The document discusses topics to be covered in an IICB course on 8th December 2012, including primer designing, restriction mapping, and gene prediction. It provides information and guidelines on these topics, such as the appropriate length and properties of primers, the four types of restriction enzymes, and methods for gene prediction including patterns, frame consistency, dicodon frequencies, position-specific scoring matrices, and coding potential. Relevant references and websites discussing these techniques are also listed.

1. IICB Course work, 8th Dec 2012

2. Topics to be covered
 Primer designing
 Restriction mapping
 Gene Prediction

3. ATGACGCATAGCAGCATAGACGCATAGACGACGCATAGACGACATAGAGACG
AGACGCAGAATAGAGGACGAGCAATGATGATAGAAATGACGCATAGCAGCAT
AGACGCATAGACGACGCATAGACGACATAGAGACGAGACGCAGAATAGAGGA
CGAGCAATGATGATAGAAATGACGCATAGCAGCATAGACGCATAGACGACGC
ATAGACGACATAGAGACGAGACGCAGAATAGAGGACGAGCAATGATGATAGA
AATGACGCATAGCAGCATAGACGCATAGACGACGCATAGACGACATAGAGAC
GAGACGCAGAATAGAGGACGAGCAATGATGATAGAAATGACGCATAGCAGCA
TAGACGCATAGACGACGCATAGACGACATAGAGACGAGACGCAGAATAGAGG
ACGAGCAATGATGATAGAAATGACGCATAGCAGCATAGACGCATAGACGACG
CATAGACGACATAGAGACGAGACGCAGAATAGAGGACGAGCAATGATGATAG
AAATGACGCATAGCAGCATAGACGCATAGACGACGCATAGACGACATAGAGA
CGAGACGCAGAATAGAGGACGAGCAATGATGATAGAA
ATGACGCATAGCAGCATAGACGCATAGACGACGCATAGACGACATAGAGACG
AGACGCAGAATAGAGGACGAGCAATGATGATAGAAATGACGCATAGCAGCAT
AGACGCATAGACGACGCATAGACGACATAGAGACGAGACGCAGAATAGAGGA
CGAGCAATGATGATAGAAATGACGCATAGCAGCATAGACGCATAGACGACGC
ATAGACGACATAGAGACGAGACGCAGAATAGAGGACGAGCAATGATGATAGA
AATGACGCATAGCAGCATAGACGCATAGACGACGCATAGACGACATAGAGAC
GAGACGCAGAATAGAGGACGAGCAATGATGATAGAA
Oligo Analyzer:
http://www.idtdna.com/analyzer/Applications/OligoAnalyzer/
http://www.idtdna.com/Analyzer/Applications/Instructions/Default.as
px?AnalyzerDefinitions=true

4. Primer Designing
 No non-specific binding
 Melting temperature
 Should not be forming dimers with itself or other
primers.

5. Some thoughts
1. primers should be 17-28 bases in length;
2. base composition should be 50-60% (G+C);
3. primers should end (3') in a G or C, or CG or GC: this prevents
"breathing" of ends and increases efficiency of priming;
4. Tms between 55-80oC are preferred;
5. 3'-ends of primers should not be complementary (ie. base
pair), as otherwise primer dimers will be synthesised
preferentially to any other product;
6. primer self-complementarity (ability to form 2o structures
such as hairpins) should be avoided;
7. runs of three or more Cs or Gs at the 3'-ends of primers may
promote mispriming at G or C-rich sequences (because of
stability of annealing), and should be avoided.
Adapted from: Innis and Gelfand,1991

6. Reference:
http://bioweb.uwlax.edu/genweb/molecular/seq_anal/primer_design/primer_design.ht

7. Restriction Analysis
 Found in Bacteria and archea.
 4 types
 Type -1: cleavage remote to recognition site (methylase
activity)
 Type-2: cleavage within a specific distance
 Type-3: Cleavage within a short distance
 Type-4: Cleaves modified DNA (methylated)
Ref: http://insilico.ehu.es/restriction/long_seq/
http://molbiol-tools.ca/Restriction_endonuclease.htm

8. Gene Prediction
 Patterns
 Frame Consistency
 Dicodon frequencies
 PSSMs
 Coding Potential and Fickett’s statistics
 Fusion of Information
 Sensitivity and Specificity
 Prediction programs
 Known problems

9. Genes are all about Patterns – real
life example

10. Gene Prediction Methods
Common sets of rules
 Homology
 Ab initio methods
 Compositional information
 Signal information

