@misc{RajasundaramSelbig2016,
  author    = {Rajasundaram, Dhivyaa and Selbig, Joachim},
  title     = {More effort — more results},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {923},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-44263},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-442639},
  pages     = {57 -- 61},
  year      = {2016},
  abstract  = {The development of 'omics' technologies has progressed to address complex biological questions that underlie various plant functions thereby producing copious amounts of data. The need to assimilate large amounts of data into biologically meaningful interpretations has necessitated the development of statistical methods to integrate multidimensional information. Throughout this review, we provide examples of recent outcomes of 'omics' data integration together with an overview of available statistical methods and tools.},
  language  = {en}
}
@article{JargoschKroegerGralinskaetal.2016,
  author    = {Jargosch, M. and Kroeger, S. and Gralinska, E. and Klotz, Ulrike and Fang, Z. and Chen, W. and Leser, U. and Selbig, Joachim and Groth, Detlef and Baumgrass, Ria},
  title     = {Data integration for identification of important transcription factors of STAT6-mediated cell fate decisions},
  series = {Genetics and molecular research},
  volume    = {15},
  journal   = {Genetics and molecular research},
  publisher = {FUNPEC},
  address   = {Ribeirao Preto},
  issn      = {1676-5680},
  doi       = {10.4238/gmr.15028493},
  pages     = {17},
  year      = {2016},
  abstract  = {Data integration has become a useful strategy for uncovering new insights into complex biological networks. We studied whether this approach can help to delineate the signal transducer and activator of transcription 6 (STAT6)-mediated transcriptional network driving T helper (Th) 2 cell fate decisions. To this end, we performed an integrative analysis of publicly available RNA-seq data of Stat6-knockout mouse studies together with STAT6 ChIP-seq data and our own gene expression time series data during Th2 cell differentiation. We focused on transcription factors (TFs), cytokines, and cytokine receptors and delineated 59 positively and 41 negatively STAT6-regulated genes, which were used to construct a transcriptional network around STAT6. The network illustrates that important and well-known TFs for Th2 cell differentiation are positively regulated by STAT6 and act either as activators for Th2 cells (e.g., Gata3, Atf3, Satb1, Nfil3, Maf, and Pparg) or as suppressors for other Th cell subpopulations such as Th1 (e.g., Ar), Th17 (e.g., Etv6), or iTreg (e.g., Stat3 and Hifla) cells. Moreover, our approach reveals 11 TFs (e.g., Atf5, Creb3l2, and Asb2) with unknown functions in Th cell differentiation. This fact together with the observed enrichment of asthma risk genes among those regulated by STAT6 underlines the potential value of the data integration strategy used here. Thus, our results clearly support the opinion that data integration is a useful tool to delineate complex physiological processes.},
  language  = {en}
}
@article{EdlichMuthMurayaAltmannetal.2016,
  author    = {Edlich-Muth, Christian and Muraya, Moses M. and Altmann, Thomas and Selbig, Joachim},
  title     = {Phenomic prediction of maize hybrids},
  series = {Biosystems : journal of biological and information processing sciences},
  volume    = {146},
  journal   = {Biosystems : journal of biological and information processing sciences},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0303-2647},
  doi       = {10.1016/j.biosystems.2016.05.008},
  pages     = {102 -- 109},
  year      = {2016},
  abstract  = {Phenomic experiments are carried out in large-scale plant phenotyping facilities that acquire a large number of pictures of hundreds of plants simultaneously. With the aid of automated image processing, the data are converted into genotype-feature matrices that cover many consecutive days of development. Here, we explore the possibility of predicting the biomass of the fully grown plant from early developmental stage image-derived features. We performed phenomic experiments on 195 inbred and 382 hybrid maizes varieties and followed their progress from 16 days after sowing (DAS) to 48 DAS with 129 image-derived features. By applying sparse regression methods, we show that 73\% of the variance in hybrid fresh weight of fully-grown plants is explained by about 20 features at the three-leaf-stage or earlier. Dry weight prediction explained over 90\% of the variance. When phenomic features of parental inbred lines were used as predictors of hybrid biomass, the proportion of variance explained was 42 and 45\%, for fresh weight and dry weight models consisting of 35 and 36 features, respectively. These models were very robust, showing only a small amount of variation in performance over the time scale of the experiment. We also examined mid-parent heterosis in phenomic features. Feature heterosis displayed a large degree of variance which resulted in prediction performance that was less robust than models of either parental or hybrid predictors. Our results show that phenomic prediction is a viable alternative to genomic and metabolic prediction of hybrid performance. In particular, the utility of early-stage parental lines is very encouraging. (C) 2016 Elsevier Ireland Ltd. All rights reserved.},
  language  = {en}
}
@misc{RajasundaramSelbig2016,
  author    = {Rajasundaram, Dhivyaa and Selbig, Joachim},
  title     = {analysis},
  series = {Current opinion in plant biology},
  volume    = {30},
  journal   = {Current opinion in plant biology},
  publisher = {Elsevier},
  address   = {London},
  issn      = {1369-5266},
  doi       = {10.1016/j.pbi.2015.12.010},
  pages     = {57 -- 61},
  year      = {2016},
  abstract  = {The development of 'omics' technologies has progressed to address complex biological questions that underlie various plant functions thereby producing copious amounts of data. The need to assimilate large amounts of data into biologically meaningful interpretations has necessitated the development of statistical methods to integrate multidimensional information. Throughout this review, we provide examples of recent outcomes of 'omics' data integration together with an overview of available statistical methods and tools.},
  language  = {en}
}