▶Book Description
Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data.
This book covers next-generation sequencing, genomics, metagenomics, population genetics, phylogenetics, and proteomics. You'll learn modern programming techniques to analyze large amounts of biological data. With the help of real-world examples, you'll convert, analyze, and visualize datasets using various Python tools and libraries.
This book will help you get a better understanding of working with a Galaxy server, which is the most widely used bioinformatics web-based pipeline system. This updated edition also includes advanced next-generation sequencing filtering techniques. You'll also explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks such as Dask and Spark.
By the end of this book, you'll be able to use and implement modern programming techniques and frameworks to deal with the ever-increasing deluge of bioinformatics data.
▶What You Will Learn
⦁ Learn how to process large next-generation sequencing (NGS) datasets
⦁ Work with genomic dataset using the FASTQ, BAM, and VCF formats
⦁ Learn to perform sequence comparison and phylogenetic reconstruction
⦁ Perform complex analysis with protemics data
⦁ Use Python to interact with Galaxy servers
⦁ Use High-performance computing techniques with Dask and Spark
⦁ Visualize protein dataset interactions using Cytoscape
⦁ Use PCA and Decision Trees, two machine learning techniques, with biological datasets
▶Key Features
⦁ Perform complex bioinformatics analysis using the most important Python libraries and applications
⦁ Implement next-generation sequencing, metagenomics, automating analysis, population genetics, and more
⦁ Explore various statistical and machine learning techniques for bioinformatics data analysis
▶Who This Book Is For
This book is for Data data Scientistsscientists, Bioinformatics bioinformatics analysts, researchers, and Python developers who want to address intermediate-to-advanced biological and bioinformatics problems using a recipe-based approach. Working knowledge of the Python programming language is expected.
▶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 nextgeneration 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 nonmodel species. It introduces GFF processing, teaches you 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 could perform Principal Components Analysis, computer FST, or structure/admixture plots.
⦁ Chapter 5, Population Genetics Simulation, covers simuPOP, an extremely powerful Pythonbased forward-time population genetics simulator. This chapter shows you how to simulate different selection and demographic regimes. It also briefly discusses 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, 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 is Airflow, a type of pipeline that targets programmers.
⦁ Chapter 9, Python for Big Genomics Datasets, discusses high-performance programming techniques necessary to handle big datasets. It briefly discusses parallel processing with Dask and Spark. Code optimization frameworks (such as Numba or Cython) are introduced. Finally, efficient file formats such as HDF5 or Parquet are presented.
⦁ Chapter 10, 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 11, Advanced NGS Processing, covers advanced programming techniques to filter NGS data. These include the use of Mendelian datasets that are then analyzed by standard statistics and machine learning techniques.
