LEAFDATA: a literature-curated database for Arabidopsis leaf development

Szakonyi, Dóra

doi:10.1186/s13007-016-0115-9

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…Total RNA-Seq data of other plant leaves were also from GEO database ( Triticum aestivum , GSE58805; Glycine max , GSE69469; Zea mays , GSE71046; Oryza sativa Indica , GSE74465). Genes involved in Arabidopsis leaf development or senescence were from LEAFDATA [ 19 ] and LSD [ 20 ]. Arabidopsis transcription factors (TFs) were downloaded from AtTFDB [ 21 ] and PlantTFDB [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Identification and characterization of ncRNA-associated ceRNA networks in Arabidopsis leaf development

et al. 2018

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BackgroundLeaf development is a complex biological process that is accompanied by wide transcriptional changes. Many protein-coding genes have been characterized in plant leaves, but little attention has been given to noncoding RNAs (ncRNAs). Moreover, increasing evidence indicates that an intricate interplay among RNA species, including protein-coding RNAs and ncRNAs, exists in eukaryotic transcriptomes, however, it remains elusive in plant leaves.ResultsWe detected novel ncRNAs, such as circular RNAs (circRNAs) and long noncoding RNAs (lncRNAs), and further constructed and analyzed their associated competitive endogenous RNA (ceRNA) networks in Arabidopsis leaves. Transcriptome profiling showed extensive changes during leaf development. In addition, comprehensive detection of circRNAs in other plant leaves suggested that circRNAs are widespread in plant leaves. To investigate the complex post-transcriptional interactions in Arabidopsis leaves, we constructed a global circRNA/lncRNA-associated ceRNA network. Functional analysis revealed that ceRNAs were highly correlated with leaf development. These ceRNAs could be divided into six clusters, which were enriched for different functional classes. Stage-specific ceRNA networks were further constructed and comparative analysis revealed different roles of stage common and specific hub ceRNAs.ConclusionsOur results demonstrate that understanding the ceRNA interactions will lead insights into gene regulations implicated in leaf development.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4993-2) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Identification and characterization of ncRNA-associated ceRNA networks in Arabidopsis leaf development

et al. 2018

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“…There are 96 articles related to the keywords of “UAV,” “UAS,” “Drone,” “Unmanned Aerial Vehicle,” “Unmanned Aerial System,” “Unmanned Aircraft,” “Low Altitude Platform,” “Crop,” Plant,” “Crop breeding,” “Remote Sensing,” “Field-Based,” “Phenotyping,” and “Phenomics” in the Web of Science™ Core Collection Database (THOMSON REUTERS™) until May 17, 2017. However, there are only six articles that explicitly include “Phenotyping” or “Phenomics” in the titles and keyword (Zaman-Allah et al, 2015 ; Gomez-Candon et al, 2016 ; Haghighattalab et al, 2016 ; Holman et al, 2016 ; Shi et al, 2016 ; Watanabe et al, 2017 ). The other literatures are closely related to crop phenotyping using UAV-RSPs but do not explicitly mention crop phenotype; the research focuses on one or more crop traits.…”

Section: Literature Surveymentioning

confidence: 99%

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

et al. 2017

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Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

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“…Plant morphologic traits can often be used for evaluating plant growth (Hosoi and Omasa, 2012 ; Taheriazad et al, 2016 ), which determines plant performance in terms of final crop biomass and yield (Dhondt et al, 2013 ). Several studies showed that morphologic traits such as canopy height and leaf area index (LAI) were strongly related to plant species, type of cultivation, plant growth rate, and final yield (Gebbers et al, 2011 ; Sharma and Ritchie, 2015 ; Friedli et al, 2016 ; Sun et al, 2017 ). Importantly, plant growth and yield is dependent upon leaf area development, the average photosynthetic efficiency of all leaves in the plant canopy (Gardner, 1985 ; Krieg and Sung, 1986 ), and partitioning of dry matter to the harvested portion of the crop (Earl and Davis, 2003 ).…”

Section: Introductionmentioning

confidence: 99%

“…Plant growth curves were generated by the platform, which were used to gain a deeper understanding of energy distribution. Friedli et al ( 2016 ) introduced a terrestrial 3D laser scanner-based plant growth monitoring system which could be used for monitoring canopy height growth for different crops under field conditions. Paulus et al ( 2014b ) monitored the organ-specific growth dynamics of cereal plants with a high precision triangulation line laser scanner by scanning every 2–3 days.…”

Section: Introductionmentioning

confidence: 99%

In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR

et al. 2018

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Plant breeding programs and a wide range of plant science applications would greatly benefit from the development of in-field high throughput phenotyping technologies. In this study, a terrestrial LiDAR-based high throughput phenotyping system was developed. A 2D LiDAR was applied to scan plants from overhead in the field, and an RTK-GPS was used to provide spatial coordinates. Precise 3D models of scanned plants were reconstructed based on the LiDAR and RTK-GPS data. The ground plane of the 3D model was separated by RANSAC algorithm and a Euclidean clustering algorithm was applied to remove noise generated by weeds. After that, clean 3D surface models of cotton plants were obtained, from which three plot-level morphologic traits including canopy height, projected canopy area, and plant volume were derived. Canopy height ranging from 85th percentile to the maximum height were computed based on the histogram of the z coordinate for all measured points; projected canopy area was derived by projecting all points on a ground plane; and a Trapezoidal rule based algorithm was proposed to estimate plant volume. Results of validation experiments showed good agreement between LiDAR measurements and manual measurements for maximum canopy height, projected canopy area, and plant volume, with R2-values of 0.97, 0.97, and 0.98, respectively. The developed system was used to scan the whole field repeatedly over the period from 43 to 109 days after planting. Growth trends and growth rate curves for all three derived morphologic traits were established over the monitoring period for each cultivar. Overall, four different cultivars showed similar growth trends and growth rate patterns. Each cultivar continued to grow until ~88 days after planting, and from then on varied little. However, the actual values were cultivar specific. Correlation analysis between morphologic traits and final yield was conducted over the monitoring period. When considering each cultivar individually, the three traits showed the best correlations with final yield during the period between around 67 and 109 days after planting, with maximum R2-values of up to 0.84, 0.88, and 0.85, respectively. The developed system demonstrated relatively high throughput data collection and analysis.

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LEAFDATA: a literature-curated database for Arabidopsis leaf development

Cited by 6 publications

References 36 publications

Identification and characterization of ncRNA-associated ceRNA networks in Arabidopsis leaf development

Identification and characterization of ncRNA-associated ceRNA networks in Arabidopsis leaf development

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR

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