Bioinformatics with Python Cookbook Second Edition

Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology

Tiago Antao 지음

Packt(GCO Science) 2018년 11월 30일 출간

작품소개

책소개

▶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.

목차

▶TABLE of CONTENTS
Chapter 1: Python and the Surrounding Software Ecology
Chapter 2: Next-Generation Sequencing
Chapter 3: Working with Genomes
Chapter 4: Population Genetics
Chapter 5: Population Genetics Simulation
Chapter 6: Phylogenetics
Chapter 7: Using the Protein Data Bank
Chapter 8: Bioinformatics Pipelines
Chapter 9: Python for Big Genomics Datasets
Chapter 10: Other Topics in Bioinformatics
Chapter 11: Advanced NGS Processing

책 속으로

▶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.

출판사 서평

▶ Preface
Whether you are reading this book as a computational biologist or a Python programmer, you will probably relate to the phrase "explosive growth, exciting times." The recent growth in the use of Python is strongly connected with its status as big data's main programming language. The deluge of data in biology, mostly from genomics and proteomics, makes bioinformatics one of the forefront applications of data science. There is a massive need for bioinformaticians to analyze all this data; of course, one of the main tools is Python. We will not only talk about the programming language but also the whole community and software ecology behind it.

When you choose Python to analyze your data, you expect to get an extensive set of libraries, ranging from statistical analysis to plotting, parallel programming, machine learning, and bioinformatics. However, you actually get even more than this; the community has a tradition of providing good documentation, reliable libraries, and frameworks. It is also friendly and supportive of all its participants.

In this book, we will present practical solutions to modern bioinformatics problems using Python. Our approach will be hands-on; we will address important topics, such as nextgeneration sequencing, genomics, population genetics, phylogenetics, and proteomics.

At this stage, you probably know the language reasonably well and are aware of the basic analysis methods in your field of research. You will dive directly into relevant complex computational biology problems and learn how to tackle them with Python. This is not your first Python book or your first biology lesson; this is where you will find reliable and pragmatic solutions to realistic and complex problems.

The first edition of this book took several high-risk decisions a few years ago, considering Docker, Jupyter Notebook, and even Python 3 were not obvious choices. These choices worked perfectly well. The second edition once again uses these technologies, which are now standard in the field. Probably due to bioinformatics being a more mature field, there are no high-risk options now. There is new content on pipelines, parallel processing systems, and file formats, but none of them are unsafe bets.

작가정보

저자(글) Tiago Antao

Tiago Antao is a bioinformatician currently working in the field of genomics. He was originally a computer scientist but he crossed over to computational biology with an MSc in Bioinformatics from the Faculty of Sciences at the University of Porto, Portugal, and a PhD on the spread of drug-resistant malaria from the Liverpool School of Tropical Medicine in the UK. He is one of the co-authors of Biopython, a major bioinformatics package written in Python.In his post-doctoral career, he has worked with human datasets at the University of Cambridge (UK) and with mosquito whole genome sequence data at the University of Oxford (UK). He is currently working as a research scientist at the University of Montana.