Chapter 14: Cancer Genome Analysis

Abstract

Although there is great promise in the benefits to be obtained by analyzing cancer genomes, numerous challenges hinder different stages of the process, from the problem of sample preparation and the validation of the experimental techniques, to the interpretation of the results. This chapter specifically focuses on the technical issues associated with the bioinformatics analysis of cancer genome data. The main issues addressed are the use of database and software resources, the use of analysis workflows and the presentation of clinically relevant action items. We attempt to aid new developers in the field by describing the different stages of analysis and discussing current approaches, as well as by providing practical advice on how to access and use resources, and how to implement recommendations. Real cases from cancer genome projects are used as examples.

The Application of Omics Technologies to the Study of Mammalian Toxicology

“…The front-end components of most analysis pipelines are as follows: the reads are first filtered for quality, trimmed, and mapped. Different pipeline analysis modules are then used to identify single nucleotide changes, insertion deletion mutations, and large-scale rearrangements [18]. Furthermore, the quality and depth of reads across the genome is such that it is not uncommon to identify a large number of false-positive alignments.…”

Systems Analysis of High-Throughput Data

“…For more details on sequencing, alignment, and variant calling in NGS studies, the reader is referred to two recent reviews [8,9]. Once these steps are completed, the data may be analyzed to reveal disease–associated genetic variants, epigenetic modifications, and differential expression [10]. …”

CDPath: Cooperative Driver Pathways Discovery Using Integer Linear Programming and Markov Clustering

“…For example, Ding et al [4] used synonymous somatic mutations identified in 250 genes to estimate the background mutation rate, and then identified 26 genes significantly mutating in 188 human lung adenocarcinomas. However, this kind of methods unreasonably consider the rate as a constant value for all samples in the entire genome, and therefore ignore the heterogeneity of genome aberrations [5].…”