Schulz Lab

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Integrative Methods for the Analysis of Gene Regulation

Integrative Machine Learning approaches for gene regulation

Despite the vast amount of research in that area, the regulation of genes is still not fully understood. In particular the interplay between regulators that modulate the transcriptional activation of a gene and post-transcriptional regulators that modulate the abundance of a gene’s product, the mRNA, is neglected in most system biology studies. With the availability of numerous complete epigenomics datasets our vision is to produce a comprehensive computational catalogue of gene regulation for each gene in the human genome, including transcriptional and post-transcriptional regulators, at much higher detail as is currently available.

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Prediction of ncRNA function

MicroRNAs (miRNAs) are small non-coding RNAs which a play critical role in a wide range of biological processes, via post-transcriptional gene regulation. Identifying miRNA targets is a critical step toward elucidating their functions in different diseases. In recent years, several computational methods based on miRNA-mRNA sequence complementarity information have been developed. However the expected false positive rate of sequence based predictions is still large. In addition many target relationships are context-specific. Therefore, most approaches incorporate miRNA-mRNA expression levels to improve prediction accuracy.

Next generation RNA-sequencing (RNA-seq) extends the possibilities of transcriptome profiling to quantitative analysis of expression levels of genes and their transcript isoforms. We use approaches from Machine Learning for inferring miRNA-mRNA interaction networks in cancer using gene and also transcript expression levels. Learning the regulation of miRNAs for individual transcripts has the advantage that the effect of a miRNA on the direct precursors of a protein can be estimated, which is ambiguous on the level of transcripts if these are summarized as one gene expression level.

Machine Learning approaches to learn context-specific miRNA-transcript interactions

Reconstruction of dynamic regulatory networks is a challenging task in Computational Systems Biology. Current models of gene regulatory networks are often constructed as a static snapshot of the regulatory wiring in cells. We are working on methods that can dynamically rewire the network connections modeling transcriptional and posttranscriptional factors through the integration of binding data (e.g. Chip-Seq) and gene expression data. In addition, we are enhancing these methods to utilize transcript expression level measurements with RNA-Seq to improve the resolution for reconstruction of dynamic regulatory networks.

dynamic networks

D Gérard, F Schmidt, A Ginolhac, M Schmitz, R Halder, P Ebert, MH Schulz, T Sauter, L Sinkkonen
Temporal epigenomic profiling identifies AHR and GLIS1 as super-enhancer controlled regulators of mesenchymal multipotency,
Nucleic Acids Research full text

MH Schulz, KV Pandit, CL Lino Cardenas, N Ambalavanan, N Kaminski and Z Bar-Joseph
Reconstructing dynamic microRNA-regulated interaction networks
PNAS 2013 [full text]

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Relating Histone Modifications to Regulation of Gene Expression

The regulation of residues on histone proteins, the elements of the nucleosome, has been shown to be connected to the regulation of gene transcription. Many studies have shown that the abundance of the core histone modifications, such as H3K4me3, H3K27me and H3K27ac, along a gene promoter are predictive of the gene’s expression level. We are interested in performing a detailed mapping of functional histone modification.

We study machine learning methods that enable us to link the abundance of modifications along a gene region, e.g., the promoter, to the expression of the gene. A particular interest is in understanding the regulation of sense / antisense promoters.

histone example figure