11. Pattern Recognition in Gene
Finding
 atgttggacagactggacgcaagacgtgtggatgaactcgttttggagctgaac
aaggctctatacgtacttaatcaagcggggcgtttgtggagcgagt
tacttcacaaaaagctagccaatttgggttcaatgcagtgcctgaccgacatggg
tatgtattagtaacgtttggaagaagaaactgttgtggttggtgt
ttatgcagacaatctacaggtgactgcaacgaattcaactctcgtggacagttttt
tcgttgatttacaggacctctcggtaaaggactatgaagaggtg
acaaaattcttggggatgcgcatttcttatgcgcctgaaaatgggtatgattatat
atcgagaagtgacaacccgggaaatgataaaggataa
atggagaggatgctggagacggtcaagacgaccatcacccctgcgcaggcaatgaag
ctgtttactgcacccaaagaacctcaagcgaacctggcccgag
cacttcatgtacttggtggccatctcggaggcctgcggtggtacttagtcctgaataacg
tcgtgccgtacgcgtccgcggatctacgaacggtcctgat
agccaaagtggacggcacgcgtgtcgactacctacagcaagctgaggaactggcgca
tttcgcgcaatcctgggagcttgaagcgcgcacgaagaacatt
We need to study the basic structures of genes first ….!

12. Gene Structure – Common sets of
rules
• Generally true: all long (> 300 bp) orfs in prokaryotic genomes
encode genes
But this may not necessarily be true for eukaryotic genomes
• Eukaryotic introns begin with GT and end with AG(donor and
acceptor sites)
– CT(A/G)A(C/T) 20-50 bases upstream of acceptor site.

13. Gene Structure
 Each coding region (exon or whole gene) has a fixed
translation frame
 A coding region always sits inside an ORF of same
reading frame
 All exons of a gene are on the same strand.
 Neighboring exons of a gene could have different
reading frames .
 Exons need to be Frame consistent!
GATGGGACGACAGATAAGGTGATAGATGGTAGGCAGCAG
0 3 6 9 12 01

14. Gene Structure – reading frame consistency
 Neighboring exons of a gene should be frame-consistent
Frame 0 Frame 2
GATGGGACGACAGATAGGTGATTAAGATGGTAGGCCGAGTGGTC
1 16 33
GATGGGACGACAGATAGGTGATTAAGATGGTAGGCCGAGTGGTC
1 16 40
Frame 0
Frame 2
Exon1 (1,16) -> Frame = a = 0 ; i = 1 and j = 16
Case1: Exon2 (33,100): Frame = b = 2; m = 33 and n = 100
Case2: Exon2 (40,100): Frame = b = 2; m = 40 and n =100
exon1 (i, j) in frame a and exon2 (m, n) in frame b are consistent if
b = (m - j - 1 + a) mod 3
12/7/2012
…31,34,37,40,43,46,49,52…

15. Codon Frequencies
 Coding sequences are translated into protein sequences
 We found the following – the dimer frequency in protein sequences is
NOT evenly distributed
 Organism specific!!!!!!!!!!!
The average frequency is ¼% (1/20
* 1/20 = 1/400 = ¼%)
Some amino acids prefer to be next
to each other
Some other amino acids prefer to
be not next to each other

16. ALA ARG ASN AS CYS GLU GLN GLY HIS ILE LEU LYS MET PHE PRO SER THR TRP TYR VAL
P
A .99 .5 .27 .45 .13 .52 .34 .51 .19 .38 .83 .46 .2 .31 .37 .73 .56 .09 .18 .65
R .5 .5 .2 .3 .1 .4 .3 .4 .18 .25 .63 .37 .14 .22 .26 .54 .34 .08 .17 .46
N .31 .19 .11 .2 .05 .23 .23 .26 .07 .13 .27 .16 .07 .11 .15 .24 .16 .04 .08 .27
D .54 .32 .17 .47 .08 .51 .19 .42 .13 .25 .48 .26 .12 .20 .24 .40 .29 .06 .14 .5
C .14 .11 .05 .09 .04 .09 .06 .13 .04 .08 .14 .08 .03 .06 .07 .14 .10 .02 .04 .13
E .57 .43 .22 .42 .09 .59 .30 .33 .16 .28 .64 .40 .17 .20 .21 .44 .36 .06 .16 .44
Q .34 .31 .11 .20 .06 .27 .29 .20 .12 .15 .45 .21 .10 .13 .17 .29 .22 .05 .10 .29
G .50 .39 .22 .37 .11 .37 .21 .50 .16 .28 .50 .33 .14 .23 .21 .54 .35 .07 .17 .46
H .21 .17 .07 .14 .04 .16 .10 .17 .08 .09 .22 .10 .05 .09 .12 .17 .11 .03 .06 .21
I .37 .25 .13 .27 .08 .27 .15 .27 .09 .15 .34 .22 .08 .14 .18 .29 .21 .04 .11 .32
L .79 .65 .30 .53 .16 .62 .45 .50 .25 .31 .97 .47 .19 .32 .44 .71 .49 .10 .22 .67
K .43 .41 .19 .26 .08 .35 .24 .26 .14 .20 .49 .41 .13 .17 .20 .37 .32 .07 .15 .33
M .23 .17 .09 .17 .04 .19 .12 .14 .06 .10 .25 .15 .07 .08 .11 .20 .17 .03 .06 .17
F .30 .20 .11 .22 .07 .22 .13 .26 .08 .14 .33 .15 .08 .14 .14 .27 .18 .04 .10 .28
P .35 .28 .13 .23 .05 .27 .16 .24 .10 .17 .39 .19 .10 .15 .26 .42 .29 .04 .09 .33
S .68 .51 .26 .46 .14 .43 .26 .55 .17 .33 .68 .38 .18 .28 .36 .93 .55 .09 .17 .56
T .51 .36 .19 .29 .10 .31 .21 .37 .12 .25 .52 .32 .13 .21 .31 .54 .42 .07 .13 .39
W 0.07 .08 .05 .06 .02 .06 .05 .06 .03 .06 .12 .07 .03 .04 .04 .09 .07 .02 .03 .07
Y .21 .16 .09 .15 .05 .16 .09 .17 .06 .10 .23 .11 .06 .10 .1 .17 .13 .03 .07 .2
V .69 .42 .24 .46 .13 .48 .31 .42 .18 .29 .72 .37 .16 .27 .33 .55 .42 .07 .18 .61

