2013

A Palaeolithic-type diet causes strong tissue-specific effects on ectopic fat deposition in obese postmenopausal women
Ryberg, M., Sandberg S., Mellberg C., Stegle O., Lindahl B., Larsson C., Hauksson J., and Olsson T.
Journal of Internal Medicine. (2013),
Detecting regulatory gene–environment interactions with unmeasured environmental factors
Fusi, Nicoló, Lippert Christoph, Borgwardt Karsten, Lawrence Neil D., and Stegle Oliver
Bioinformatics. (2013),
A Lasso multi-marker mixed model for association mapping with population structure correction
Rakitsch, B., Lippert C., Stegle O., and Borgwardt K.
Bioinformatics. Volume 29, Number 2, (2013), p.206–214
Accurate detection of differential RNA processing
Drewe, Philipp, Stegle Oliver, Hartmann Lisa, Kahles André, Bohnert Regina, Wachter Andreas, Borgwardt Karsten, and Rätsch Gunnar
Nucleic acids research. (2013),
Transcriptome and genome sequencing uncovers functional variation in humans
Lappalainen, Tuuli, Sammeth Michael, Friedländer Marc R., Hoen Peter A. C. ‘t, Monlong Jean, Rivas Manuel A., Gonzàlez-Porta Mar, Kurbatova Natalja, Griebel Thasso, Ferreira Pedro G., et al.
Nature. (2013),
Genotype-Environment Interactions Reveal Causal Pathways That Mediate Genetic Effects on Phenotype
Gagneur, Julien, Stegle Oliver, Zhu Chenchen, Jakob Petra, Tekkedil Manu M., Aiyar Raeka S., Schuon Ann-Kathrin, Pe'er Dana, and Steinmetz Lars M.
PLOS Genetics. Volume 9, Number 9, (2013), p.e1003803

2012

Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies
Fusi, N., Stegle O., and Lawrence N. D.
PLoS computational biology. Volume 8, Number 1, (2012), p.e1002330
ShapePheno: unsupervised extraction of shape phenotypes from biological image collections
Karaletsos, T., Stegle O., Dreyer C., Winn J., and Borgwardt K. M.
Bioinformatics. Volume 28, Number 7, (2012), p.1001–1008
Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses
Stegle, O., Parts L., Piipari M., Winn J., and Durbin R.
Nature protocols. Volume 7, Number 3, (2012), p.500–507
Patterns of cis regulatory variation in diverse human populations
Stranger, B. E., Montgomery S. B., Dimas A. S., Parts L., Stegle O., Ingle C. E., Sekowska M., Smith G. D., Evans D., Gutierrez-Arcelus M., et al.
PLoS genetics. Volume 8, Number 4, (2012), p.e1002639
Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis
Windram, O., Madhou P., McHattie S., Hill C., Hickman R., Cooke E., Jenkins D. J., Penfold C. A., Baxter L., Breeze E., et al.
The Plant Cell Online. Volume 24, Number 9, (2012), p.3530–3557

2011

Whole-genome sequencing of multiple Arabidopsis thaliana populations
Cao, J., Schneeberger K., Ossowski S., Günther T., Bender S., Fitz J., Koenig D., Lanz C., Stegle O., Lippert C., et al.
Nature Genetics. Volume 43, Number 10, (2011), p.956–963
Efficient branch-and-bound techniques for two-locus association mapping
Klotzbücher, K., Kobayashi Y., Shervashidze N., Stegle O., Müller-Myhsok B., Weigel D., and Borgwardt K.
BMC Bioinformatics. Volume 12, Number Suppl 11, (2011), p.A3
Joint genetic analysis of gene expression data with inferred cellular phenotypes
Parts, L., Stegle O., Winn J., and Durbin R.
PLoS genetics. Volume 7, Number 1, (2011), p.e1001276
Detecting low-complexity unobserved causes
Janzing, D., Sgouritsa E., Stegle O., Peters J., and Schölkopf B.
Uncertainty in Artificial Intelligence. 2011, (2011), doi:
Efficient inference in matrix-variate Gaussian models with iid observation noise
Stegle, O., Lippert C., Mooij J., Lawrence N., and Borgwardt K.
Advances in Neural Information processing Systems. 2011, (2011), doi:
Spontaneous epigenetic variation in the Arabidopsis thaliana methylome
Becker, C., Hagmann J., Müller J., Koenig D., Stegle O., Borgwardt K., and Weigel D.
Nature. (2011),
Multiple reference genomes and transcriptomes for Arabidopsis thaliana
Gan, Xiangchao, Stegle Oliver, Behr Jonas, Steffen Joshua G., Drewe Philipp, Hildebrand Katie L., Lyngsoe Rune, Schultheiss Sebastian J., Osborne Edward J., Sreedharan Vipin T., et al.
Nature. Volume 477, Number 7365, (2011), p.419–423

2010

A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series
Stegle, O., Denby K. J., Cooke E. J., Wild D. L., Ghahramani Z., and Borgwardt K. M.
Journal of Computational Biology. Volume 17, Number 3, (2010), p.355–367
Probabilistic latent variable models for distinguishing between cause and effect
Mooij, J., Stegle O., Janzing D., Zhang K., and Schölkopf B.
(2010),
A toolbox for predicting G-quadruplex formation and stability
Wong, H. M., Stegle O., Rodgers S., Huppert J. L., and others
J. Nucleic Acids. (2010),
A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies
Stegle, O., Parts L., Durbin R., and Winn J.
PLoS computational biology. Volume 6, Number 5, (2010), p.e1000770

2009

Predicting and understanding the stability of G-quadruplexes
Stegle, O., Payet L., Mergny J. L., MacKay D. J. C., and Huppert J. L.
Bioinformatics. Volume 25, Number 12, (2009), p.i374
Inference algorithms and learning theory for Bayesian sparse factor analysis
Rattray, M., Stegle O., Sharp K., and Winn J.
Journal of Physics: Conference Series. Volume 197, (2009), p.012002
Discovering temporal patterns of differential gene expression in microarray time series
Stegle, O., Denby K., McHattie S., Meade S., Wild D. L., Ghahramani Z., and Borgwardt K.
German Conference on Bioinformatics. (2009), doi:

2008

Gaussian process product models for nonparametric nonstationarity
Adams, R. P., and Stegle O.
Proceedings of the 25th international conference on Machine learning. (2008), p.1–8
Gaussian process robust regression for noisy heart rate data
Stegle, O., Fallert S. V., MacKay DJ, and Brage S.
Biomedical Engineering, IEEE Transactions on. Volume 55, Number 9, (2008), p.2143–2151
Accounting for non-genetic factors improves the power of eQTL studies
Stegle, O., Kannan A., Durbin R., and Winn J.
Research in Computational Molecular Biology. (2008), p.411–422