1. ORF Calling

2. ORF Calling Why? Need to know protein sequence
Protein sequence is usually what does the work
Functional studies
Crystallography
Proteomics
Similarity studies
Proteins are better for remote similarities than DNA sequences
Protein sequences change slower than DNA sequences

3. ORF Calling Extrinsic gene calling Intrinsic gene calling
Compare your DNA sequences to known sequences. Needs other sequences that are known!
Intrinsic gene calling
Only use information in your DNA sequences. Does not use other information.

4. Extrinsic gene calling
Start with DNA sequence
Translate in all 6 reading frames

5. Why are there 6 reading frames?
AG TAA AAC TTT AAT TGT TGG TTA A 3
A GTA AAA CTT TAA TTG TTG GTT AA 2
AGT AAA ACT TTA ATT GTT GGT TAA 1
AGT AAA ACT TTA ATT GTT GGT TAA
TCA TTT TGA AAT TAA CAA CCA ATT
| | | | | | | | | | | | | | | | | | | | | | | |
TCA TTT TGA AAT TAA CAA CCA ATT -1
TC ATT TTG AAA TTA ACA ACC AAT T -2
T CAT TTT GAA ATT AAC AAC CAA TT -3

6. Extrinsic gene calling
Start with DNA sequence
Translate in all 6 reading frames
Compare your sequence to known protein sequences
Find the ends of each, and call those genes!

7. } For example DNA sequence Similar protein sequences e.g. from BLAST
Protein encoding
gene
DNA
sequence }
Similar
protein
sequences
e.g. from BLAST

8. Uses of extrinsic calling
This is how (most) metagenome ORF calling is done
Eukaryotic ORF calling – especially using EST sequences

9. Problems with extrinsic calling
Very slow (depending on search algorithm)
Dependent on your database
Only finds known genes

10. Alternatives to extrinsic gene calling
Intrinsic gene calling
Ab initio gene calling
What are the start codons?
What are the stop codons?
ATG
TAA TAG TGA

11. How frequently do stop codons appear?
Approximately once every 20 amino acids at random!
A stretch of 100 amino acids is likely to have a stop codon!

12. How to call ORFs (the easy way)3 2 1
DNA -1 -2 -3

13. Find all the stop codons3 2 1
DNA -1 -2 -3

14. Find all the ORFs > x amino acids
X is often 100 amino acids 3 2 1
DNA -1 -2 -3

15. Trim to those ORFs that have a start3 2 1
DNA -1 -2 -3

16. Remove “shadow” ORFs
Short ORFs that overlap others 3 2 1
DNA -1 -2 -3

17. Trim the start sites to first ATG3 2 1
DNA -1 -2 -3

18. These are the ORFs 3 2 1
DNA -1 -2 -3

19. Intrinsic ORF calling using Markov Models

20. Markov Models Based on language processing
Common for gene and protein finding, alignments, and so on

21. What is the most common word?
English: the
Spanish: el (la)
Portuguese: que

22. Scrabble

23. Scrabble In scrabble, how do they score the letters?
The most abundant letters (easiest to place on the board) are given the lowest score

24. Scrabble 1 point: E, A, I, O, N, R, T, L, S, U 2 points: D, G
3 points: B, C, M, P
4 points: F, H, V, W, Y
5 points: K
8 points: J, X
10 points: Q, Z

25. Frequency of letters

26. Making up sentences
If I want to make up a sentence, I could choose some letters at random, based on their occurrence in the alphabet (i.e their scrabble score)
rla bsht es stsfa ohhofsd

27. Lets get clever! What follows a period (“.”)? What follows a t?
Usually a space “ ”
Usually an “i” (-tion, -tize, ...)

28. Frequency of two letters
When the first letter is “t” (from 3,269 words):
ti %
te %
ta %
th %

29. Level 1 analysis
Choose a letter based on the probability that it follows the letter before: s h a n d t u c t h i n e y m e l e o l l d

30. Levels of analysis 1 letter (a, e, o …) 2 letters (th, ti, sh …)
3 letters (the, and, …)
4 letters (that, …)
Zero order model
First order model
Second order model
Third order model

31. Markov models
With about 10th order Markov models of English you get complete words and sentences!

32. Markov models
With about 10th order Markov models of English you get complete words and sentences!

33. Markov Models and ORF calling
Codons have three letters (ATG, CAC, GGG, ...)
Use a 2nd order Markov model for ORF calling
The frequency of a letter is predicted based on the frequency of the two letters before

34. Scrabble

35. Scrabble (México)
Do English and Spanish use the same letters?

36. Scrabble (México)

37. Scrabble (US) 1 point: E, A, I, O, N, R, T, L, S, U 2 points: D, G
3 points: B, C, M, P
4 points: F, H, V, W, Y
5 points: K
8 points: J, X
10 points: Q, Z
Based on the front page of the NY Times!

38. Scrabble (Spanish) 1 point: A, E, O, I, S, N, L, R, U, T
2 points: D, G
3 points: C, B, M, P
4 points: H, F, V, Y
5 points: CH, Q
8 points: J, LL, Ñ, RR, X
10 points: Z

39. What about scrabble scores for DNA?
Will vary with the composition of the organism!
Remember, some organisms have high G+C compared to A+T

40. Markov Models and ORF calling
Use a 2nd order Markov model for ORF calling
The frequency of a letter is predicted based on the frequency of the two letters before

41. Problems!
Need to train the Markov model – not all organisms are the same
Can use phylogentically close organisms
Can use “long orfs” – likely to be correct because unlikely to be random stretches without a stop codon!

42. Interpolated Markov Model (The imm in GLIMMER)
Markov Models order 1-8 (word size 2-9)
Discard (or ↓ weight) for rare words
Promote (or ↑ weight) for common words
Probability is the sum of all probabilities from 1-8
2-9

43. RNA genes As with proteins, two main methods: Ab initio Intrinsic
Homology based
extrinsic

44. Ribosomes
Ribosomes are made of proteins and RNA

45. 30S subunit from Thermus aquaticus
Blue: protein
Orange: rRNA

46. E. coli
16S rRNA
secondary structure

47. Variable region
Conserved region

48. V6 V5 V7 (43) (37) (28, 29) V4 V8 (P23-1, (45, 46) 24) V9 (49) V3 (18)
Variable regions in
the 16S rRNA.
Vn – 9 regions
(n) – variable loop(s)
forward/rev primers
V1 (6)
Van de Peer Y, Chapelle S, De Wachter R.
(1996) A quantitative map of nucleotide
substitution rates in bacterial rRNA.
Nucl. Acids Res. 24:
V2 (8-11)

49. Ribosomes Ribosomes are made of proteins and RNA Prokaryotic ribosome:
Large subunit:
50S
5S and 23S rRNA genes
Small subunit:
30S
16S rRNA gene

50. Finding 16S genes Easiest way is iterative: BLAST ALIGN TRIM
Problem: secondary structure makes identification of the ends difficult

51. Finding tRNA genes Not as easy as rRNA Much shorter Varied sequence
Only conservation is 2° structure

52. tRNAScan-SE
Sean Eddy
Use it!

53. How does this relate to tRNA?
tRNA-Phe by Yikrazuul - Own work.
Licensed under CC BY-SA 3.0 via Wikimedia Commons

54. tRNA structure Start of acceptor stem (7-9 bp)
D-loop (4-6-bp) stem plus loop
anticodon arm (6-bp) stem plus loop with anticodon
T-loop (4-5-bp) stem plus loop
End of acceptor stem (7-9 bp)
CCA to attach amino acid (may not be in sequence ... added during processing)