This book contains the latest material in the subject, covering NGS applications and meeting the requirements of a complete semester course. This book provides both concept and practice to satisfy the exact need of researchers seeking to use NGS data reprocessing, genome assembly, variant discovery, gene profiling, epigenetics, and metagenomics.
This book contains the latest material in the subject, covering NGS applications and meeting the requirements of a complete semester course. This book provides both concept and practice to satisfy the exact need of researchers seeking to use NGS data reprocessing, genome assembly, variant discovery, gene profiling, epigenetics, and metagenomics.
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.
Inhaltsangabe
1. Sequencing and Raw Sequence Data Quality Control. 2. Mapping of Sequence Reads to the Reference Genomes. 3. De Novo Genome Assembly. 4. Variant Discovery. 5. RNA-Seq Data Analysis. 6. Chromatin Immunoprecipitation Sequencing. 7. Targeted Gene Metagenomic Data Analysis. 8. Shotgun Metagenomic Data Analysis.
1. Sequencing and Raw Sequence Data Quality Control. 2. Mapping of Sequence Reads to the Reference Genomes. 3. De Novo Genome Assembly. 4. Variant Discovery. 5. RNA-Seq Data Analysis. 6. Chromatin Immunoprecipitation Sequencing. 7. Targeted Gene Metagenomic Data Analysis. 8. Shotgun Metagenomic Data Analysis.
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