ReviewThe Plant journal : for cell and molecular biology2019
Computational aspects underlying genome to phenome analysis in plants.
Review in The Plant journal : for cell and molecular biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- A Multidimensional Approach to Cereal Caryopsis Development: Insights into Adlay (Plants (Basel, Switzerland) · 2026Article
- Pan-omics insights into abiotic stress responses: bridging functional genomics and precision crop breeding.Functional & integrative genomics · 2025Review
- Special Issue "Molecular Genetics and Plant Breeding 3.0 and 4.0".International journal of molecular sciences · 2025Article
- Review of applications of artificial intelligence (AI) methods in crop research.Journal of applied genetics · 2024Review
- "Chamber #8" - a holistic approach of high-throughput non-destructive assessment of plant roots.Frontiers in plant science · 2023Article
- The Plant Phenomics and Genomics Research Data Repository: An On-Premise Approach for FAIR-Compliant Data Acquisition.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.Plants (Basel, Switzerland) · 2022Review
- A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping.Phenomics (Cham, Switzerland) · 2022Review
- Article
- Meta-analysis: Congruence of genomic and phenotypic differentiation across diverse natural study systems.Evolutionary applications · 2021Article
- A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion.Frontiers in plant science · 2021Article
- Next-Generation Breeding Strategies for Climate-Ready Crops.Frontiers in plant science · 2021Review
- Applying Molecular Phenotyping Tools to Explore Sugarcane Carbon Potential.Frontiers in plant science · 2021Review
- WinRoots: A High-Throughput Cultivation and Phenotyping System for Plant Phenomics Studies Under Soil Stress.Frontiers in plant science · 2021Article
- Oxford Nanopore sequencing: new opportunities for plant genomics?Journal of experimental botany · 2020Article
- Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm.Plants (Basel, Switzerland) · 2020Review
- Emerging Advanced Technologies to Mitigate the Impact of Climate Change in Africa.Plants (Basel, Switzerland) · 2020Review
- AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana.Nucleic acids research · 2020Article
- Recent Progress in Germplasm Evaluation and Gene Mapping to Enable Breeding of Drought-Tolerant Wheat.Frontiers in plant science · 2020Review
- Data synthesis for crop variety evaluation. A review.Agronomy for sustainable development · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait-trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.