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 Jupyter Notebooks.
Chapter 2, Getting to Know NumPy, pandas, Arrow, and Matplotlib, introduces the fundamental Python libraries for data science: NumPy for array and matrix processing; Pandas for table-based data manipulation; Arrow to optimize Pandas processing and Matplotlib for charting.
Chapter 3, 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 4, Advanced NGS Processing, covers advanced programming techniques to filter NGS data. This includes the use of mendelian datasets that are then analyzed by standard statistics. We also introduce metagenomic analysis
Chapter 5, 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 nonmodel species. It introduces GFF processing, teaches you to analyze genomic feature information, and discusses how to use gene ontologies.
Chapter 6, Population Genetics, describes how to perform population genetics analysis of empirical datasets. For example, in Python, we could perform Principal Components Analysis, computer FST, or structure/admixture plots.
Chapter 7, 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 8, 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 9, Bioinformatics Pipelines, introduces two types of pipelines. The first type of pipeline is Python-based Galaxy, a widely used system with a web interface targeting mostly non-programming users although bioinformaticians might still have to interact with it programmatically. The second type will be based on snakemake and nextflow, a type of pipeline that targets programmers.
Chapter 10, Machine Learning for Bioinformatics, introduces machine learning using an intuitive approach to deal with computational biology problems. The chapter covers Principal Components Analysis, Clustering, Decision Trees, and Random Forests.
Chapter 11, Parallel Processing with Dask and Zarr, introduces techniques to deal with very large datasets and computationally intensive algorithms. The chapter will explain how to use parallel computation across many computers (cluster or cloud). We will also discuss the efficient storage of biological data.
Chapter 12, Functional Programming for Bioinformatics, introduces functional programming which permits the development of more sophisticated Python programs that, through lazy programming and immutability are easier to deploy in parallel environments with complex algorithms
