Publications

Autoren Titel
2017
Dreissig S, Schiml S, Schindele P, Weiss O, Rutten T, Schubert V, Gladilin E, Mette M F, Puchta H, Houben A Live cell CRISPR-imaging in plants reveals dynamic telomere movements. Plant J. 91 (2017) 565-573. dx.doi.org/10.1111/tpj.13601
Gladilin E Graph-theoretical model of global human interactome reveals enhanced long-range communicability in cancer networks. PLoS One 12 (2017) e0170953. dx.doi.org/10.1371/journal.pone.0170953
González-Avalos P, Mürnseer M, Deeg J, Bachmann A, Spatz J, Dooley S, Eils R, Gladilin E Quantification of substrate and cellular strains in stretchable 3D cell cultures: an experimental and computational framework. J. Microsc. 266 (2017) 115-125. dx.doi.org/10.1111/jmi.12520
Guo Z, Chen D, Alqudah A M, Röder M S, Ganal M W, Schnurbusch T Genome-wide association analyses of 54 traits identified multiple loci for the determination of floret fertility in wheat. New Phytol. 214 (2017) 257-270. dx.doi.org/10.1111/nph.14342
Muraya M M, Chu J, Zhao Y, Junker A, Klukas C, Reif J C, Altmann T Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non-invasive phenotyping. Plant J. 89 (2017) 366–380. dx.doi.org/10.1111/tpj.13390
2016
Arend D, Lange M, Pape J-M, Weigelt-Fischer K, Arana-Ceballos F, Mücke I, Klukas C, Altmann T, Scholz U, Junker A Quantitative monitoring of Arabidopsis thaliana growth and development using high-throughput plant phenotyping. Scientific Data 3 (2016) 160055. dx.doi.org/10.1038/sdata.2016.55
Chen D, Shi R, Pape J-M, Klukas C Predicting plant biomass accumulation from image-derived parameters. bioRxiv (2016) dx.doi.org/10.1101/046656
Ćwiek-Kupczyńska H, Altmann T, Arend D, Arnaud E, Chen D, Cornut G, Fiorani F, Frohmberg W, Junker A, Klukas C, Lange M, Mazurek C, Nafissi A, Neveu P, van Oeveren J, Pommier C, Poorter H, Rocca-Serra P, Sansone S-A, Scholz U, van Schriek M, Seren Ü, Usadel B, Weise S, Kersey P, Krajewski P Measures for interoperability of phenotypic data: minimum information requirements and formatting. Plant Methods 12 (2016) 44. dx.doi.org/10.1186/s13007-016-0144-4
Pape J-M Ein Klassifikationssystem zur quantitativen Analyse von Krankheitssymptomen im Kontext der Hochdurchsatz-Phänotypisierung von Pflanzen. (Master Thesis) Magdeburg, Fakultät für Informatik, Otto-von-Guericke-Universität (2016) 84 pp.
Scharr H, Minervini M, French A P, Klukas C, Kramer D M, Liu X, Luengo I, Pape J-M, Polder G, Vukadinovic D, Yin X, Tsaftaris S A Leaf segmentation in plant phenotyping: a collation study. Mach. Vision Appl. 27 (2016) 585-606. dx.doi.org/10.1007/s00138-015-0737-3
2015
Castellini A, Edlich-Muth C, Muraya M, Klukas C, Altmann T, Selbig J Towards a graph-theoretic approach to hybrid performance prediction from large-scale phenotypic data. In: Lones M, Tyrrell A, Smith S, Fogel G (Eds.): Information Processing in Cells and Tissues.10th International Conference, IPCAT 2015, San Diego, CA, USA, September 14-16, 2015, Proceedings. (Series: Lecture Notes in Computer Science, Vol. 9303) : Springer International Publishing (2015) 173-184. dx.doi.org/10.1007/978-3-319-23108-2_15 ISBN 978-3-319-23107-5
Guo Z, Chen D, Schnurbusch T Variance components, heritability and correlation analysis of anther and ovary size during the floral development of bread wheat. J. Exp. Bot. 66 (2015) 3099-3111. dx.doi.org/10.1093/jxb/erv117
Junker A, Muraya M M, Weigelt-Fischer K, Arana-Ceballos F, Klukas C, Melchinger A E, Meyer R C, Riewe D, Altmann T Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems. Front. Plant Sci. 5 (2015) 770. dx.doi.org/10.3389/fpls.2014.00770
Krajewski P, Chen D, Ćwiek H, van Dijk A D J, Fiorani F, Kersey P, Klukas C, Lange M, Markiewicz A, Nap J P, van Oeveren J, Pommier C, Scholz U, van Schriek M, Usadel B, Weise S Towards recommendations for metadata and data handling in plant phenotyping. J. Exp. Bot. 66 (2015) 5417-5427. dx.doi.org/10.1093/jxb/erv271
