Bioinformatics Algorithms

Bioinformatics Algorithms: Design and Implementation in Python provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and showing how to implement them. The book focuses on the use of the Python programming language and its algorithms, which is quickly becoming the most popular language in the bioinformatics field. Readers will find the tools they need to improve their knowledge and skills with regard to algorithm development and implementation, and will also uncover prototypes of bioinformatics applications that demonstrate the main principles underlying real world applications.

• Presents an ideal text for bioinformatics students with little to no knowledge of computer programming
• Based on over 12 years of pedagogical materials used by the authors in their own classrooms
• Features a companion website with downloadable codes and runnable examples (such as using Jupyter Notebooks) and exercises relating to the book

Auteur

Miguel Rocha is an Associate Professor at the University of Minho (Portugal), where heteaches in the Informatics Department and has a senior researcher position in the Centreof Biological Engineering. He is the Director and founder of the Master in Bioinformaticssince 2007, teaching and coordinating curricular units related to Bioinformatics algorithmsand tools, data analysis and machine learning. His research is mainly devoted toBioinformatics subjects, including the development of tools and algorithms for metabolicmodelling andomics data analysis.

Contenu

Part I: Bioinformatics Basics 1. Introduction 2. Relevant Biological Concepts 3. Algorithms and Python: Introduction 4. Optimization: Basic Concepts and Algorithms

Part II: Sequence Analysis Algorithms 5. Basic Processing of DNA Sequences: Transcription and Translation 6. Finding Patterns in Sequences 7. Pairwise Sequence Alignment 8. Searching Similar Sequences in Databases 9. Multiple Sequence Alignment 10. Phylogenetic Analysis 11. Motif Discovery 12. Hidden Markov Models 13. Stochastic Algorithms

Part III: Graph and Large-Scale Sequencing Data Processing 14. Graphs 15. Biological Networks 16. Assembling Reads into Genomes 17. Matching Reads to Reference Sequences

Part IV: Conclusions 18. Further Reading and Resources 19. Final Words

Appendix: Python Reference Functions