18. Dicodon Frequencies
 Believe it or not – the biased (uneven) dimer frequencies are the
foundation of many gene finding programs!
 Basic idea – if a dimer has lower than average dimer frequency; this
means that proteins prefer not to have such dimers in its sequence;
Hence if we see a dicodon encoding this dimer, we may
want to bet against this dicodon being in a coding
region!

19. Dicodon Frequencies - Examples
 Relative frequencies of a di-codon in coding versus non-coding
 frequency of dicodon X (e.g, AAAAAA) in coding region, total number of occurrences of
X divided by total number of dicocon occurrences
 frequency of dicodon X (e.g, AAAAAA) in noncoding region, total number of
occurrences of X divided by total number of dicodon occurrences
In human genome, frequency of dicodon “AAA AAA” is
~1% in coding region versus ~5% in non-coding region
Question: if you see a region with many “AAA AAA”,
would you guess it is a coding or non-coding region?

20. Basic idea of gene finding
 Most dicodons show bias towards either coding or non-coding
regions; only fraction of dicodons is neutral
 Foundation for coding region identification
Regions consisting of dicodons that mostly tend
to be in coding regions are probably coding
regions; otherwise non-coding regions
 Dicodon frequencies are key signal used for coding region
detection; all gene finding programs use this information

21. Prediction of Translation Starts Using PSSM
 Certain nucleotides prefer to be in certain position around start
“ATG” and other nucleotides prefer not to be there 75,100, 75 ATG
TCTAGAAGATGGCAGTGGCGAAGA
A 0,0,0,100 ,0,
TCTAGAAAATGACAGTGGCGAAGA
T 100,0,100,0,0, 25, 0, 0 ATG
TCTAGAAAATGGCAGTAGCGAAGA
G 0, 0, 0, 0, 75 ,0, 0, 25 ATG
TCTACT A AATGATAGTAGCGAAGA ATG C 0,100,0,0, 25 ,0, 0, 0 ATG
A
C
G
T
-4 -3 -2 -1 +3 +4 +5 +6
CACC ATG GC
TCGA ATG TT
 The “biased” nucleotide distribution is information! It is a basis for
translation start prediction
 Question: which one is more probable to be a translation start?

22. Prediction of Translation Starts
 Mathematical model: Fi (X): frequency of X (A, C, G, T) in
position i
 Score a string by log (Fi (X)/0.25)
CACC ATG GC TCGA ATG TT
log (58/25) + log (49/25) + log (40/25) + log (6/25) + log (6/25) + log (15/25) + log
log (50/25) + log (43/25) + log (39/25) = (15/25) + log (13/25) + log (14/25) =
0.37 + 0.29 + 0.20 + 0.30 + 0.24 + 0.29 -(0.62 + 0.62 + 0.22 + 0.22 + 0.28 + 0.25)
= 1.69 = -2.54
The model captures our intuition!
A
C
G
T
12/7/2012

23. Evaluation of Gene prediction
TP FP TN FN TP
Real
Predicted
• Sensitivity = No. of Correct exons/No. of actual exons(Measurement of False
negative) -> How many are discarded by mistake
Sn = TP/TP+FN
• Specificity = No. of Correct exons/No. of predicted exons(Measurement of
False positive) -> How many are included by mistake
Sp=TP/TP+FP
• CC = Metric for combining both
(TP*TN) – (FN*FP)/sqrt( (TP+FN)*(TN+FP)*(TP+FP)*(TN+FN) )
12/7/2012

24. Challenges of Gene finder
• Alternative splicing
• Nested/overlapping genes
• Extremely long/short genes
• Extremely long introns
• Non-canonical introns
• Split start codons
• UTR introns
• Non-ATG triplet as the start codon
• Polycistronic genes
• Repeats/transposons

25. Known Gene Finders
 GeneScan
 GeneMarkHMM
 Fgenesh
 GlimmerHMM
 GeneZilla
 SNAP
 PHAT
 AUGUSTUS
 Genie
Ref: http://bioinf.uni-greifswald.de/augustus/submission