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Bioinformatics with Python Cookbook: Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology, 2nd Edition
Discover modern, next-generation sequencing libraries from Python ecosystem to analyze large amounts of biological data
Key FeaturesPerform complex bioinformatics analysis using the most important Python libraries and applicationsImplement next-generation sequencing, metagenomics, automating analysis, population genetics, and moreExplore various statistical and machine learning techniques for bioinformatics data analysisBook DescriptionBioinformatics 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 learnLearn how to process large next-generation sequencing (NGS) datasetsWork with genomic dataset using the FASTQ, BAM, and VCF formatsLearn to perform sequence comparison and phylogenetic reconstructionPerform complex analysis with protemics dataUse Python to interact with Galaxy serversUse High-performance computing techniques with Dask and SparkVisualize protein dataset interactions using CytoscapeUse PCA and Decision Trees, two machine learning techniques, with biological datasetsWho this book is forThis 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.
Table of ContentsPython and the Surrounding Software EcologyNext-generation SequencingWorking with GenomesPopulation GeneticsPopulation Genetics SimulationPhylogeneticsUsing the Protein Data BankBioinformatics pipelinesPython for Big Genomics DatasetsOther Topics in BioinformaticsMachine learning in Bioinformatics
Key FeaturesPerform complex bioinformatics analysis using the most important Python libraries and applicationsImplement next-generation sequencing, metagenomics, automating analysis, population genetics, and moreExplore various statistical and machine learning techniques for bioinformatics data analysisBook DescriptionBioinformatics 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 learnLearn how to process large next-generation sequencing (NGS) datasetsWork with genomic dataset using the FASTQ, BAM, and VCF formatsLearn to perform sequence comparison and phylogenetic reconstructionPerform complex analysis with protemics dataUse Python to interact with Galaxy serversUse High-performance computing techniques with Dask and SparkVisualize protein dataset interactions using CytoscapeUse PCA and Decision Trees, two machine learning techniques, with biological datasetsWho this book is forThis 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.
Table of ContentsPython and the Surrounding Software EcologyNext-generation SequencingWorking with GenomesPopulation GeneticsPopulation Genetics SimulationPhylogeneticsUsing the Protein Data BankBioinformatics pipelinesPython for Big Genomics DatasetsOther Topics in BioinformaticsMachine learning in Bioinformatics
- GenresProgramming
360 pages, Kindle Edition
Published November 30, 2018
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25 people want to read
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