I present the use of statistical methods to analyze high throughput sequencing (HTS) data, which is essential for understanding various biological processes at a molecular level. The introduction that highlights the importance of HTS data in modern biology, followed by a section on sequencing platforms and library preparation, and a section on the analysis of sequence reads. I also discuss methodological problems that arise from sequence data, such as sequence comparison and the generation of summary statistics. Additionally, I present probabilistic models for sequencing assays, including state-space models and hidden Markov models, and their applications in sequence alignment and abundance estimation. I finish with a chapter about the implications of omics data for discovery in biology, particularly focusing on the debate between reductionistic and holistic approaches in scientific research. It explores the context of high-throughput sequencing (HTS) technologies and their impact on biological research, questioning whether the vast amounts of data generated can lead to scientific discoveries without specific hypotheses.
