The document discusses various topics related to gene prediction including primer designing, restriction mapping, and gene prediction. It provides guidelines for primer designing such as avoiding non-specific binding and dimer formation between primers. It also discusses key concepts in gene prediction such as reading frame consistency between exons, codon and dicodon frequencies that can help distinguish coding from non-coding regions, and position specific scoring matrices to predict translation start sites.

1. IICB Course work, 27th Nov 2014

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

3. Primer Designing
 No non-specific binding
 Melting temperature
 Should not be forming dimers with itself or other
primers.
The temperature at which 50% of the oligonucleotide and its perfect
complement are in duplex

4. 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. Primer self-complementarity (ability to form
2o structures such as hairpins) should be avoided;
6. 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

5. Reference:
http://bioweb.uwlax.edu/genweb/molecular/seq_anal/primer_design/primer_design.htm

6. ATGACGCATAGCAGCATAGACGCATAGACGACGCATAGACGACATAGAGACG
AGACGCAGAATAGAGGACGAGCAATGATGATAGAAATGACGCATAGCAGCAT
AGACGCATAGACGACGCATAGACGACATAGAGACGAGACGCAGAATAGAGGA
CGAGCAATGATGATAGAAATGACGCATAGCAGCATAGACGCATAGACGACGC
ATAGACGACATAGAGACGAGACGCAGAATAGAGGACGAGCAATGATGATAGA
AATGACGCATAGCAGCATAGACGCATAGACGACGCATAGACGACATAGAGAC
GAGACGCAGAATAGAGGACGAGCAATGATGATAGAAATGACGCATAGCAGCA
TAGACGCATAGACGACGCATAGACGACATAGAGACGAGACGCAGAATAGAGG
ACGAGCAATGATGATAGAAATGACGCATAGCAGCATAGACGCATAGACGACG
CATAGACGACATAGAGACGAGACGCAGAATAGAGGACGAGCAATGATGATAG
AAATGACGCATAGCAGCATAGACGCATAGACGACGCATAGACGACATAGAGA
CGAGACGCAGAATAGAGGACGAGCAATGATGATAGAAATGACGCATAGCAGC
ATAGACGCATAGACGACGCATAGACGACATAGAGACGAGACGCAGAATAGAG
GACGAGCAATGATGATAGAAATGACGCATAGCAGCATAGACGCATAGACGAC
GCATAGACGACATAGAGACGAGACGCAGAATAGAGGACGAGCAATGATGATA
GAAATGACGCATAGCAGCATAGACGCATAGACGACGCATAGACGACATAGAG
ACGAGACGCAGAATAGAGGACGAGCAATGATGATAGAAATGACGCATAGCAG
CATAGACGCATAGACGACGCATAGACGACATAGAGACGAGACGCAGAATAGA
GGACGAGCAATGATGATAGAA
Oligo Analyzer: http://simgene.com/Primer3

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

9. 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 ….!

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

11. Genes are all about Patterns – real
life example

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

13. 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.

14. 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 0 1

15. Gene Structure – reading frame consistency
 Neighboring exons of a gene should be frame-consistent
exon1 (i, j) in frame a and exon2 (m, n) in frame b are consistent if
b = (m - j - 1 + a) mod 3
2/17/2016
GATGGGACGACAGATAGGTGATTAAGATGGTAGGCCGAGTGGTC
Exon1 (1,16) -> Frame = a = 0 ; i = 1 and j = 16
Case1: Exon2 (33,100): Frame = b = 1; m = 33 and n = 100
Case2: Exon2 (40,100): Frame = b = 1; m = 40 and n =100
GATGGGACGACAGATAGGTGATTAAGATGGTAGGCCGAGTGGTC
1 16 33
1 16 40
Frame 0 Frame 1
Frame 0
Frame 1
…33,36,39,42,45,48,51…

16. 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

17. ALA ARG ASN AS
P
CYS GLU GLN GLY HIS ILE LEU LYS MET PHE PRO SER THR TRP TYR VAL
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

19. 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!

20. 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?

21. 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
 Dicodon frequencies are key signal used for coding region
detection; all gene finding programs use this information
Regions consisting of dicodons that mostly tend
to be in coding regions are probably coding
regions; otherwise non-coding regions

22. 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
 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?
ATG
A
C
T
G
-1-2-4 -3 +3 +5+4 +6
CACC ATG GC
TCGA ATG TT
TCTAGAAGATGGCAGTGGCGAAGA
TCTAGAAAATGACAGTGGCGAAGA
TCTAGAAAATGGCAGTAGCGAAGA
TCTACT A AATGATAGTAGCGAAGA
A 0,0,0,100 ,0, 75,100, 75 ATG
T 100,0,100,0,0, 25, 0, 0 ATG
G 0, 0, 0, 0, 75 ,0, 0, 25 ATG
C 0,100,0,0, 25 ,0, 0, 0 ATG

23. 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)
A
C
T
G
CACC ATG GC TCGA ATG TT
log (58/25) + log (49/25) + log (40/25) +
log (50/25) + log (43/25) + log (39/25) =
0.37 + 0.29 + 0.20 + 0.30 + 0.24 + 0.29
= 1.69
log (6/25) + log (6/25) + log (15/25) + log
(15/25) + log (13/25) + log (14/25) =
-(0.62 + 0.62 + 0.22 + 0.22 + 0.28 + 0.25)
= -2.54
The model captures our intuition!

