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Managing Your Biological Data with Python
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Table of Contents

Getting Started The Python Shell In This Chapter You Will Learn Story: Calculating the G of ATP Hydrolysis What Do the Commands Mean? Examples Testing Yourself Your First Python Program In This Chapter You Will Learn Story: How to Calculate the Frequency of Amino Acids from Insulin What Do the Commands Mean? Examples Testing Yourself Data Management Analyzing a Data Column In This Chapter You Will Learn Story: Dendritic Lengths What Do the Commands Mean? Examples Testing Yourself Parsing Data Records In This Chapter You Will Learn Story: Integrating Mass Spectrometry Data into Metabolic Pathways What Do the Commands Mean? Examples Testing Yourself Searching Data In This Chapter You Will Learn Story: Translating an RNA Sequence into the Corresponding Protein Sequence What Do the Commands Mean? Examples Testing Yourself Filtering Data In This Chapter You Will Learn Story: Working with RNA-Seq Output Data What Do the Commands Mean? Examples Testing Yourself Managing Tabular Data In This Chapter You Will Learn Story: Determining Protein Concentrations What Do the Commands Mean? Examples Testing Yourself Sorting Data In This Chapter You Will Learn Story: Sort a Data Table What Do the Commands Mean? Examples Testing Yourself Pattern Matching and Text Mining In This Chapter You Will Learn Story: Search a Phosphorylation Motif in a Protein Sequence What Do the Commands Mean? Examples Testing Yourself Modular Programming Divide a Program into Functions In This Chapter You Will Learn Story: Working with Three-Dimensional Coordinate Files What Do the Commands Mean? Examples Testing Yourself Managing Complexity with Classes In This Chapter You Will Learn Story: Mendelian Inheritance What Do the Commands Mean? Examples Testing Yourself Debugging In This Chapter You Will Learn Story: When Your Program Does Not Work What Do the Commands Mean? Examples Testing Yourself Using External Modules: The Python Interface to R In This Chapter You Will Learn Story: Reading Numbers from a File and Calculating Their Mean Value Using R with Python What Do the Commands Mean? Examples Testing Yourself Building Program Pipelines In This Chapter You Will Learn Story: Building an NGS Pipeline What Do the Commands Mean? Examples Testing Yourself Writing Good Programs In This Chapter You Will Learn Problem Description: Uncertainty What Do the Commands Mean? Examples Testing Yourself Data Visualization Creating Scientific Diagrams In This Chapter You Will Learn Story: Nucleotide Frequencies in the Ribosome What Do the Commands Mean? Examples Testing Yourself Creating Molecule Images with PyMOL In This Chapter You Will Learn Story: The Zinc Finger Seven Steps to Create a High-Resolution Image Examples Testing Yourself Manipulating Images In This Chapter You Will Learn Story: Plot a Plasmid What Do the Commands Mean? Examples Testing Yourself Biopython Working with Sequence Data In This Chapter You Will Learn Story: How to Translate a DNA Coding Sequence into the Corresponding Protein Sequence and Write It to a FASTA File What Do the Commands Mean? Examples Testing Yourself Retrieving Data from Web Resources In This Chapter You Will Learn Story: Searching Publications by Keywords in PubMed, Downloading the Corresponding Records, and Writing Papers Published in a Given Year to a File What Do the Commands Mean? Examples Testing Yourself Working with 3D Structure Data In This Chapter You Will Learn Story: Extracting Atom Names and Three-Dimensional Coordinates from a PDB File What Do the Commands Mean? Examples Testing Yourself Cookbook Recipe 1: The PyCogent Library Recipe 2: Reversing and Randomizing a Sequence Recipe 3: Creating a Random Sequence with Probabilities Recipe 4: Parsing Multiple Sequence Alignments Using Biopython Recipe 5: Calculating a Consensus Sequence from a Multiple Sequence Alignment Recipe 6: Calculating the Distance between Phylogenetic Tree Nodes Recipe 7: Codon Frequencies in a Nucleotide Sequence Recipe 8: Parsing RNA 2D Structures in the Vienna Format Recipe 9: Parsing BLAST XML Output Recipe 10: Parsing SBML Files Recipe 11: Running BLAST Recipe 12: Accessing, Downloading, and Reading Web Pages in Python Recipe 13: Parsing HTML Files Recipe 14: Split a PDB File into PDB Chain Files Recipe 15: Find the Two Closest C Atoms in a PDB Structure Recipe 16: Extract the Interface between Two PDB Chains Recipe 17: Building Homology Models Using Modeller Recipe 18: RNA 3D Homology Modeling with ModeRNA Recipe 19: Calculating RNA Base Pairs from a 3D Structure Recipe 20: A Real Case of Structural Superimposition: The Serine Protease Catalytic Triad Appendix A: Command Overview Appendix B: Python Resources Appendix C: Record Samples Appendix D: Handling Directories and Programs with UNIX

