Unlocking the potential of plant phenotyping data through integration and data-driven approaches

Current Opinion in Systems Biology

Volumn 4, Issue , 2017, Pages 58-63

  Coppens, Frederik ^a,b   Wuyts, Nathalie ^a,b   Inzé, Dirk ^a,b   Dhondt, Stijn ^a,b  

^a GHENT UNIVERSITY   (Belgium)

^b Belgium   (Belgium)

Author keywords

Data integration; Data management; Data driven analysis; Plant phenotyping

Indexed keywords

DATA EXTRACTION; GENETIC DATABASE; GENOTYPE PHENOTYPE CORRELATION; HIGH THROUGHPUT SCREENING; HYPOTHESIS; IMAGE PROCESSING; INFORMATION PROCESSING; INFORMATION SYSTEM; MACHINE LEARNING; METADATA; NONHUMAN; PHENOTYPE; PLANT GENETICS; PLANT GROWTH; REVIEW;

EID: 85045034307     PISSN: None     EISSN: 24523100     Source Type: Journal    
DOI: 10.1016/j.coisb.2017.07.002     Document Type: Review

References (40)

1. Dhondt S., Wuyts N., Inzé D. Cell to whole-plant phenotyping: the best is yet to come. Trends Plant Sci 2013, 18:428-439.
2. Furbank R.T., Tester M. Phenomics - technologies to relieve the phenotyping bottleneck. Trends Plant Sci 2011, 16:635-644.
3. Fahlgren N., Gehan M.A., Baxter I. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 2015, 24:93-99.
4. Araus J.L., Cairns J.E. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 2014, 19:52-61.
5. Dhondt S., Gonzalez N., Blomme J., De Milde L., Van Daele T., Van Akoleyen D., Storme V., Coppens F., Beemster G.T.S., Inzé D. High-resolution time-resolved imaging of in vitro Arabidopsis rosette growth. Plant J 2014, 80:172-184.
6. 4D: a light-field imaging system for non-invasive and accurate monitoring of spatio-temporal plant growth. Plant J 2015, 82:693-706.
7. Munns R., James R.A., Sirault X.R.R., Furbank R.T., Jones H.G. New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J Exp Bot 2010, 61:3499-3507.
8. Merlot S., Mustilli A.-C., Genty B., North H., Lefebvre V., Sotta B., Vavasseur A., Giraudat J. Use of infrared thermal imaging to isolate Arabidopsis mutants defective in stomatal regulation. Plant J 2002, 30:601-609.
9. Poorter H., Fiorani F., Pieruschka R., Wojciechowski T., van der Putten W.H., Kleyer M., Schurr U., Postma J. Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field. New Phytol 2016, 212:838-855.
10. Zamir D. Where have all the crop phenotypes gone?. PLoS Biol 2013, 11:e1001595.
11. Toribio A.L., Alako B., Amid C., Cerdeño-Tarrága A., Clarke L., Cleland I., Fairley S., Gibson R., Goodgame N., ten Hoopen P., et al. European nucleotide archive in 2016. Nucleic Acids Res 2017, 45:D32-D36.
12. Leinonen R., Sugawara H., Shumway M. The sequence Read archive. Nucleic Acids Res 2011, 39:D19-D21. on behalf of the International Nucleotide Sequence Database Collaboration.
13. Seren Ü., Grimm D., Fitz J., Weigel D., Nordborg M., Borgwardt K., Korte A. AraPheno: a public database for Arabidopsis thaliana phenotypes. Nucleic acids Res 2017, 45:D1054-D1059.
14. Arend D., Junker A., Scholz U., Schüler D., Wylie J., Lange M. PGP repository: a plant phenomics and genomics data publication infrastructure. Database 2016, 2016. baw033.
15. Li Y.-F., Kennedy G., Davies F., Hunter J. PODD: an ontology-driven data repository for collaborative phenomics research. Lect Notes Comput Sci 2010, 6102:179-188.
16. Das A., Bucksch A., Price C.A., Weitz J.S. ClearedLeavesDB: an online database of cleared plant leaf images. Plant Methods 2014, 10:8.
17. Fabre J., Dauzat M., Nègre V., Wuyts N., Tireau A., Gennari E., Neveu P., Tisné S., Massonnet C., Hummel I., et al. PHENOPSIS DB: an Information System for Arabidopsis thaliana phenotypic data in an environmental context. BMC Plant Biol 2011, 11:77.
18. Fahlgren N., Feldman M., Gehan M.A., Wilson M.S., Shyu C., Bryant D.W., Hill S.T., McEntee C.J., Warnasooriya S.N., Kumar I., et al. A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria. Mol Plant 2015, 8:1520-1535.
19. Steinbach D., Alaux M., Amselem J., Choisne N., Durand S., Flores R., Keliet A.-O., Kimmel E., Lapalu N., Luyten I., et al. GnpIS: an information system to integrate genetic and genomic data from plants and fungi. Database 2013, 2013. bat058.
