Gene Prediction

Gene PredictionChengwei 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?

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

Homology Search

Strategy

open reading frame(ORF)

How/Why find ORF?

How/Why find 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

GeneMark.hmm

• Probability of any sequence S underlying functional sequence X is calculated as P(X|S)=P(x1,x2,…………,xL| b1,b2,…………,bL)

• 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.

PrincipleIMM 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

PRODIGALProkaryotic Dynamic Programming Gene Finding Algorithm

Developed at Oak Ridge National Laboratory and the University of Tennessee

PRODIGAL-Features

PRODIGAL-Features

EasyGene

Developed at University of Copenhagen

Statistical significance is the measure for gene prediction.

¥ High quality data set based onsimilarity in SwissPRot isextracted from genome.

¥ Data set used to estimate theHMM where based on ORF scoreand length statistical significance iscalculated.

Problem:

¥ No standalone version available

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 confirmedJonathan 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 & FinishingConsensus strategy

pass

pass

fail

Broken gene recruiting

ab initio results

homology search

candidate fragments

Modification & FinishingTIS correcting

Start codon redundancy:ATG, GTG, TTG, CTG

Markov iteration, experimental verified data

Leaderless genes

Modification & FinishingIS calling Operon annotating

IS Finder DB

Modification & FinishingGene Presence/absence analysis

Gene Prediction

• Introduction

• Protein-coding gene prediction

• RNA gene prediction

• Modification and finishing

• Project schemaProject schema

Schema (proposed)

Schema (proposed)

assembly group

Schema (proposed)

assembly group