Important! The first meeting on 2020/07/03/ will be held only as Zoom-meeting. If you want to participate in the seminar, please write us an e-mail, so we can send you the Zoom invitation link.
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 registration or reservation is possible.
Please confirm your participation in the seminar until 2020/07/10 via mail to teaching@bioinf.uni-sb.de.
Also, you have to register in HISPOS until 2020/07/24.
| # | Paper | Autoren | |
|---|---|---|---|
| 1 | Detection of active transcription factor binding sites with the combination of DNase hypersensitivity and histone modifications | Gusmao et al. | |
| 2 | Salmon provides fast and bias-aware quantification of transcript expression | Patro et al. | |
| 3 | Broad-Enrich: functional interpretation of large sets of broad genomic regions. | Cavalcante et al. | |
| 4 | Effective normalization for copy number variation in Hi-C data. | Servant et al. | |
| 5 | Integrating Diverse Datasets Improves Developmental Enhancer Prediction | Genevieve et al. | |
| 6 | Predicting transcription factor binding using ensemble random forest models | Ardakani et al. | |
| 7 | iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition | Liu et al. | |
| 8 | GenoGAM: genome-wide generalized additive models for ChIP-Seq analysis. | Stricker et al. | |
| 9 | Combinatorial ensemble miRNA target prediction of co-regulation networks with non-prediction data. | Davis et al. | |
| 10 | DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning | Ni et al. |