AccI recognizes GTMKAC
Basic Algorithm




Start with first character of sequence to be searched
See if enzyme site matches starting at that position
Advance to next character of sequence to be searched
Repeat previous two steps until all positions have been
tested
http://www.cmu.edu/bio/education/courses/03310/LectureNotes/
Statistics of pattern
appearance


Goal: Determine the significance of
observing a feature (pattern)
Method: Estimate the probability that a
pattern would occur randomly in a given
sequence. Three different methods



Assume all nucleotides are equally frequent
Use measured frequencies of each nucleotide
(mononucleotide frequencies)
Use measured frequencies with which a given
nucleotide follows another (dinucleotide
frequencies)
http://www.cmu.edu/bio/education/courses/03310/LectureNotes/
Example 1


What is the probability of observing the
sequence feature ART (A followed by a
purine, either A or G, followed by a T)?
Using observed mononucleotide
frequencies:


pART = pA (pA + pG) pT
Using equal mononucleotide frequencies


pA = pC = pG = pT = 1/4
pART = 1/4 * (1/4 + 1/4) * 1/4 = 1/32
http://www.cmu.edu/bio/education/courses/03310/LectureNotes/
Example 1: using
mononucleotide frequencies

Using equal mononucleotide
frequencies



pA = pC = pG = pT = 1/4
pART = 1/4 * (1/4 + 1/4) * 1/4 = 1/32
Using observed mononucleotide
frequencies:

pART = pA (pA + pG) pT
Example 1: using dinucleotide
frequencies

pART=pA(p*AAp*AT+p*AGp*GT)
Example 2:




What is the probability of observing the
sequence feature ARYT (A followed by a
purine {either A or G}, followed by a
pyrimidine {either C or T}, followed by a T)?
Using equal mononucleotide frequencies
pA = pC = pG = pT = 1/4
pARYT = 1/4 * (1/4 + 1/4) * (1/4 + 1/4) * 1/4
= 1/64
http://www.cmu.edu/bio/education/courses/03310/LectureNotes/