|Organized by:||Faculty of Technology, GRK 1906 DiDy|
|Place:||Bielefeld University, main building, room V2-105/115|
|Date:||November 20-22, 2017|
Monday, November 20th
|9h30||Jochen Kruppa||The interactive visualization of k-mer distributions in virus and bacteria genome sequences to reveal specific genetic regions|
|13h00||Susanne Gerber||Big Data integration and trans-Omics analysis for reconstructing relevant pathways and networks underlying neurodegenerative diseases|
|15h30||Marcel Schulz||Data science approaches for learning gene regulatory networks|
Tuesday, November 21st
| || ||
|14h00||Tobias Marschall||Towards haplotype-resolved genome assembly – or how to solve multiple jigsaw puzzles simultaneously|
Wednesday, November 22nd
|9h30||Stephan Schiffels||Unlocking human history - Computational methods for demographic inference from genome sequences|
|14h00||Alexander Schönhuth||Genome Data Science|
The interactive visualization of k-mer distributions in virus and bacteria genome sequences to reveal specific genetic regions
by Jochen Kruppa
Bioinformatics methods often incorporate the frequency distribution of nulecobases or k-mers in DNA or RNA sequences, for example as part of metagenomic or phylogenetic analysis. Because the frequency matrix, with sequences in the rows and nucleobases in the columns, is multi-dimensional and therefore hard to visualize. Here, we present the R-package 'kmerPyramid' that allows to display each sequence, based on its nucleobase or k-mer distribution projected to the space of principal components, as a point within a 3-dimensional, interactive pyramid (Kruppa et al., 2017). Using the computer mouse, the user can turn the pyramid's axes, zoom in and out and identify individual points. Additionally, the package provides the related frequency distribution matrices of about 2.000 bacteria and 5.000 viruses, respectively, calculated from NCBI GenBank. The 'kmerPyramid' can particularly be used for intra- and inter species comparisons. We show the application of clustering genetic regions, like coding and non-coding DNA sequences, the visualization of genomic islands in bacteria genomes, and the detection of low complexity regions in a genome. We are also able to visualize the direct comparison of two sequences considering higher k-mers. This feature might be a guidance for later motif search. The kmerPyramid is based on principal component analysis (PCA) that is used to project the multi-dimensional matrix of nucleobase and k-mer frequencies in the 3-dimensional space. PCA, as a method for dimension reduction, has already been demonstrated to preserve relevant information when exploring these frequencies (Dodsworth et al., 2013; Podar et al., 2013; Imelfort et al., 2014). The kmerPyramid package is available on GitHub (https://github.com/jkruppa/kmerPyramid).
Big Data integration and trans-Omics analysis for reconstructing relevant pathways and networks underlying neurodegenerative diseases
by Susanne Gerber
Risks and costs of neurodegenerative diseases constantly grow as the average expected age of humans increases. Due to population ageing - and according to the estimates of the WHO - the current net costs of 160 billions USD worldwide for such diseases like Alzheimer's disease will almost double during the next ten years. However, despite decades of research and despite of the considerable progress achieved in the identification of risk genes, relevant epigenetic modifications, potent biomarkers, environmental/latent risk factors, and dozens of disease-associated Single Nucleotide Polymorphisms (SNPs), the key conditional factors for an outbreak of several neurodegenerative diseases, e.g Alzheimer’s disease (AD) are still unknown. Also, the question whether there are commonalities in the various (patho)physiological processes associated with neurodegeneration remains yet unanswered. Practical implication of this lacking progress in research is the fact that the current medication for Alzheimer is not more efficient now then it was 20 years ago. It became, however, generally accepted that the underlying mechanisms are polyfactorial and depend on multiple (partly unknown) genetic and non-genetic variables, epigenetics and cellular component factors at different scales.
The work of my research group (as well as of other colleagues in the field) will be introduced.
It aims at the point where the huge collections of disease-related data on various levels (involving Genomics-, Epigenomics-, Transcriptomics- and Proteomics data layers) have to be integrated – and subject to advanced computational and statistical methods on high-performance computing facilities.
By making use of multi-omic measurements data – combined with co-designing new more advanced computational data integration methods for supercomputing facilities, the aim of this research is to reconstruct the global biochemical networks across multiple omic layers – and even across different diseases.
Data science approaches for learning gene regulatory networks
by Marcel Schulz
Deciphering the gene regulatory mechanisms that control the establishment and maintenance of cellular programs is an essential task in computational biology and systems medicine. Over the last years a number of key technologies have been developed to measure genes and their surrounding epigenomic environment in great detail. However the integration of these different Omics data types poses a number of statistical challenges.
