What this book covers
Chapter 1, Python and the Surrounding Software Ecology, tells you how to set up a modern bioinformatics environment with Python. This chapter discusses how to deploy software using Docker, interface with R, and interact with the IPython Notebook.
Chapter 2, Next-generation Sequencing, provides concrete solutions to deal with next-generation sequencing data. This chapter teaches you how to deal with large FASTQ, BAM, and VCF files. It also discusses data filtering.
Chapter 3, Working with Genomes, not only deals with high-quality references—such as the human genome—but also discusses how to analyze other low-quality references typical in non-model species. It introduces GFF processing, teaches you how to analyze genomic feature information, and discusses how to use gene ontologies.
Chapter 4, Population Genetics, describes how to perform population genetics analysis of empirical datasets. For example, on Python, we will perform Principal Components Analysis, compute FST, or Structure/Admixture plots.
Chapter 5, Population Genetics Simulation, covers simuPOP, an extremely powerful Python-based forward-time population genetics simulator. This chapter shows you how to simulate different selection and demographic regimes. It also briefly discusses the coalescent simulation.
Chapter 6, Phylogenetics, uses complete sequences of recently sequenced Ebola viruses to perform real phylogenetic analysis, which includes tree reconstruction and sequence comparisons. This chapter discusses recursive algorithms to process tree-like structures.
Chapter 7, Using the Protein Data Bank, focuses on processing PDB files, for example, performing the geometric analysis of proteins. This chapter takes a look at protein visualization.
Chapter 8, Other Topics in Bioinformatics, talks about how to analyze data made available by the Global Biodiversity Information Facility (GBIF) and how to use Cytoscape, a powerful platform to visualize complex networks. This chapter also looks at how to work with geo-referenced data and map-based services.
Chapter 9, Python for Big Genomics Datasets, discusses high-performance programming techniques necessary to handle big datasets. It briefly discusses cluster usage and code optimization platforms (such as Numba or Cython).
