| Description |
1 online resource (x, 286 pages) |
| Series |
Community Experience Distilled |
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Community experience distilled.
|
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
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 |
| Notes |
Includes index |
|
"Quick answers to common problems"--Cover |
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Online resource; title from PDF title page (ebrary, viewed July 11, 2015) |
| Subject |
Python (Computer program language)
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Bioinformatics.
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Computational biology.
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COMPUTERS -- Programming Languages -- General.
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Computational biology
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Bioinformatics
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Python (Computer program language)
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| Form |
Electronic book
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| ISBN |
9781783558650 |
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1783558652 |
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