Genome evolution and adaptation in a long-term experiment with Escherichia coli

By Jeffrey E. Barrick, D. Yu, S. Yoon, Haeyoung Jeong, T. Oh, D. Schneider, Richard E. Lenski, Jihyun F. Kim. Nature, 2009

1,201 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/5349c02ea8c934faf65678e749fad28e78436a9b

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Citations in the corpus

Genetic Draft, Selective Interference, and Population Genetics of Rapid Adaptation (2013, background): “More typically, one finds a handful of polymorphisms with a distribution of effects (Barrick et al., 2009; Lang et al., 2011; Strelkowa and Lässig, 2012).”
Regulation by Small RNAs in Bacteria: Expanding Frontiers (2011, neutral): “coli has not yet revealed critical changes in sRNA genes (Barrick et al., 2009).”
LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets (2012, methodology): “It is possible that some of these SNVs are in fact real as it is known that ‘clonal’ bacterial populations evolve under laboratory conditions (27), maintaining variation even in equilibrium conditions in chemostats (39,40).”
The Speed of Evolution in Large Asexual Populations (2009, background): “Prompted by progress in experimental evolution studies with microbial populations [ 2 , 14, 27, 48, 52, 57, 58], the question of the speed of evolution in the setting of Crow and Kimura has been reconsidered by several authors in recent years [3, 7, 10, 11, 50, 51, 64, 65].”
Adaptive laboratory evolution – principles and applications for biotechnology (2013, background): “Also, a significant parallelism in the change of the gene expression profiles was noticed in these parallel cultures [64]; WGS of the parallel populations revealed that most cultures acquired mutations in the transcriptional regulator nadR, the pyruvate kinase pykF, the rbs operon, the transcription”
Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data (2013, methodology): “Prior to assessing PGM error profiles, we determined if there were any genuine polymorphisms between the PGM determined and reference genomes, which may be the result of accumulated mutations in the genome [4] or sequencing errors in the original genome project.”
Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. (2014, methodology): “…experiments, and synthetic biology experiments with microbes – where detecting a single key genetic change in a sample can be very important (6–9) – it emphasizes sensitivity over speed and reports evidence for a wider variety of genetic variants than most other tools that are currently…”