Bioinformatics Algorithms: Design and Implementation in Python

by Miguel Rocha, Pedro G. Ferreira

Overview

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

Product Details

ISBN-13:	9780128125212
Publisher:	Elsevier Science & Technology Books
Publication date:	06/08/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	400
File size:	69 MB
Note:	This product may take a few minutes to download.

About the Author

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.Pedro G. Ferreira is an Assistant Researcher at Ipatimup/i3S (Portugal), where he has an FCT Investigator Starting grant. He develops research on computational biology in particular in the fields of cancer and population genomics. He has collaborated with several research groups and has been involved in different international consortia including ICGC-CLL, GEUVADIS or GTEx. He has intensive training in Bioinformatics and experience in genomics start-up environment where he has developed information systems for personal genomics data interpretation.

Table of Contents

Part I: Bioinformatics Basics1. Introduction2. Relevant Biological Concepts3. Algorithms and Python: Introduction4. Optimization: Basic Concepts and Algorithms Part II: Sequence Analysis Algorithms5. Basic Processing of DNA Sequences: Transcription and Translation6. Finding Patterns in Sequences7. Pairwise Sequence Alignment8. Searching Similar Sequences in Databases9. Multiple Sequence Alignment10. Phylogenetic Analysis11. Motif Discovery12. Hidden Markov Models13. Stochastic Algorithms Part III: Graph and Large-Scale Sequencing Data Processing14. Graphs15. Biological Networks16. Assembling Reads into Genomes17. Matching Reads to Reference Sequences Part IV: Conclusions18. Further Reading and Resources19. Final Words Appendix: Python Reference Functions

What People are Saying About This

From the Publisher

Provides a basis for understanding algorithm development and implementation in Python, with limited computer programming experience needed