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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
ORF Calling
Intrinsic gene calling
Extrinsic gene calling
Compare your DNA sequences to known sequences. Needs other sequences that are known!
Only use information in your DNA sequences. Does not use other information.
ORF Calling
Start with DNA sequence
Translate in all 6 reading frames
Extrinsic gene calling
AGT AAA ACT TTA ATT GTT GGT TAAAGT AAA ACT TTA ATT GTT GGT TAA1
AG TAA AAC TTT AAT TGT TGG TTA A3A GTA AAA CTT TAA TTG TTG GTT AA2
TCA TTT TGA AAT TAA CAA CCA ATT | | | | | | | | | | | | | | | | | | | | | | | |
T CAT TTT GAA ATT AAC AAC CAA TT-3
TCA TTT TGA AAT TAA CAA CCA ATT-1TC ATT TTG AAA TTA ACA ACC AAT T-2
Why are there 6 reading frames?
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!
Extrinsic gene calling
DNAsequence
}Similarproteinsequencese.g. from BLAST
Protein encodinggene
For example
This is how (most) metagenome ORF calling is done
Eukaryotic ORF calling – especially using EST sequences
Uses of extrinsic calling
Very slow (depending on search algorithm)
Dependent on your database
Only finds known genes
Problems with extrinsic calling
Intrinsic gene calling Ab initio gene calling
What are the start codons?
What are the stop codons?
ATG
TAA TAG TGA
Alternatives to extrinsic gene calling
Approximately once every 20 amino acids at random!
A stretch of 100 amino acids is likely to have a stop codon!
How frequently do stop codons appear?
DNA
3
2
1
-1
-2
-3
How to call ORFs (the easy way)
DNA
3
2
1
-1
-2
-3
Find all the stop codons
DNA
3
2
1
-1
-2
-3
X is often 100 amino acids
Find all the ORFs > x amino acids
DNA
3
2
1
-1
-2
-3
Trim to those ORFs that have a start
DNA
3
2
1
-1
-2
-3
Short ORFs that overlap others
Remove “shadow” ORFs
DNA
3
2
1
-1
-2
-3
Trim the start sites to first ATG
DNA
3
2
1
-1
-2
-3
These are the ORFs
Intrinsic ORF calling usingMarkov Models
Based on language processing
Common for gene and protein finding, alignments, and so on
Markov Models
English: the
Spanish: el (la)
Portuguese: que
What is the most common word?
Scrabble
In scrabble, how do they score the letters?
The most abundant letters (easiest to place on the board) are given the lowest score
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
Scrabble
Frequency of letters
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
Making up sentences
What follows a period (“.”)?
What follows a t?
Usually a space “ ”
Usually an “i” (-tion, -tize, ...)
Lets get clever!
When the first letter is “t” (from 3,269 words):
ti 51%
te 20%
ta 15%
th 8%
Frequency of two letters
Choose a letter based on the probability that it follows the letter before:
s h a n d t u c ht i n e y m e l e o l l d
Level 1 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
Levels of analysis
With about 10th order Markov models of English you get complete words and sentences!
Markov models
With about 10th order Markov models of English you get complete words and sentences!
Markov models
Scoring words with Markov Models
If I choose random letters how can I tell if they are real words?
Sum the scores of 10th order Markov models across the words … if it is high it is likely to be a real word!
In reality, maybe use 1st, 2nd, 3rd, 4th, 5th, 6th … order models and compare to some known words
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
Markov Models and ORF calling
Scrabble
Do English and Spanish use the same letters?
Scrabble (México)
Scrabble (México)
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
Scrabble (US)
Based on the front page of the NY Times!
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
Scrabble (Spanish)
Will vary with the composition of the organism!
Remember, some organisms have high G+C compared to A+T
What about scrabble scores for DNA?
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
Markov Models and ORF calling
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!
Problems!
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
Interpolated Markov Model(The imm in GLIMMER)
As with proteins, two main methods:
Ab initio
• Intrinsic
Homology based
• extrinsic
RNA genes
Ribosomes are made of proteins and RNA
Ribosomes
30S subunit from Thermus aquaticus
Blue: proteinOrange: rRNA
E. coli16S rRNA secondary structure
Variable regionConserved region
Variable regions inthe 16S rRNA. Vn – 9 regions(n) – variable loop(s)forward/rev primers V1
(6)
V2 (8-11)
V3 (18)
V4 (P23-1, 24)
V5(28, 29)
V6(37)
V7 (43)
V8(45, 46)
V9 (49)
Van de Peer Y, Chapelle S, De Wachter R. (1996) A quantitative map of nucleotide substitution rates in bacterial rRNA. Nucl. Acids Res. 24:3381-3391
Ribosomes are made of proteins and RNA
Prokaryotic ribosome:
Large subunit:50S
5S and 23S rRNA genes
Small subunit:
30S
16S rRNA gene
Ribosomes
Easiest way is iterative: BLAST ALIGN TRIM
Problem: secondary structure makes identification of the ends difficult
Finding 16S genes
Not as easy as rRNA
Much shorter
Varied sequence
Only conservation is 2° structure
Finding tRNA genes
tRNAScan-SE
Sean Eddy
Use it!
How does this relate to tRNA?
tRNA-Phe by Yikrazuul - Own work.Licensed under CC BY-SA 3.0 via Wikimedia Commonshttps://commons.wikimedia.org/wiki/File:TRNA-Phe_yeast_en.svg
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)