Coming of age: ten years of next-generation sequencing technologies

By Sara Goodwin, J. McPherson, W. Richard McCombie. Nature reviews genetics, 2016

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Microbial communities in anammox reactors: a review (2017, background): “NGS has allowed the recovery of thousands of reads from complex environmental samples and within a short period of time, and has generated a large amount of data that may be used to evaluate the biodiversity of specific environments and to understand increasingly complex phenotypes [12,13].”
Creating a universal SNP and small indel variant caller with deep neural networks (2016, background): “At its core, DeepVariant (1) generates candidate entities with high sensitivity but low specificity, (2) represents the experimental data about each entity in a machine-learning compatible format and then (3) applies deep learning to assign meaningful biological labels to these entities.”
Splatter: simulation of single-cell RNA sequencing data (2017, background): “Background 13 The first decade of next-generation sequencing has seen an explosion in our 14 understanding of the genome [1].”
Transcriptomics technologies (2017, background): “Consequently, the development of DNA sequencing technologies has been a defining feature of RNA-Seq [73][75][76].”
Proposed minimal standards for the use of genome data for the taxonomy of prokaryotes. (2018, background): “In the last decade, several next generation sequencing platforms were commercially introduced and proved to provide adequate genome data for taxonomic purposes in microbiology [3, 24, 25].”