Reviews

"... a significant step forward ... The book is cleverly designed to cover a wide range of subjects in a pleasant, easy-to-follow sequence of chapters. These have been carefully prepared so that the minimum level of interdependence is kept, making it possible to begin working at virtually any level without falling into intricate cross-references. A beginner will find the first chapters quite welcoming while a person with medium or even high levels of programming experience can easily find a suitable entry point in the middle. The book is written using an entertaining style that pushes the reader into a naturally built engaging experience ... the authors have chosen a collection of underlying subject areas that cover a very wide variety of interests, ensuring that mixed audiences are kept engaged. In that sense, the content becomes adaptable to the wide diversity of learners that are found in today's communities of specialised biologists. ... also usable as a reference guide, due to the richness of its worked examples that will prove valuable as seeds for code development for programmers at any level. ... as a single book to support learning Python for problem solvers in the life sciences, this book is certainly a very smart choice. It is also ready for creative teachers to develop more in the same direction." -Pedro L. Fernandes, Instituto Gulbenkian de Ciencia "Having read Managing Your Biological Data with Python brings back memories of the times I started writing my first lines of code nearly a decade ago. As a beginning structural biologist without any coding experience, this book would have been a welcome companion to quickly get me started on my bioinformatical projects with Python. It is this, often pragmatic, attitude scientists have towards programming that makes Python the language of choice for many. A clear syntax, powerful build-in functions and a lively ecosystem of user contributed modules allow you to do advanced things with only little lines of code. The book introduces you to the basic principles of programming in Python using the many build-in functions. It does so using practical examples that you can start using right away in your day-to-day research. Python's modular design principles could even be seen in the organization of this book. If you have never written a line of code in your life, the first chapters are indispensable to teach you basic coding principles but if you have some experience, you can safely skip these. I would however, recommend to read the ones introducing the build-in functions. It never hurts to refresh your memory on the many powerful build-ins Python actually has; I certainly forgot about one or two of them. Working your way through the first chapters will help you get comfortable with Python and lay the foundation for writing more advanced programs in the remaining chapters. These chapters introduce some of the powerful community contributed Python modules that make your life as a biologist a whole lot easier. Again, the example code introducing these modules is of high practical value and together with the coding recipes in the `cookbook' chapter they provide a solid blueprint for you to build your own code upon. I'm confident that reading Managing Your Biological Data with Python will quickly allow you to get the most out of your data and start answering those trilling scientific questions you have, and do all of that while having fun. " -Marc van Dijk, Structural biologist, bioinformaticien, and eScience entrepreneur, Bijvoet Center for Biomolecular Research, Utrecht University, The Netherlands "For many biologists faced with computational challenges, Python has become the language of choice, due to its power, elegance, and simplicity. Managing Your Biological Data with Python by Allegra Via et al. teaches Python using biological examples and discusses important Python-driven applications, such as PyMol and Biopython. The book is an excellent resource for any biologist needing relevant programming skills." -Thomas Hamelryck, Associate Professor, Bioinformatics Center, University of Copenhagen, Denmark "Biological data volumes are growing rapidly as high-throughput technologies (e.g., DNA microarrays or DNA/RNA sequencing) improve. Managing and analyzing biological data are becoming more demanding and the application of programming techniques has simply become a standard. Managing Your Biological Data with Python is one of very few user-friendly books for biologists. It is amazing how clearly authors explain the possible applications of Python for data management (parsing data records, filtering and sorting data) and data visualization (also using the Python interface to R). The book also offers the description of modular programming, which is simply excellent! It guides readers from writing simple functions through writing classes to building program pipelines-everything according to Python coding standards and in an easy-to-follow way. This is absolutely the best book to start learning Python. Intermediate Python users can use this book to learn some new tricks that they could implement in their own code. I can highly recommend this book to researchers, students, and their lecturers." -Dr. Barbara Uszczynska, Centre de Regulacio Genomica (CRG), Barcelona, Spain

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