Bioinformatics: A Practical Guide to Next Generation Sequencing Data Analysis (Chapman & Hall/CRC Computational Biology Series)

Ismail, Hamid D.

ISBN 10: 103240891X / ISBN 13: 9781032408910

Editore: Chapman and Hall/CRC, 2023

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Dati bibliografici

Titolo: Bioinformatics: A Practical Guide to Next ...

Casa editrice: Chapman and Hall/CRC

Data di pubblicazione: 2023

Legatura: Soft cover

Condizione: New

Edizione: 1st Edition

Descrizione articolo

Riassunto:

This book contains the latest material in the subject, covering next generation sequencing (NGS) applications and meeting the requirements of a complete semester course. This book digs deep into analysis, providing both concept and practice to satisfy the exact need of researchers seeking to understand and use NGS data reprocessing, genome assembly, variant discovery, gene profiling, epigenetics, and metagenomics. The book does not introduce the analysis pipelines in a black box, but with detailed analysis steps to provide readers with the scientific and technical backgrounds required to enable them to conduct analysis with confidence and understanding. The book is primarily designed as a companion for researchers and graduate students using sequencing data analysis but will also serve as a textbook for teachers and students in biology and bioscience.

Informazioni sull?autore:

Hamid D. Ismail received his M.Sc. and Ph.D. in computational science from North Carolina Agricultural and Technical State University (NC A&T), USA, and DVM and B.Sc. from the University of Khartoum, Sudan. He earned several professional certifications including SAS Advanced Programmer and SQL Expert Programmer. Currently he works as a post-doc scholar at Michigan Technological University and adjunct professor of Data Science and Bioinformatics at NC A&T State University. Hamid is a bioinformatician, biologist, data scientist, statistician, and machine learning specialist. He contributed widely to the field of bioinformatics by developing bioinformatics tools and methods for applications of machine learning on genomic data.