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Название: Bioinformatics: A Practical Guide to Next Generation Sequencing Data Analysis
Автор: Hamid D. Ismail
Издательство: CRC Press
Год: 2023
Страниц: 349
Язык: английский
Формат: pdf (true)
Размер: 48.1 MB

Bioinformatics: A Practical Guide to Next Generation Sequencing Data Analysis contains the latest material in the subject, covering 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.

There are several programs for the processing of raw sequence data. FASTX-toolkit is the most popular one for single-end FASTQ files, and Trimmomatic is more sophisticated and can be used for both single-end and paired-end raw data. Fastp filters low-quality reads and automatically recognizes and trims adaptor sequences. It is important to process the paired-end FASTQ files (forward and reverse) together to avoid leaving out singletons, which may not be accepted by almost all aligners. In this chapter, we discussed the command-line programs for quality controls. However, those programs or similar ones are implemented in Python, R, and other programing languages, but understanding the general principle for checking the raw data quality and solving potential quality problems are the same. Most sequencing applications use these kinds of QC processing, but when we cover the metagenomic data analysis, you will learn how to preprocess microbial raw data using different programs. Once the raw sequencing data are cleaned, then we can move safely to the next step of sequence data analysis depending on the application workflow that we are adopting.









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