In this talk I will highlight some of our recent work to improve the estimation of regulatory networks from epigenomics data. I will present our mathematical approach for prediction of transcription factor regulation from paired gene expression and epigenomics time series data using hidden Markov models. In addition, I will introduce a new statistical method for the inference of competing endogenous RNA interaction networks from expression data and will show an application for the prediction of prognostic cancer biomarkers using these networks.
Statistical Models of post-transcriptional gene regulation
by Annalisa Marsico
RNA Binding proteins (RBPs) and non-coding RNAs function in coordination with each other to control post-transcriptional regulation (PTR). In human cells, hundreds of RBPs and thousands of non-coding RNAs have been annotated but the detailed functions of only a few have been explored so far. There is therefore a huge need of in silico methods to assist this task starting from the modeling of recent high-throughput data, such as CLIP-seq data, to shed lights on mechanisms of PTR I will present some of the machine learning methods developed in our lab to determine the precise location of RBP binding sites (Krakau et al., bioarxiv 2017), characterize their RNA sequence-structure preferences (Heller et al., NAR 2017) and predict long non-coding RNA functions and mechanisms of action. I will also demonstrate how the characterization of RBPs, lncRNAs and their interactions is the first step to better understand diseases associated with changes in PTR by presenting an application of our tools to the identification and characterization of biomarkers from immune response experimental data.
Towards haplotype-resolved genome assembly – or how to solve multiple jigsaw puzzles simultaneously
by Tobias Marschall
Genome assembly is like a one-dimensional jigsaw puzzle: Given many short sequence fragments, we are tasked to reconstruct the sequence corresponding to the whole genome. This classic bioinformatical problem has been studied for decades and, yet, very pressing and fundamental challenges remain unsolved. First, many species of interest (including humans) are diploid or even polyploid and hence harbor multiple similar yet distinct copies of each chromosome (called haplotypes). Second, state-of-the-art assembly projects routinely employ multiple technologies, which produce massive amounts of data and come with technology-specific errors and uncertainties. Haplotype-resolved genome assembly using data from multiple technologies is hence a significant data integration task. In my presentation, I highlight recent progress on multiple statistical and algorithmic methods that serve as building blocks towards this goal. Furthermore, I sketch a way forward for solving this problem in the mid-term future.
Unlocking human history - Computational methods for demographic inference from genome sequences
by Stephan Schiffels
In recent years, the number of publicly available human genomes from diverse populations has increased by several orders of magnitude. In particular in conjunction with ancient DNA, these large data sets present an opportunity for population genetic research to investigate our human past with unprecedented detail. Here I will present several studies that showcase how to exploit this data with new high-resolution methods. In particular, I will introduce MSMC, a method based on Coalescence Hidden Markov Models, and rarecoal, a method to efficiently model the rare joint site frequency spectrum across multiple populations. The results of these studies cover new understandings on deep human history ~50,000 years ago, the peopling of the Americas ~15,000 years ago, all the way to the early medieval Anglo-Saxon migrations into England. Building upon these developments, I will point out future directions for genetic data analyses in the era of population-scale ancient and modern sequencing data sets, as they are increasingly available today.
Genome Data Science
by Alexander Schönhuth
Die modernen Sequenziertechnologien haben die Biologie, und insbesondere die Genomik mit sintflutartigen Datenmengen konfrontiert. Die Konsequenzen sind gewaltig, nicht nur in Hinsicht auf die sich dadurch ergebenden Chancen in punkto Lebensdauer und -qualität, sondern auch hinsichtlich der der Data Science zuzurechnenden Herausforderungen. In meinem Vortrag werde ich zwei gegenwärtig dominante Themenkreise ansprechen.
Zum Ersten werde ich besprechen, wie man Cliquen in Genom-Assembly-Graphen zügig enumerieren kann, um diese dann dazu benutzen, um Virusgenome zu rekonstruieren. Diese Vorgehensweise der Rekonstruktion von Virusgenomen ist neu. Die Ergebnisse zeigen, dass dieser Data-Mining-orientierte, streng datenbezogene Ansatz entscheidende Vorteile im Abgleich mit (weniger datenbezogenen) State-of-the-Art-Methoden hat.
Zweitens werde ich besprechen, wie man DNA-Sequenz – und auch Sequenz im Allgemeinen – mit Hilfe von Hilbert-Kurven repräsentieren kann, um sie mit Deep Convolutional Neural Networks zu klassifizieren. Convolutional Neural Networks haben in letzter Zeit insbesondere in der Bildanalyse grosse Erfolge gefeiert. Die Idee ist, solche Erfolge in der DNA-Sequenzanalyse zu reproduzieren. Hilbert-Kurven haben aufgrund ihrer charakterisierenden Eigenschaften das Potenzial, Sequenz in Bilder zu verwandeln, so dass die Stärken der Konvolution optimal ausgenutzt werden, was sich in den entsprechenden Ergebnissen niederschlägt.