TY  - JOUR
A1  - Hill, Natascha
A1  - Leow, Alexander
A1  - Bleidorn, Christoph
A1  - Groth, Detlef
A1  - Tiedemann, Ralph
A1  - Selbig, Joachim
A1  - Hartmann, Stefanie
T1  - Analysis of phylogenetic signal in protostomial intron patterns using Mutual Information
JF  - Theory in biosciences
N2  - Many deep evolutionary divergences still remain unresolved, such as those among major taxa of the Lophotrochozoa. As alternative phylogenetic markers, the intron-exon structure of eukaryotic genomes and the patterns of absence and presence of spliceosomal introns appear to be promising. However, given the potential homoplasy of intron presence, the phylogenetic analysis of this data using standard evolutionary approaches has remained a challenge. Here, we used Mutual Information (MI) to estimate the phylogeny of Protostomia using gene structure data, and we compared these results with those obtained with Dollo Parsimony. Using full genome sequences from nine Metazoa, we identified 447 groups of orthologous sequences with 21,732 introns in 4,870 unique intron positions. We determined the shared absence and presence of introns in the corresponding sequence alignments and have made this data available in "IntronBase", a web-accessible and downloadable SQLite database. Our results obtained using Dollo Parsimony are obviously misled through systematic errors that arise from multiple intron loss events, but extensive filtering of data improved the quality of the estimated phylogenies. Mutual Information, in contrast, performs better with larger datasets, but at the same time it requires a complete data set, which is difficult to obtain for orthologs from a large number of taxa. Nevertheless, Mutual Information-based distances proved to be useful in analyzing this kind of data, also because the estimation of MI-based distances is independent of evolutionary models and therefore no pre-definitions of ancestral and derived character states are necessary.
KW  - Mutual Information
KW  - Evolution
KW  - Gene structure
Y1  - 2013
U6  - https://doi.org/10.1007/s12064-012-0173-0
SN  - 1431-7613
VL  - 132
IS  - 2
SP  - 93
EP  - 104
PB  - Springer
CY  - New York
ER  - 
TY  - JOUR
A1  - Sulpice, Ronan
A1  - Nikoloski, Zoran
A1  - Tschoep, Hendrik
A1  - Antonio, Carla
A1  - Kleessen, Sabrina
A1  - Larhlimi, Abdelhalim
A1  - Selbig, Joachim
A1  - Ishihara, Hirofumi
A1  - Gibon, Yves
A1  - Fernie, Alisdair
A1  - Stitt, Mark
T1  - Impact of the Carbon and Nitrogen Supply on Relationships and Connectivity between Metabolism and Biomass in a Broad Panel of Arabidopsis Accessions(1[W][OA])
JF  - Plant physiology : an international journal devoted to physiology, biochemistry, cellular and molecular biology, biophysics and environmental biology of plants
N2  - Natural genetic diversity provides a powerful tool to study the complex interrelationship between metabolism and growth. Profiling of metabolic traits combined with network-based and statistical analyses allow the comparison of conditions and identification of sets of traits that predict biomass. However, it often remains unclear why a particular set of metabolites is linked with biomass and to what extent the predictive model is applicable beyond a particular growth condition. A panel of 97 genetically diverse Arabidopsis (Arabidopsis thaliana) accessions was grown in near-optimal carbon and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and 54 metabolic traits. Correlation-based metabolic networks were generated from the genotype-dependent variation in each condition to reveal sets of metabolites that show coordinated changes across accessions. The networks were largely specific for a single growth condition. Partial least squares regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range of 0.27-0.58 and 0.21-0.51 and P values in the range of <0.001-<0.13 and <0.001-<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the partial least squares regression were mainly condition specific and often related to the resource that restricts growth under that condition. Linear mixed-model analysis using the combined metabolic traits from all growth conditions as an input indicated that inclusion of random effects for the conditions improves predictions of biomass. Thus, robust prediction of biomass across a range of conditions requires condition-specific measurement of metabolic traits to take account of environment-dependent changes of the underlying networks.
Y1  - 2013
U6  - https://doi.org/10.1104/pp.112.210104
SN  - 0032-0889
SN  - 1532-2548
VL  - 162
IS  - 1
SP  - 347
EP  - 363
PB  - American Society of Plant Physiologists
CY  - Rockville
ER  -