Algorithms for comparative sequence analysis and comparative proteomics

The recent advances in biotechnology have provided us with a wealth of data, that we need to analyse intelligently. This dissertation presents algorithms for the analysis of two types of such data: genomics and proteomics. In the genomics arena, it describes algorithms for computing statistical significance of multiple sequence alignments, identifying orthologous promoter sequences, comparing phylogenetic footprinting tools and estimating significance of whole genome alignments. For biologists working in proteomics, this dissertation demonstrates algorithms for aligning raw mass spectrometry data and improved signal-to-noise analysis in mass spectrometry by exploiting redundancy in the data. All these tools and results are open source and freely available for use.