Phenomics: the next challenge

By David Houle, D. Govindaraju, Stig W. Omholt. Nature reviews genetics, 2010

1,250 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/761e5ea2bc84eb2942fe6bb8bd2c69914504305f

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Citations in the corpus

Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN] (2014, background): “However, our capabilities for systematic assessment and quanti ﬁ cation of plant phenotypes have not kept pace (Houle et al., 2010; Furbank and Tester, 2011).”
Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement (2013, background): “The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twentyfirst century crop varieties.”
Crop Phenomics and High-throughput Phenotyping: Past Decades, Current Challenges and Future Perspectives. (2020, neutral): “Over the past decade, the question of why and how to measure all of genomics has been investigated in depth, and now technological advances will allow us to answer the question of why and how to measure whole-plant phenotypes in the coming decades (Houle et al., 2010).”
High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, background): “Thus, crop performance as an expression of genetic background requires an exploration of the relationship between phenotypes and genetics [14].”
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks (2017, background): “The genotype-to-phenotype gap is one of the most important problems in modern plant breeding (Houle et al., 2010; Großkinsky et al., 2015).”
Multi-modality imagery database for plant phenotyping (2016, background): “A classic genetic approach is to produce a diverse population of mutant lines, grow them either in growth chambers with simulated environmental conditions or directly in the field, visually observe the plants during the growth period, and finally identify plant morphological or physiological pattern”
Computer vision for high content screening (2016, background): “In functional genomics screens, cells with different genetic perturbations (such as a genetic deletion or RNA interference) are arrayed and imaged to discover functions and relationships between genes (Carpenter & Sabatini, 2004; Houle et al ., 2010; Shalem et al ., 2015).”
Learning to Count Leaves in Rosette Plants (2015, background): “However, the interaction between genes and environmental (and growth) conditions leads to a combinatorial explosion of possible phenotypes, making the understanding of the link between genotype and phenotype a complex task [18].”
Plant Phenomics, From Sensors to Knowledge. (2017, positive): “Genomic selection (GS) extends the 159 former approach to establish predictions of the best combinations of alleles for yield [49].”
Biobanks - A Source of Large Biological Data Sets: Open Problems and Future Challenges (2014, background): “from genomics [7], [8], proteomics [9], metabolomics [10], lipidomics [11], transcriptomics [12], epigenetics [13], microbiomics [14], fluxomics [15], phenomics [16], etc.”
The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species (2016, background): “However, leveraging these model data for computational use is non-trivial primarily because the relationships between gene and disease (7) and between model system and disease phenotypes (8) are not straightforward.”