Bioinformatics for High Throughput Sequencing17 Jun 2013
Bioinformatics for High Throughput Sequencing PDF Download Ebook. Naiara Rodrguez-Ezpeleta, Michael Hackenberg and Ana M. Aransay extract biological knowledge out of the large amount of information generated by the scientific community. The chapters compiled in this volume will allow both biologists and computer scientists to understand the significance and potential of this discipline in the analysis of high throughput sequencing data.
An essential reference book, not only for serving as guideline for the comprehension of the main data analysis steps, but also for providing new insights into existing software and inspiration to develop new applications. Next generation sequencing is revolutionizing molecular biology. Owing to this new technology it is now possible to carry out a panoply of experiments at an unprecedented low cost and high speed.
These go from sequencing whole genomes, transcriptomes and small non-coding RNAs to description of methylated regions, identification protein DNA interaction sites and detection of structural variation. The generation of gigabases of sequence information for each of this huge bandwidth of applications in just a few days makes the development of bioinformatics applications for next generation sequencing data analysis as urgent as challenging.
The purpose of this book is to collect in a single volume the essentials of high throughput sequencing data analysis. These new technologies allow performing, at an unprecedented low cost and high speed, panoply of experiments spanning the sequencing of whole genomes or transcriptomes, the profi ling of DNA methylation, and the detection of proteinDNA interaction sites, among others.
In each experiment a massive amount of sequence information is generated, making data analysis the major challenge in high throughput sequencing-based projects. Hundreds of bioinformatics applications have been developed so far, most of them focusing on specific tasks. Indeed, numerous approaches have been proposed for each analysis step, while integrated analysis applications and protocols are generally missing.
As a result, even experienced bioinformaticians struggle when they have to discern among countless possibilities to analyze their data. This, together with a lack of enough qualifi ed personnel, reveals an urgent need to train bioinformaticians in existing approaches and to develop integrated, from start to end software applications to face present and future challenges in data analysis.
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