Downloads provided by UsageCounts
handle: 10261/99967
Groundbreaking research in food science is shifting from classical methods to novel analytical approaches in which high-throughput techniques have a key role. Among these techniques, RNA-Seq in combination with bioinformatics is applied to investigate topics in food science that were not approachable few years ago. Relevant applications of transcriptomics in modern food science include transcriptome characterization and analysis of gene-expression levels in food crops, foodborne pathogens, and fermenting microorganisms. The aim of the present chapter is to provide an overview of the recent progress in RNA-Seq techniques discussing their advantages and drawbacks. Besides, relevant applications of these technologies will be highlighted in the context of food science to illustrate their impressive potential. Besides, some ideas of the foreseen technological advances and potential applications of these fast-evolving techniques are also provided. © 2014 Elsevier B.V.
This work was supported by AGL2011-29857-C03-01 project (Ministerio de Economía y Competitividad, Spain), and CSD2007-00063 FUN-C-FOOD (Programa CONSOLIDER, Ministerio de Educación y Ciencia, Spain). A.V. thanks the Ministerio de Economía y Competitividad for his FPI pre-doctoral fellowship.
Peer Reviewed
Foodborne pathogens, Fermentations, Next-generation sequencing, Crops, Gene expression, RNA-Seq, Transcriptomics
Foodborne pathogens, Fermentations, Next-generation sequencing, Crops, Gene expression, RNA-Seq, Transcriptomics
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 37 | |
| downloads | 116 |

Views provided by UsageCounts
Downloads provided by UsageCounts