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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

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Discover modern, next-generation sequencing libraries from Python ecosystem to analyze large amounts of biological data

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

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

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.

Table of Contents

  1. Python and the Surrounding Software Ecology
  2. Next-generation Sequencing
  3. Working with Genomes
  4. Population Genetics
  5. Population Genetics Simulation
  6. Phylogenetics
  7. Using the Protein Data Bank
  8. Bioinformatics pipelines
  9. Python for Big Genomics Datasets
  10. Other Topics in Bioinformatics
  11. Machine learning in Bioinformatics
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Machine Learning with R - Second Edition
Machine Learning with R - Second Edition

Key Features

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  • Explore, forecast, and classify data with R
  • Use R to apply common machine learning algorithms to real-world scenarios

Book Description

Machine learning, at its core, is concerned...

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

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  • Discover new developments in EM algorithm, PCA, and bayesian...
Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more
Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

Take your NLP knowledge to the next level and become an AI language understanding expert by mastering the quantum leap of Transformer neural network models

Key Features

  • Build and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using...

Learning R for Geospatial Analysis
Learning R for Geospatial Analysis

Leverage the power of R to elegantly manage crucial geospatial analysis tasks

About This Book

  • Write powerful R scripts to manipulate your spatial data
  • Gain insight from spatial patterns utilizing R's advanced computation and visualization capabilities
  • Work within a...
Mastering Vim: Build a software development environment with Vim and Neovim
Mastering Vim: Build a software development environment with Vim and Neovim

Mastering Vim, reviewed by Bram Moolenaar, the creator of Vim, covers usage of Vim and Neovim, showcases relevant plugins, and teaches Vimscript

Key Features

  • Expert Vim and Vimscript techniques to work with Python and other development environment
  • Accomplish end-to-end...
Engineering Science
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Engineering Science is a comprehensive textbook suitable for all vocational and pre-degree courses in engineering, being fully in line with the latest vocational courses at Level 2 and leading into Level 3. Taking a subject-led approach, engineering students will find the essential scientific principles necessary for their...

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