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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 /

If you have intermediate-level knowledge of Python and are well aware of the main research and vocabulary in your bioinformatics topic of interest, this book will help you develop your knowledge further.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Antao, Tiago (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, England ; Mumbai [India] : Packt Publishing, 2015.
Colección:Community experience distilled.
Temas:
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Python and the Surrounding Software Ecology; Introduction; Installing the required software with Anaconda; Installing the required software with Docker; Interfacing with R via rpy2; Performing R magic with IPython; Chapter 2: Next-generation Sequencing; Introduction; Accessing GenBank and moving around NCBI databases; Performing basic sequence analysis; Working with modern sequence formats; Working with alignment data; Analyzing data in variant call format
  • Studying genome accessibility and filtering SNP dataChapter 3: Working with Genomes; Introduction; Working with high-quality reference genomes; Dealing with low-quality genome references; Traversing genome annotations; Extracting genes from a reference using annotations; Finding orthologues with the Ensembl REST API; Retrieving gene ontology information from Ensembl; Chapter 4: Population Genetics; Introduction; Managing datasets with PLINK; Introducing the Genepop format; Exploring a dataset with Bio. PopGen; Computing F-statistics; Performing Principal Components Analysis
  • Investigating population structure with AdmixtureChapter 5: Population Genetics Simulation; Introduction; Introducing forward-time simulations; Simulating selection; Simulating population structure using island and stepping-stone models; Modeling complex demographic scenarios; Simulating the coalescent with Biopython and fastsimcoal; Chapter 6: Phylogenetics; Introduction; Preparing the Ebola dataset; Aligning genetic and genomic data; Comparing sequences; Reconstructing phylogenetic trees; Playing recursively with trees; Visualizing phylogenetic data; Chapter 7: Using the Protein Data Bank
  • IntroductionFinding a protein in multiple databases; Introducing Bio. PDB; Extracting more information from a PDB file; Computing molecular distances on a PDB file; Performing geometric operations; Implementing a basic PDB parser; Animating with PyMol; Parsing mmCIF files using Biopython; Chapter 8: Other Topics in Bioinformatics; Introduction; Accessing the Global Biodiversity Information Facility; Geo-referencing GBIF datasets; Accessing molecular-interaction databases with PSIQUIC; Plotting protein interactions with Cytoscape the hard way; Chapter 9: Python for Big Genomics Datasets
  • IntroductionSetting the stage for high-performance computing; Designing a poor human concurrent executor; Performing parallel computing with IPython; Computing the median in a large dataset; Optimizing code with Cython and Numba; Programming with laziness; Thinking with generators; Index