24. Practice…
TCTAGAAGATGGCAGTGGCGAAGA
TCTAGAAAATGACAGTTGCGAAGA
TCTACAAAATGGCAGTAGCGAAGA
TCTACT AAATGATAGTCGCGAAGA
CAACAATG GC TCGA ATG TT

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

26. 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

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

28. Practice
 GTCAGCCTCCACGACCAACTAATCGGCACACAGAACACGCCATCGTGAGGCCAGAGCGCGTAGAAGATAAGATTCTTGATGCTTCATCAATA
TGCATTGAGGCTAAGAGCGTGTATATAAAAAGTAAATAAGAGCGTGTATATAAAAACCAAACAGCCACACCTCGCGAATTGTGCCGTTTAGC
GTTGTGGACACTTCGTCGATTGCTGCATCGACTCTAAAGCGCGTTGAGTAGGCTTCCTCTTGCGCGCCGAACGACGGGATCCTCACAGTAAT
TCCTGTGCCGGGGGACTTGCTTCTGGTGCGGGGGCAGAGGATTCCGGAATGGCGCTCTCCGCCACTGGTTCTTCGACGAGAAGCACCGTTTC
TGCTGCACTCACACTTAAAGCCGGACTCGCAGCGCACTCGTGCACCACGTGACCCCCATCATGTTCTGCATCATATGCTTCCAGCACCTGATC
CCCCTCATGCTCTGCATAAGATGCCACAATAGAGAATGATAGATCCTGTAGTTCTGCCAGGGACACTCCATCCTCTCCATCTGTCTCAGATAA
TCTCGCTTCGGATGAGAGATCTTCAACTACCATGCGCCGCGTGCGTCCGTGTCCCGGTCTGACAAATTGACGGCGGTTATCAGCACCCGGAC
AGTGAATGCGGGATTGGATACATGCAGTGACAATGTGACAGCACATACCGTGTTTTCGGCTATACGCACATGTGGATCCTTTGCTTCTAGTAT
CAACAATCCATCCACCACTAGGCATACCGTCAGTCTGCATTCTTCTAGAGTTGATAGATTTGATGGACTTTTTTATCTGCTTCTTCTCCTCCGC
CGTTAATCGAACCGGCCTCTTCATAACTCGGTACAGATCTGCAGGCACATTCCCAACAGGAGGGATGCGCTCCACTGAAAGGCAACCGTGGT
TGTACATAAACCGATATAGTTTCAGCAAGCGGTCCGAGGCGGTGACAATAGTCGCAAAGGTCGCCTCGCGGGTCAGAAAAGCGAGTCGAGC
ACCATCCAGATTGTGTATCAATTCCCGAGGAGTTGCCAAACCGTCAGGACACTGCAACTTCACTTTGCGGTGGTACTGCTCCAACGGATTGTT
GGTTGCAGCATAGCCACTGGGTGTGTAGAACGCCTGCCATCTATAAAATCGTTTTGAATTGACCCAGAATCGTTTCAGATGATCCGTTAATTT
CCCGGCAGGTGAACCACGTGGGTATCTACTCCACCGGAGTAAAACTCTAGATTTTACTGAGTCAAAATTTTCTTCGCTTGCATAGTGCATGTC
AAACAGGTCTGCGAATATCGAATGTGTATCATCCATGGTTACATGGTGTAGCCGAGCCTGTTTCCACACGTTTTGTGTTACATGAAACCAACA
CATCAACAGCGTGGTGCCTGGAAGCTCCGATACACAAGCGTTGAACTGCGCCTTGCAAGCATCCGACATCACAAAGCGGGCTGAAAACTGGG
CATTGGATGTGTCAGAGCACACGCGTTTGATATAGCGGATGCACCAGCCTATATCAATCGCACGCTTCTGTGGAGTGCAAAAGTATGCCAAC
AAAAAAATTGTCCGCATCTATCTGAATATCCAAACGCAAAGACCAGGTAGCCATTGATTACCACGCTATGCGTGCTGTCTACGTGAAAAATC
GTCGTGCAATCAACCCGACCTTGCACGCACGTCAATGTAATCACGGACCAGGTTTAGACAGGTCATGCCAATCCTGAACGGATATGTAGTAG
ATCCATCGCCTAGATGGGAAATGCTGCCTTCAGAATCAGGACAATGGTCAGGCTGCGAGTCGCACAAAATCATTATCTCGCGATCTGACACG
GTCTCCAAATCCAGCAGATCGTGTAACGGCCCGTCACACAGCTCGATCACCG ATGCCATTGAGTTTCTAGGGTTATCCCTCCTT