20. Klukas C., Chen D., Pape J.-M. Integrated Analysis Platform: an open-source information system for high-throughput plant phenotyping. Plant Physiol 2014, 165:506-518.
21. Lobet G., Draye X., Périlleux C. An online database for plant image analysis software tools. Plant Methods 2013, 9:38.
22. Cobb J.N., DeClerck G., Greenberg A., Clark R., McCouch S. Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theor Appl Genet 2013, 126:867-887.
23. Pradal C., Artzet S., Chopard J., Dupuis D., Fournier C., Mielewczik M., Nègre V., Neveu P., Parigot D., Valduriez P., et al. InfraPhenoGrid: a scientific workflow infrastructure for plant phenomics on the Grid. Future Gener Comput Syst 2017, 67:341-353.
24. Allan C., Burel J.-M., Moore J., Blackburn C., Linkert M., Loynton S., MacDonald D., Moore W.J., Neves C., Patterson A., et al. OMERO: flexible, model-driven data management for experimental biology. Nat methods 2012, 9:245-253.
25. Kvilekval K., Fedorov D., Obara B., Singh A., Manjunath B.S. Bisque: a platform for bioimage analysis and management. Bioinformatics 2010, 26:544-552.
26. Goossens B., De Vylder J., Philips W. Quasar - a new heterogeneous programming framework for image and video processing algorithms on CPU and GPU. IEEE International Conference on Image Processing (ICIP) proceedings; Paris 2014, 2183-2185.
27. Krajewski P., Chen D., Ćwiek H., van Dijk A.D.J., Fiorani F., Kersey P., Klukas C., Lange M., Markiewicz A., Nap J.P., et al. Towards recommendations for metadata and data handling in plant phenotyping. J Exp Bot 2015, 66:5417-5427.
28. Massonnet C., Vile D., Fabre J., Hannah M.A., Caldana C., Lisec J., Beemster G.T.S., Meyer R.C., Messerli G., Gronlund J.T., et al. Probing the reproducibility of leaf growth and molecular phenotypes: a comparison of three Arabidopsis accessions cultivated in ten laboratories. Plant Physiol 2010, 152:2142-2157.
29. Poorter H., Fiorani F., Stitt M., Schurr U., Finck A., Gibon Y., Usadel B., Munns R., Atkin O.K., Tardieu F., et al. The art of growing plants for experimental purposes: a practical guide for the plant biologist. Funct Plant Biol 2012, 39:821-838.
30. Faulconbridge A., Burdett T., Brandizi M., Gostev M., Pereira R., Vasant D., Sarkans U., Brazma A., Parkinson H. Updates to BioSamples database at European Bioinformatics Institute. Nucleic Acids Res 2014, 42:D50-D52.
31. Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.-W., da Silva Santos L.B., Bourne P.E., et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016, 3.
32. Singh A., Ganapathysubramanian B., Singh A.K., Sarkar S. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 2016, 21:110-124.
33. Baranowski P., Jedryczka M., Mazurek W., Babula-Skowronska D., Siedliska A., Kaczmarek J. Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS One 2015, 10:e0122913.
34. Calderón R., Navas-Cortés J.A., Zarco-Tejada P.J. Early detection and quantification of verticillium wilt in olive using hyperspectral and thermal imagery over large areas. Remote Sens 2015, 7:5584-5610.
35. Tsaftaris S.A., Minervini M., Scharr H. Machine learning for plant phenotyping needs image processing. Trends Plant Sci 2016, 21:989-991.
36. Pound M.P., Burgess A.J., Wilson M.H., Atkinson J.A., Griffiths M., Jackson A.S., Bulat A., Tzimiropoulos G., Wells D.M., Murchie E.H., et al. Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping. bioRxiv 2016, 10.1101/053033.
37. Donné S., Luong Q., Goossens B., Dhondt S., Wuyts N., Inzé D., Philips W. Machine learning for maize plant segmentation. Proceedings of the 25th Belgian-Dutch conference on machine learning; 12-13 September 2016 2016.
38. Wuyts N., Dhondt S., Inzé D. Measurement of plant growth in view of an integrative analysis of regulatory networks. Curr Opin plant Biol 2015, 25:90-97.
39. Gibbs J.A., Pound M., Wells D.M., Murchie E., French A., Pridmore T. Three-dimensional reconstruction of plant shoots from multiple images using an active vision system. Proceedings of the IROS workshop on agri-food robotics, October 2, 2015, Hamburg, Germany 2015, G. Kootstra, Y. Edan, E. van Henten, M. Bergerman (Eds.).
40. Chen D., Neumann K., Friedel S., Kilian B., Chen M., Altmann T., Klukas C. Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. Plant Cell 2014, 26:4636-4655.