Next Generation Sequencing (NGS)/Background

The Need for an Up-To-Date Synthesis of Next Generation Sequencing Know-How

The high demand for low-cost sequencing has driven the development of high-throughput sequencing, which is also termed as Next generation sequencing (NGS). Thousands or millions of sequences concurrently produced in next-generation sequencing process. Next generation sequencing has become a commodity. With the commercialization of various affordable desktop sequencers, NGS will be of reach by more traditional wet-lab biologists . As seen in recent years, genome-wide scale computational analysis is increasingly being used as a backbone to foster novel discovery in biomedical research. However, as the quantities of sequence data increase exponentially, the analysis bottle-neck is yet to be solved.

The current sources for NGS informatics are extremely fragmented. A novice could read review articles in various journals, follow discussion threads on forums such as Biostar or SEQanswers , or sign up for courses organized by various institutes. Finding a centralized synthesis is much more difficult. Books are available, but the development of the field is so fast that book chapters risk being obsoleted by the time they are even printed. Moreover, cost for a handful of authors to continually update their text would presumably take up a lot of their schedule.

Drawing from the obvious goodwill and community spirit displayed on discussion forums, and exploiting the collaborative tools made available by the Wikimedia foundation, we propose to initiate the editing of a collaborative WikiBook on NGS. Our plan is to collect a sufficient amount of text that people will be incentivized to contribute to it, essentially providing the same information as a forum but in a tidier form. Ultimately, our goal is to create a collective lab book that explains the key concepts and describes best practices in NGS.

TARGET AUDIENCE

This set of dynamic materials are designed for the bench biologists (advanced PhD students and early career postdoctoral researchers with no or basic bioinformatics experience and demonstrate interest in NGS data analysis). Advanced materials might be added as the community contributes and the needs and trends in the field develop. The flexibility of online material should allow the reader to ignore details in a first read, yet have immediate access to the details they need. However, the overall structure and style should be in priority designed for the non-bioinformatician reader.

Some chapter comes with practical exercise so readers may get themselves familiar with the steps.

Get stuck at data analysis?

Go find help from online communities, including Biostar and SEQanswers, please make sure you follow the guidelines framed by Dall’Olio et al.