Title: Malignant Pleural Mesothelioma (MPM) Genome

Contents

2.1 Malignant Pleural Mesothelioma (MPM) Genome

2.1.1 Introduction to single nucleotide polymorphism (SNP) arrays

• Principle of SNP Arrays, while interrogating genomic loci for the presence of common genetic variations (SNPs)

• High-density SNP arrays: Affymetrix SNP arrays and Illumina Infinium arrays

• Advantages of SNP Arrays

2.1.2 Utility of SNP arrays in detecting copy number variations (CNVs) in MPM

• Using SNP arrays to identify CNVs by analyzing signal intensity ratios between the tumor and normal samples

• Bioinformatics algorithms and tools commonly employed for CNV detection from SNP Array Data

• Studies showcasing the application of SNP arrays in identifying CNVs in the MPM genome

2.1.3 Detection of loss of heterozygosity (LOH) using SNP arrays in MPM

• Concept of loss of heterozygosity (LOH),

• How SNP arrays can be used to detect LOH by comparing the allelic intensity ratios between the tumor and normal samples

• Bioinformatics methods utilized for LOH detection, such as B-allele frequency analysis and genotype calling algorithms

• Studies demonstrating the detection of LOH events in the MPM genome using SNP arrays, highlighting relevant genomic regions and their potential involvement in MPM tumorigenesis

2.1.4 Integration of CNV and LOH data for comprehensive genomic analysis in MPM

• Significance of integrating CNV and LOH data obtained from SNP arrays to gain a comprehensive understanding of the genomic alterations in MPM

• Potential implications of CNVs and LOH events in key biological pathways and signaling networks relevant to MPM development and progression

• Challenges and limitations associated with SNP array analysis

2.2 Structural variations in cancer genomes and their relevance to malignant plural mesothelioma

2.2.1 Overview of structural variations (SVs) in the context of cancer

• Importance of understanding structural variations in deciphering cancer biology

2.2.2 Types of structural variations: Deletions, insertions, inversions, duplications, and translocations

i) Deletions

ii) Insertions

iii) Inversions

iv) Duplications

iv) Translocations

• Differences in the genomic impact and functional consequences of various types of structural variations

2.2.3 Mechanisms underlying the formation of structural variations

• Mechanisms responsible for the formation of structural variations

• DNA repair Mechanisms and Genomic Instability

• How genomic architecture and repetitive elements impact SVs formation

2.2.4 Functional consequences of structural variations in cancer genomes

• Alterations in gene dosage, gene fusion, disruption of regulatory elements

• Impact of structural variations on cell cycle control, DNA repair, and signaling pathways

2.2.5 Structural variations associated with malignant plural mesothelioma

• Literature Review

• Identification of recurrent structural variations specific to malignant plural mesothelioma

• Genes and genomic regions affected by structural variations and their potential role in malignant plural mesothelioma development and progression

2.3. Methods for detecting structural variations using short reads (Illumina) and long reads (nanopore technology)

2.3.1 Short-read sequencing using Illumina.

• Popularity and widespread use of Illumina sequencing technology in genomic research and SV detection.

• Principle of short-read sequencing, where DNA fragments are sequenced in parallel using reversible terminators.

• Typical read lengths and sequencing depths achieved with Illumina platforms

• Methods and algorithms commonly used to detect SVs from Illumina short-read data (Read-pair analysis, split-read analysis, and depth-of-coverage analysis (whole genome sequencing by next generation sequencing, Mapping (dragen, BWA), caling SVs by (Manta).

• Advantages of Illumina sequencing

• Limitations of Illumina sequencing

2.3.2 Long-read Sequencing using Nanopore Technology

• Principle of Nanopore Sequencing

• Advantages of Nanopore Sequencing for SV Detection

• Describe the challenges associated with nanopore sequencing, including higher error rates compared to Illumina sequencing and the need for advanced bioinformatics tools for data analysis

• Highlight recent advancements in nanopore sequencing technology, such as improved accuracy and increased throughput, which enhance its utility for SV detection

• Methods and algorithms commonly used to detect SVs from oxford nanopore technology

2.3.3 Comparing short-read and long-read sequencing for SV detection

• Discuss the complementary nature of the two technologies and the potential for integrating data from both platforms to improve SV detection accuracy and resolution

• Power of Nanopore Sequencing in Capturing Complex SVs

• Cost considerations associated with both technologies and their suitability for different research applications

Total number of words (exclusive of references): 15, 768 (52.5 pages)

Referencing style: Harvard

Number of sources: 77