Gene Prediction Chengwei Luo, Amanda McCook, Nadeem Bulsara, Phillip Lee, Neha Gupta, and Divya Anjan Kumar

Gene Prediction Introduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schema

Gene Prediction IntroductionIntroduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schema

Why gene prediction? experimental way?

Why gene prediction? Exponential growth of sequences Metagenomics: ~1% grow in lab New sequencing technology

How to do it?

It is a complicated task, let’s break it into parts

How to do it? It is a complicated task, let’s break it into parts Genome

How to do it? It is a complicated task, let’s break it into parts Genome

How to do it? Protein-coding gene prediction Phillip Lee & Divya Anjan Kumar Homology Search ab initio approach Nadeem Bulsara & Neha Gupta

How to do it? RNA gene prediction Amanda McCook & Chengwei Luo tRNA rRNA sRNA

Gene Prediction Introduction Protein-coding gene predictionProtein-coding gene prediction RNA gene prediction Modification and finishing Project schema

Homology Search

Strategy

open reading frame(ORF)

How/Why find ORF?

Protein Database Searches

Domain searches

Limits of Extrinsic Prediction

ab initio Prediction

Homology Search is not Enough! Biased and incomplete Database Sequenced genomes are not evenly distributed on the tree of life, and does not reflect the diversity accordingly either. Number of sequenced genomes clustered here

ab initio Gene Prediction

Features

ORFs (6 frames)

Codon Statistics

Features (Contd.)

Probabilistic View

Supervised Techniques

Unsupervised Techniques

Usually Used Tools GeneMark GLIMMER EasyGene PRODIGAL

GeneMark Developed in 1993 at Georgia Institute of Technology as the first gene finding tool. Used markov chain to represent the statistics of coding and noncoding reading frames using dicodon statistics. Shortcomings Inability to find exact gene boundaries

GeneMark.hmm

Probability of any sequence S underlying functional sequence X is calculated as P(X|S)=P(x 1,x 2,…………,x L | b 1,b 2,…………,b L ) Viterbi algorithm then calculates the functional sequence X * such that P(X * |S) is the largest among all possible values of X. Ribosome binding site model was also added to augment accuracy in the prediction of translational start sites.

GeneMark RBS feature overcomes this problem by defining a % position nucleotide matrix based on alignment of 325 E coli genes whose RBS signals have already been annotated. Uses a consensus sequence AGGAG to search upstream of any alternative start codons for genes predicted by HMM. GENEMARKS Considered the best gene prediction tool. Based on unsupervised learning. Even in prokaryotic genomes gene overlaps are quite common GeneMarkS

GLIMMER Used IMM (Interpolated Markov Models) for the first time. Predictions based on variable context (oligomers of variable lengths). More flexible than the fixed order Markov models. Principle IMM combines probability based on 0,1……..k previous bases, in this case k=8 is used. But this is for oligomers that occur frequently. However, for rarely occurring oligomers, 5th order or lower may also be used. Maintained by Steven Salzberg, Art Delcher at the University of Maryland, College Park

Glimmer development Glimmer 2 (1999) Increased the sensitivity of prediction by adding concept of ICM (Interpolated Context Model) Glimmer 3 (2007) Overcomes the shortcomings of previous models by taking in account sum of RBS score, IMM coding potentials and a score for start codons which is dependent on relative frequency of each possible start codon in the same training set used for RBS determination. Algorithm used reverse scoring of IMM by scoring all ORF (open reading frames) in reverse, from the stop codon to start codon. Score being the sum of log likelihood of the bases contained in the ORF.

Glimmer3.02

PRODIGAL Prokaryotic Dynamic Programming Gene Finding Algorithm Developed at Oak Ridge National Laboratory and the University of Tennessee

PRODIGAL-Features

EasyGene Developed at University of Copenhagen Statistical significance is the measure for gene prediction.

Comparison of Different Tools

Gene Prediction Introduction Protein-coding gene prediction RNA gene predictionRNA gene prediction Modification and finishing Project schema

RNA Gene Prediction

Why Predict RNA?

Regulatory sRNA

sRNA Challenges

Fundamental Methodology

RFAM

What Is Covariance? Fig: Christian Weile et al. BMC Genomics (2007) 8:244

Noncomparative Prediction Fig: James A. Goodrich & Jennifer F. Kugel, Nature Rev. Mol. Cell Biol. (2006) 7:612

Noncomparative Prediction *Rolf Backofen & Wolfgang R. Hess, RNA Biol. (2010) 7:1

Comparative+Noncomparative Effective sRNA prediction in V. cholerae Non-enterobacteria sRNAPredict2 32 novel sRNAs predicted 9 tested 6 confirmed Jonathan Livny et al. Nucleic Acids Res. (2005) 33:4096

Software *Rolf Backofen & Wolfgang R. Hess, RNA Biol. (2010) 7:1 Eva K. Freyhult et al. Genome Res. (2007) 17:117

Gene Prediction Introduction Protein-coding gene prediction RNA gene prediction Modification and finishingModification and finishing Project schema

Modification & Finishing Consensus strategy to integrate ab initio results Broken gene recruiting TIS correcting IS calling operon annotating Gene presence/absence analysis

Modification & Finishing Consensus strategy pass fail Broken gene recruiting ab initio results homology search candidate fragments

Modification & Finishing TIS correcting Start codon redundancy:ATG, GTG, TTG, CTG Markov iteration, experimental verified data Leaderless genes

Modification & Finishing IS callingOperon annotating IS Finder DB

Modification & Finishing Gene Presence/absence analysis

Gene Prediction Introduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schemaProject schema

Schema (proposed)

assembly group

Schema (proposed) assembly group