Computational And Statistical Approaches to Genomics

At the beginning of the post-sequencing era, biology must now work with the enormous amounts of quantitative data being amassed and must render complex problems in mathematical terms, with all of the computational effort that entails.

Computational and Statistical Genomics aims to help researchers deal with current genomic challenges. Topics covered include:

This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. The methods are applied to a range of testing problems in biomedical and genomic research, including the identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments, such as microarray experiments; tests of association between gene expression measures and biological annotation metadata (e.g., Gene Ontology); sequence analysis; and the genetic mapping of complex traits using single nucleotide polymorphisms.