Cancer development

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Table of contents

Introduction
1 State of the art 
1.1 Cancer development
1.1.1 Proto-oncogenes
1.1.2 Tumor suppressor genes
1.1.3 DNA repair genes
1.2 Examples of structural variants that can result in cancer development
1.3 Sequencing technologies
1.3.1 Paired-end data
1.3.2 Mate-pair data
1.4 Approaches to structural variants detection
1.4.1 Pem based algorithms
1.4.2 Doc based algorithms
1.4.3 Split-read based approach
1.4.4 Combination of di↵erent approaches
2 Data management and alignment issues 
2.1 Mappability of fragments
2.2 Alignment issues
2.2.1 Sequencing data
2.2.2 Reads alignment
2.2.3 Probabilistic approach to mapping position selection
2.2.4 Paired reads alignment
2.3 Alignment algorithms
2.3.1 Aligner choice rationale
2.3.2 BWA drawbacks for structure variants detection
3 Structural variant detection based on paired-end mapping signatures 
3.1 Annotation of normal and abnormal read pairs
3.1.1 Detection of normal fragments orientation
3.1.2 Definition of normal insert size
3.1.3 Formal definition of normal and abnormal fragments
3.2 Clustering of abnormal fragments
3.2.1 Cluster definition
3.2.2 Primary clustering algorithm
3.2.3 Splitting algorithm
4 Coverage issues 
4.1 GC-content
4.2 Ploidy and copy number changes
4.3 Mappability
4.3.1 Repeats in human genome
4.3.2 Reads mapping and DOC
4.3.3 Mappability of a genome position
5 Abnormal and flanking regions
5.1 Definition of flanking regions and abnormal regions
5.1.1 Flanking regions
5.1.2 Abnormal region
5.2 Expected number of fragments in a genomic region
5.2.1 Expected number of fragments for a flanking region
5.2.2 Estimation of the number of fragments starting on a position considering GC-content
5.2.3 Expected number of fragments for the abnormal region
6 Bayesian models
6.1 Model definition
6.2 Bayesian approach
6.2.1 Conditional probability of a model
6.2.2 A priory probability of a model
6.2.3 Factorization of the conditional probability
6.2.4 Set of models to test
6.2.5 Model choice
6.3 Evaluation of the breakpoint position
7 SV types and assembly workflow 
7.1 Structural Variant types
7.1.1 Simple structural variations
7.1.2 Complex structural variants
7.2 SV assembly process
8 Overview of competitive methods for SV detection 
8.1 Methods description
8.1.1 GasvPro
8.1.2 BreakDancer
8.1.3 Lumpy
8.1.4 Delly
8.2 Configuration used for considered software
8.3 Main features and scopes
9 Results on simulated and real data 
9.1 Simulated data
9.1.1 Simulation of normal genome
9.1.2 Simulation of cancer genome
9.1.3 Simulation of sequencing data
9.1.4 Mate pair and pair-ended datasets statistics
9.2 Comparative performances on simulated data
9.2.1 Precision and recall
9.2.2 Detailed results
9.3 Comparative performances on a neuroblastoma mate-pair dataset
9.3.1 Experimentally validated structural variants
9.3.2 Predicted structural variations
9.3.3 SNP6-experiments
9.4 Discussion of the results
9.4.1 Sequencing technology and coverage
9.4.2 Influence of the presence of copy number variation on the SV prediction accuracy
9.4.3 Breakpoint resolution
9.5 Execution time comparison
10 Conclusion and perspectives
10.1 Conclusion
10.2 Perspectives