Cancer-specific mutation frequency

For more info about our services contact : help@bestpfe.com

Table of contents

I Conservation and coevolution 
2 Coevolution methodology 
2.1 Biological background
2.1.1 The genomics era
2.1.2 Basics of molecular biology
2.1.3 Natural selection
2.1.4 Homologous genes
2.2 The biological question
2.2.1 Predicting the eect of mutations
2.2.2 What can evolution tell us?
2.2.3 What is coevolution?
2.3 Coevolution detection methods
2.3.1 Mutual information
2.3.2 SCA and ELSC
2.3.3 DCA
2.3.4 MST
2.3.5 BIS
2.4 Conclusion
3 Predicting critical positions of protein P53 
3.1 Presentation of P53
3.2 Experimental data
3.3 Benchmarking
3.3.1 General methodology
3.3.2 ROC curve
3.3.3 PR curve
3.4 Defining critical positions
3.4.1 Global mutation frequency
3.4.2 Cancer-specific mutation frequency
3.5 Comparison of methods
3.5.1 Methods based on conservation
3.5.2 Methods based on conservation and phylogenetic trees
3.5.3 Methods based on coevolution
3.5.4 Methods based on the prediction of mutation eect
3.5.5 Conclusion on prediction methods
3.6 Sequence alignment preparation for the benchmark
3.6.1 Query coverage, alignment and tree inference
3.6.2 Sequence database
3.6.3 Sequence identity
3.6.4 Homology or orthology?
3.7 Conclusion
4 Predicting pathogenic mutations 
4.1 Methodology
4.1.1 Varibench
4.1.2 Gathering homologous sequences
4.1.3 Alignment and tree reconstruction
4.1.4 Testing by position and residue
4.2 Individual benchmark
4.2.1 Conservation
4.2.2 PolyPhen 2
4.2.3 PSIC
4.2.4 FreqDi
4.2.5 Functional Impact Score
4.3 Global benchmark
4.3.1 Why is it better?
4.3.2 Methodology
4.3.3 Z-score
4.3.4 Z-cons FreqDi
4.3.5 Results for conservation-based methods
4.3.6 Results for coevolution-based methods
4.4 Double threshold
4.4.1 Purpose
4.4.2 Results
4.5 Comparison with another benchmark
4.6 Conclusion
5 Predicting HCV protein-protein interactions 
5.1 Biological background
5.2 Perspective
5.3 Analysis with BIS
5.4 Filtering clusters
5.5 Results
5.6 Visualization
5.6.1 Interaction matrix
5.6.2 Interaction circle
5.6.3 An example on structures
5.7 Intra-protein interactions
5.8 Conclusion
6 PruneTree: a sequence filtering algorithm 
6.1 Algorithm
6.2 Benchmark with Guidance
6.3 Influence of the parameters
6.4 Correlation analysis
6.5 Species
6.6 Application to coevolution detection
6.7 Conclusion and perspectives
II Fourier transforms and genome patterns analysis 
7 Biological background 
7.1 Meiosis
7.1.1 Homologous recombination
7.1.2 Experimental data
7.2 Small RNAs in Phaeodactylum tricornotum
8 Fourier transforms for genome analysis 
8.1 Detecting periodicity on a set of genomic positions
8.1.1 Algorithm
8.1.2 Periodogram
8.1.3 Positions to distances translation
8.1.4 Application to recombination hotspots in S. cerevisiae
8.2 Statistical analysis
8.2.1 Simulation algorithm
8.2.2 Random models
8.3 Further analysis of periods
8.3.1 Histogram visualization
8.3.2 Modulo projections
8.3.3 Statistical analysis of modulo projections
8.3.4 Comparison with Solenoid Coordinate Method
8.3.5 Going back to the raw data
8.4 Local periodicity analysis in high-resolution data
8.4.1 General methodology
8.4.2 Zero-padding
8.4.3 Application to recombination proteins in S. cerevisiae
8.4.4 Scoring periodicity signals
8.4.5 Visualization
8.4.6 Results for S. cerevisiae recombination data
8.5 Application to Phaeodactylum tricornotum RNAs
8.6 Technical details
9 A model for yeast recombination proteins 
9.1 The SPoRE model and the algorithm
9.1.1 Analysis of convergent and divergent regions
9.1.2 Axis proteins model
9.1.3 DSB model
9.2 Comparison with experimental data
9.2.1 SPoRE model and axis proteins in S. cerevisiae
9.2.2 SPoRE model and DSBs in S. cerevisiae
9.2.3 Coherence of SPoRE predictions with two large-scale experimental datasets
9.2.4 SPoRE predictions on several yeast species
9.2.5 Comparison between SPoRE and other predictive tools
9.3 Conclusions
9.3.1 Orientation of genes and chromosomal axis formation
9.3.2 Modeling organisms other than yeast
9.4 Technical details
III Side works 
10 Simulating the evolution of genomes 
10.1 Phylogenetic tree inference with PhyChro
10.2 Motivation for simulations
10.3 The model
10.3.1 Making the tree
10.3.2 Genome model
10.3.3 Simulating the number of rearrangements
10.3.4 Simulating each type of event
10.4 Benchmarking PhyChro
10.4.1 Preparing synteny blocks
10.4.2 Results
10.4.3 Further analysis
10.5 Conclusion
11 R-CLAG 
11.1 What is CLAG?
11.2 Description of the CLAG algorithm
11.3 Normalization methods in R-CLAG
11.3.1 Theory
11.3.2 Application to a toy example
11.4 Clustering comparison in R-CLAG
11.5 Conclusion
Bibliography