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Small Genomes and Big Data: Adaptation of Plastid Genomics to the High-Throughput Era.
Klinger, Christen M; Richardson, Elisabeth.
Afiliação
  • Klinger CM; Division of Infectious Diseases, Department of Medicine, University of Alberta, Edmonton, AB T6G 2R3, Canada.
  • Richardson E; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada. ehrichar@ualberta.ca.
Biomolecules ; 9(8)2019 07 24.
Article em En | MEDLINE | ID: mdl-31344945
Plastid genome sequences are becoming more readily available with the increase in high-throughput sequencing, and whole-organelle genetic data is available for algae and plants from across the diversity of photosynthetic eukaryotes. This has provided incredible opportunities for studying species which may not be amenable to in vivo study or genetic manipulation or may not yet have been cultured. Research into plastid genomes has pushed the limits of what can be deduced from genomic information, and in particular genomic information obtained from public databases. In this Review, we discuss how research into plastid genomes has benefitted enormously from the explosion of publicly available genome sequence. We describe two case studies in how using publicly available gene data has supported previously held hypotheses about plastid traits from lineage-restricted experiments across algal and plant diversity. We propose how this approach could be used across disciplines for inferring functional and biological characteristics from genomic approaches, including integration of new computational and bioinformatic approaches such as machine learning. We argue that the techniques developed to gain the maximum possible insight from plastid genomes can be applied across the eukaryotic tree of life.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Plantas / Plastídeos / Biologia Computacional Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Plantas / Plastídeos / Biologia Computacional Idioma: En Ano de publicação: 2019 Tipo de documento: Article