Muscolo A, Junker A, Klukas C, Weigelt-Fischer K, Riewe D, Altmann T Phenotypic and metabolic responses to drought and salinity of four contrasting lentil accessions. J. Exp. Bot. 66 (2015) 5467-5480. dx.doi.org/10.1093/jxb/erv208
Neumann K, Klukas C, Friedel S, Rischbeck P, Chen D, Entzian A, Stein N, Graner A, Kilian B Dissecting spatio-temporal biomass accumulation in barley under different water regimes using high-throughput image analysis. Plant Cell Environ. 38 (2015) 1980-1996. dx.doi.org/10.1111/pce.12516
Pape J M, Klukas C Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant images. In: Tsaftaris S A, Scharr H, Pridmore T (Eds.): Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP). : BMVA Press (2015) 3.1-3.12. dx.doi.org/10.5244/C.29.CVPPP.3 ISBN 1-901725-55-3
Pape J M, Klukas C 3-D histogram-based segmentation and leaf detection for rosette plants. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (Eds.): Computer Vision – ECCV 2014 Workshops: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, proceedings, part IV (Series: Lecture Notes in Computer Science) Cham: Springer International Publishing Switzerland (2015) 61-74. ISBN 978-3-319-16219-5
Rahaman M M, Chen D, Gillani Z, Klukas C, Chen M Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Front. Plant Sci. 6 (2015) 619. dx.doi.org/10.3389/fpls.2015.00619
Schilling S, Gramzow L, Lobbes D, Kirbis A, Weilandt L, Hoffmeier A, Junker A, Weigelt-Fischer K, Klukas C, Wu F, Meng Z, Altmann T, Theissen G Non-canonical structure, function and phylogeny of the B MADS-box gene OsMADS30 of rice (Oryza sativa). Plant J. 84 (2015) 1059-1072. dx.doi.org/10.1111/tpj.13055
Yuan C, Wang J, Harrison A P, Meng X, Chen D, Chen M Genome-wide view of natural antisense transcripts in Arabidopsis thaliana. DNA Res. 22 (2015) 233-243. dx.doi.org/10.1093/dnares/dsv008
2014
Chen D, Chen M, Altmann T, Klukas C Bridging genomics and phenomics. In: Chen M, Hofestädt R (Eds.): Approaches in integrative bioinformatics: towards the virtual cell. Berlin [u.a.]: Springer (2014) 299-333. dx.doi.org/10.1007/978-3-642-41281-3_11 ISBN 978-3-642-41280-6
Chen D, Fu L Y, Zhang Z, Li G, Zhang H, Jiang L, Harrison A P, Shanahan H P, Klukas C, Zhang H Y, Ruan Y, Chen L L, Chen M Dissecting the chromatin interactome of microRNA genes. Nucleic Acids Res. 42 (2014) 3028-3043. dx.doi.org/10.1093/nar/gkt1294
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 26 (2014) 4636-4655. dx.doi.org/10.1105/tpc.114.129601
Harshavardhan V T, Van Son L, Seiler C, Junker A, Weigelt-Fischer K, Klukas C, Altmann T, Sreenivasulu N, Bäumlein H, Kuhlmann M AtRD22 and AtUSPL1, members of the plant-specific BURP domain family involved in Arabidopsis thaliana drought tolerance. PLoS One 9 (2014) e110065. dx.doi.org/10.1371/journal.pone.0110065
Klukas C, Chen D, Pape J M Integrated Analysis Platform: an open-source information system for high-throughput plant phenotyping. Plant Physiol. 165 (2014) 506-518. dx.doi.org/10.1104/pp.113.233932
2013
Chen D, Chen M, Klukas C Phenomics collaboration website. phenomics.cn (2013).
Klukas C, Chen D, Pape J-M IAP – The Integrated Analysis Platform for High-Througput Plant Phenotyping. iap.ipk-gatersleben.de (2013).
Lu X, Chen D, Shu D, Zhang Z, Wang W, Klukas C, Chen L L, Fan Y, Chen M, Zhang C The differential transcription network between embryo and endosperm in the early developing maize seed. Plant Physiol. 162 (2013) 440-455. dx.doi.org/10.1104/pp.113.214874
Rohn H, Junker A, Hartmann A, Grafahrend-Belau E, Treutler H, Klapperstück M, Czauderna T, Klukas C, Schreiber F Vanted 2.0. www.vanted.org (2013).