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 TAA 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 TC 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

DNA sequence } Similar protein sequences e.g. from BLAST Protein encoding gene 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 How to call ORFs (the easy way)

DNA Find all the stop codons

DNA X is often 100 amino acids Find all the ORFs > x amino acids

DNA Trim to those ORFs that have a start

DNA Short ORFs that overlap others Remove “shadow” ORFs

DNA Trim the start sites to first ATG

DNA These are the ORFs

Intrinsic ORF calling using Markov 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: shandtuchtineymeleolld 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 10 th order Markov models of English you get complete words and sentences! Markov models

With about 10 th 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 10 th order Markov models across the words … if it is high it is likely to be a real word! In reality, maybe use 1 st, 2 nd, 3 rd, 4 th, 5 th, 6 th … order models and compare to some known words

Codons have three letters (ATG, CAC, GGG,...) Use a 2 nd 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)

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 2 nd 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 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: protein Orange: rRNA

E. coli 16S rRNA secondary structure

Variable region Conserved region

Variable regions in the 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:

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 Commons

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)