The majority of cells in our body contain the same genomic information. Nevertheless, an organism consists of a variety of cell types that differ in shape and function. This variability can be explained with the central dogma of molecular biology: genes are processed to functional proteins and RNAs via a multi-step process that is highly regulated by a multitude of general and cell-type specific factors. An example for the complexity of gene regulation is embryogenesis, in which regulatory programs orchestrate the development of a functional organism from a single cell. Another example is the formation of genetic diseases like cancer. They can develop by a chain of mutations that are not only located within genes, but also in regulatory parts of the genome. These mutations can affect a variety of regulatory factors, which can alter the activity of genes. Indeed, the mechanisms that regulate gene activity are not fully uncovered yet.
Recent advances in high-throughput technologies and experimental techniques enhanced the possiblities to study gene regulation. However, the resutling experimental data is often complex and high-dimensional. This highlights the necessity of special purpose bioinformatics methods (e.g. in the area of machine learning and statics) that are suitable for analyzing and visualizing the biological data.
This seminar covers a selection of novel and impactful methods in this field. It will be held as a block seminar the week before the lectures start.
In order to participate in the seminar you are required to attend the first meeting.
There the topics are distributed. No prior reservation is possible.
Due to the corona pandemic, the first meeting will be held via Zoom. If you like to participate, please register via mail (teaching@bioinf.uni-sb.de).
After you received a topic, please confirm your participation in the seminar until 2021/02/01 via mail to teaching@bioinf.uni-sb.de.
Also, you have to register officially in HISPOS until 2021/02/15.
# | Paper | Autoren | |
---|---|---|---|
1 | Inferring Regulatory Networks from Expression Data Using Tree-Based Methods | Huynh-Thu et al. | |
2 | Model-based Analysis of ChIP-Seq (MACS) | Zhang et al. | |
3 | Detection of active transcription factor binding sites with the combination of DNase hypersensitivity and histone modifications | Gusmao et al. | |
4 | DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning | Ni et al. | |
5 | Salmon provides fast and bias-aware quantification of transcript expression | Patro et al. | |
6 | Predicting transcription factor binding using ensemble random forest models | Ardakani et al. | |
7 | Unsupervised pattern discovery in human chromatin structure through genomic segmentation | Hoffman et al. | |
8 | The Distance Precision Matrix: computing networks from non-linear relationships | Ghanbari et al. | |
9 | Discriminative prediction of mammalian enhancers from DNA sequence. | Lee et al. | |
10 | SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles | Gao et al. | |
11 | Gene regulatory network inference from single-cell data using multivariate information measures | Chan et al. | |
12 | Predicting transcription factor affinities to DNA from a biophysical model. | Roider et al. |