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  • Print publication year: 2016
  • Online publication date: June 2016

10 - Perspective: Systematics in the age of genomics

from Part III - Next Generation Challenges and Questions

Summary

The advent of the data age

The study of the DNA and protein record is a key component of systematic biology research. Recent technological advances in genome science have enabled researchers to routinely generate unprecedented, genome-scale, amounts of sequence data, opening the floodgates for the study of the genome content and function of any organism across the Tree of Life (ToL) (Rokas and Abbot 2009). The main catalyst for these changes has been the development of several different so-called next generation DNA sequencing technologies (NGS) that are capable of producing orders of magnitude more data, for orders of magnitude lower cost than Sanger sequencing approaches (Glenn 2011).

Astonishingly, the amount of sequence data that a single NGS machine currently produces in a few days is larger than the total amount of sequence data collected by individual users via traditional methods that is deposited in GenBank (Gilad et al. 2009). This phenomenal increase in data generation has not only enabled the collection of more sequence data, but also the systematic collection of new types of sequence data (e.g. microRNAs, SINEs, LINEs and other rare genomic changes) that were previously laborious to obtain, as well as the development of new protocols (e.g. RAD-Tags, Baird et al. 2008) and computational pipelines (e.g. phylogenomics, Hittinger et al. 2010b; metagenomics, Patil et al. 2011) for doing so. Furthermore, NGS technologies yield not only qualitative information about the sequence of every DNA fragment analysed, but also quantitative information about the relative abundance of each DNA fragment in the library sequenced (Rokas and Abbot 2009).

The abundance of NGS data, their qualitative and quantitative nature, and their applicability to the study of any organism for which fresh DNA or RNA is available (this volume, Chapter 14) has enabled researchers to adopt NGS not only for answering old questions with new data rigour, but also for formulating and tackling a new ‘generation’ of questions (Rokas and Abbot 2009).

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