improved techniques for the identification of pseudogenes
DESCRIPTION
IMPROVED TECHNIQUES FOR THE IDENTIFICATION OF PSEUDOGENES. L. Coin and R. Durbin Wellcome Trust Sanger Institute. BIOINFORMATICS 2004 Presented by: Oscar Sanchez Plazas. Outline. Problem definition Previous works on pseudogene identification Proposed method Protein domain profile (Pfam) - PowerPoint PPT PresentationTRANSCRIPT
IMPROVED TECHNIQUES FOR THE IDENTIFICATION OF
PSEUDOGENES
L. Coin and R. DurbinWellcome Trust Sanger Institute
BIOINFORMATICS2004
Presented by:Oscar Sanchez Plazas
Outline
Problem definitionPrevious works on pseudogene
identificationProposed method
Protein domain profile (Pfam)AlgorithmResults and Discussion
Pseudogene Identification
Pseudogene: Remnants of genomic sequences of genes that are no longer translated into functional proteins. Non-processed (duplicated):
Product of genome duplication (paralogous)loss of function at the transcription or translation level
Processed (~70%):Product of retro-transpositionNo introns, no promoter
(*) Plagiarized Errors and Molecular Genetics. Edward E. Max, M.D.
(*) http://www.pseudogene.org/definition.html
PseudogenesSignificance:
Comparative Genomic Evolution of DNA, new gene expression, patterns Study of mechanisms for regulation of gene
expression Verification of gene sequences in databases
Pseudogenes
Are they functional? (why high conservation compared to prokaryotes?) “Pseudogenes exhibit evolutionary conservation of gene sequence, reduced nucleotide variability,
excess synonymous over nonsynonymous nucleotide polymorphism, and other features that are expected in genes or DNA sequences that have functional roles”1
(1) PSEUDOGENES: Are They “Junk” or Functional DNA? Evgeniy S. Balakirev, Francisco Ayala. 2003- An expressed pseudogene regulates the messenger stability of its homologous coding gene. Nature, Hirotsune,S. et
al. 2003 - The putatively functional Mkrn1-p1 pseudogene is neither expressed nor imprinted, nor does it regulate its source gene
in trans. Gray TA, Wilson A, Fortin PJ, Nicholls RD. PNAS. 2006
(*) www.answersingenesis.org/tj/v17/i2/pseudogene.asp
Problem
Sometimes pseudogenes are mis-annotated in gene sequence databases as functional genes.
Key Insight:Employ a evolutionary constraint model
derived from a functional characterization over the gene product.
Constrained vs. neutral model
Previous approaches
(*) Large-scale analysis of pseudogenes in the human genomeZhao Lei Zhang, Mark Gerstein
Presence of stop codon and frameshift. Not very sensitive (~50% are detectable )
Previous approaches
Ratio of synonymous and non-synonymous substitutions (dN/dS) Not very accurate: e.g.
gene under positive selection pressure.
(*) Genome-wide survey of human pseudogenes. Torrents,D., Suyama,M., Zdobnov,E. and Bork,P.
Model Proposed
PSILC: Pseudogene inference from loss of constraint (log-odd score) Protein Domain evolution (functional constrain)
- Null probability model (Pfam) Neutral nucleotide model Protein coding model
Domain Profile - HMM
Protein Domains: structural, functional and evolutionary units of proteins
HMM profiles: the most sensitive models for domains Every state has a particular emission distribution over {A,C,T,G}
(*) genome.nasa.gov/MediaLib/hmm_project_fig2.jpg
match
deletion
insertion
(*) http://pfam.sanger.ac.uk//family/TAF
Model Proposed
Objective Look at pattern of substitution in conserved protein
domains
Algorithm Input
Alignment A Unrooted tree T Profile HMM D (aligned with A)
Output Score for a leaf of the tree which represents the belief that the
node corresponds to a pseudogene.
Algorithm
Notation Xn. : row corresponding to leaf-node n. X.i : i-th column. A\Xn. : Alignment A excluding Xn. mj : j-th match column of profile HMM. pn : parent node of n. bn : branch from pn to n. T\bn : Tree T excluding bn.
Algorithm Input: Unrooted tree T, Alignment A, profile HMM D Output: Log-odds scores:
A neutral nucleotide model compared to a Pfam domain encoding model (PSILC-nuc/dom)
A protein coding model compared to a Pfam domain encoding model (PSILC-prot/dom).
Evolutionary model
Algorithm
• Independence assumptions
• xni respect to other columns in the row given A\xn
• xni respect to other columns in A\xn given x.i\xni
• Tree assumption: xni respect to x.i\xn given xpni
AlgorithmSteps:
Calculate the distribution at xpni given the evolutionary constraints on the other branches.
For each residue/base at xpni, calculate the transition probability to xni given the evolutionary constraints.
•pn is set as the root of the T•Prior distribution: Stationary dist. of Q
Evolutionary Model
Instantaneous rate matrix (Q)*: DNA models: HKY model (^ - uniform) Amino acid model: database estimates (WAG, ^)
- steady state distribution (vs. equilibrium): Alternative models: observed in A Null model: distribution of the state in the HMM
Parameters (ML): f: trade off mutation pressure
(from-to) r: evolutionary rate : ratio transition/transversion
(*) A Novel Use of Equilibrium Frequencies in Models of Sequence EvolutionNick Goldman and Simon Whelan
AlgorithmDirectionality of the calculation
Score on an alignment of two transcripts x1, x2 is not symmetric (detailed balance).
If base x1i is more likely than x2i at a particular match state but equally likely under the protein model, score for x2. being a pseudogene is higher than score for x1.
dN/dS does not have this property (a third sequence should be used). Requires a PFam model (independent)
Results
Data: Cromosome 6 human genome Manually annotated (pseudo)genes Blast search-ENSEMBL e<10^-7 (>80%) (<99%) Multiple alignment: ClustalW Max. likelihood distance. Nearest neighbor tree. 598 (875) coding transcripts, 97 (158)
pseudogenes
Results
ROC
Why PSILC-prot/dom is better than PSILC-nuc/dom?
Results
•Better discrimination
Question
What is the main difference between the HMM’s previously studied (eg. Pairwise alignment) and the HMM profiles? Why the latter HMM’s are important for the identification of pseudogenes?