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This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning. As such, the book is ideal for self-learning and also as the basis of a semester-long course for undergraduate and graduate students in molecular biology, bioengineering, genome sciences, or systems biology./a
MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression and play an essential role in phenotype development. The regulation mechanism behind miRNA reveals insight into gene expression and gene regulation. Transcription Start Site(TSS) is the key to studying gene expression. However, the TSSs of miRNAs can be thousands of nucleotides away from the precursor miRNAs, which makes it hard to be detected by conventional RNA-Seq experiments. Some previous methods tried to take advantage of sequencing data using sequence features or integrated epigenetic markers, but resulted in either not condition-specific or low-resolution prediction. Furthermore, the availability of a large amount of Single-Cell RNA-Seq(scRNA-Seq) data provides remarkable opportunities for studying gene regulatory mechanisms at single-cell resolution. Incorporating the gene regulatory mechanisms can assist with cell type identification and state discovery from scRNA-Seq data. In this dissertation, we studied computational modeling of gene transcription initialization and expression, including two novel approaches to identify TSSs with various type of conditions and one case study at the single-cell level. Firstly, we studied how TSS can be identified based on Cap Analysis Gene Expression (CAGE) experiments data using the thriving Deep Learning Neural Network. We used a control model to study the Deepbind binding score features that the protein binding motif model can improve overall prediction performance. Furthermore, comparing data from unseen cell lines showed better performance than existing tools. Secondly, to better predict the TSSs of miRNA in a condition-specific manner, we built D-miRT, a two-steam convolutional neural network based on integrated low-resolution epigenetic features and high-resolution sequence features. D-miRT outperformed all baseline models and demonstrated high accuracy for miRNA TSS prediction tasks. Compared with the most recent approaches on cell-specific miRNA TSS identification using cell lines that were unseen to the model training processes, D-miRT also showed superior performance. Thirdly, to study gene transcription initialization and regulation from single-cell perspective, we developed INSISTC, an unsupervised machine learning-based approach that incorporated network structure information for single-cell type classification. In contrast to other clustering algorithms, we showed that INSISTC with the SC3 algorithm provides cluster number estimation. Future studies on gene expression and regulation will benefit from INSISTC's adaptability with regard to the kinds of biological networks that can be used.
Transcription regulation is a complex process that can be considered and investigated from different perspectives. Traditionally and due to technical reasons (including the evolution of our understanding of the underlying processes) the main focus of the research was made on the regulation of expression through transcription factors (TFs), the proteins directly binding to DNA. On the other hand, intensive research is going on in the field of chromatin structure, remodeling and its involvement in the regulation. Whatever direction we select, we can speak about several levels of regulation. For instance, concentrating on TFs, we should consider multiple regulatory layers, starting with signaling pathways and ending up with the TF binding sites in the promoters and other regulatory regions. However, it is obvious that the TF regulation, also including the upstream processes, represents a modest portion of all processes leading to gene expression. For more comprehensive description of the gene regulation, we need a systematic and holistic view, which brings us to the importance of systems biology approaches. Advances in methodology, especially in high-throughput methods, result in an ever-growing mass of data, which in many cases is still waiting for appropriate consideration. Moreover, the accumulation of data is going faster than the development of algorithms for their systematic evaluation. Data and methods integration is indispensable for the acquiring a systematic as well as a systemic view. In addition to the huge amount of molecular or genetic components of a biological system, the even larger number of their interactions constitutes the enormous complexity of processes occurring in a living cell (organ, organism). In systems biology, these interactions are represented by networks. Transcriptional or, more generally, gene regulatory networks are being generated from experimental ChIPseq data, by reverse engineering from transcriptomics data, or from computational predictions of transcription factor (TF) – target gene relations. While transcriptional networks are now available for many biological systems, mathematical models to simulate their dynamic behavior have been successfully developed for metabolic and, to some extent, for signaling networks, but relatively rarely for gene regulatory networks. Systems biology approaches provide new perspectives that raise new questions. Some of them address methodological problems, others arise from the newly obtained understanding of the data. These open questions and problems are also a subject of this Research Topic.
(Cont.) We next present a biophysically motivated framework for modeling protein-DNA interactions and show how it leads to very natural algorithms for analyzing the binding specificity of an immunoprecipitated protein, and jointly analyzing protein localization data for multiple regulators or multiple conditions. Finally, we present an analysis of transcriptional coregulator binding in a variety of mouse tissues and a method for predicting which proteins form complexes with the coregulator based purely on the sequence of the regions it binds. We detail a simple but powerful model relating regulator binding to gene expression, and show how the position of regulatory regions is of crucial importance for predicting the expression level of nearby genes.
Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
The advances in biotechnology such as the next generation sequencing technologies are occurring at breathtaking speed. Advances and breakthroughs give competitive advantages to those who are prepared. However, the driving force behind the positive competition is not only limited to the technological advancement, but also to the companion data analy
Gene regulatory networks dynamically control the expression levels of all the genes, and are the keys in explaining various phenotypes and biological processes. The advance of high-throughput measurement technology, such as microarray and next-generation sequencing, enabled us to globally scrutinize various cell properties related to gene regulation and build statistical models to make quantitative predictions. The evolutionary process has left all kinds of traces in the current biological systems. The study of the evolution of gene regulatory networks in comparable cell types across species is an efficient method to unravel such evolutionary traces and help us to better understand the regulatory mechanism. The two main themes of my research are: analysing various "omics" data in the evolutionary context to identify conservation and changes in gene regulatory networks; and building computational models to incorporate different "omics" data for the annotation of genomes and prediction of evolution in gene regulation. The second chapter of my thesis described a computational algorithm for de novo prediction of transcription factor binding site motifs in multiple species. The algorithm, named "GibbsModule", uses three information sources to improve the prediction power, which are 1)co-expressed genes sharing the same set of motifs; 2)binding sites co-localizing to form modules; and 3)the conservation for the use of motifs across species. We developed a Gibbs sampling procedure to incorporate the three information sources. GibbsModule out-performed the existing algorithms on several synthetic and real datasets. When applied to study the binding regions of KLF in embryonic stem cells, GibbsModule discovered a new functional motif. We also used ChIP followed by qPCR to demonstrate that the binding affinity of GibbsModule predicted binding sites are stronger than non-predicted motifs. Both genome sequence and gene expression carry information about gene regulation. Therefore, we can learn more about gene regulatory networks by jointly analysing sequence and expression data. In the third chapter of my thesis, we first introduced a comparative study of the pre-implantation process of embryos in three mammalian species: human, mouse, and cow. We measured time course expression profiles of the embryos during the early development, and analysed them together with genome sequence data and ChIP-seq data. We observed a large portion of changed homologous gene expression, suggesting a prevalent rewiring of gene regulation. We associated the changes of gene expression with different types of cis-changes on the genome sequences. Especially, we found about 10% of species specific transposons are carrying multiple functional binding sites, which are likely to explain the evolution of gene expression. The second part of this chapter presented a phylogenetic model that incorporated the change of motif use and gene expression to infer the rewiring of gene regulatory networks. Epi-genetic modifications, including histone modifications and DNA methylation, are known to be associated with gene regulation. In chapter four, we studied the evolution of epi-genomes in pluripotent stem cells of human, mice, and pigs. We observed the conservation of epi-genomes in different categories of genomic regions. We found the evidence of positive and negative selections on the evolution of epi-genomes. Using linear regression models, the evolution of epi-genomes can largely explain the evolution of gene expression. In the second part of this chapter, we introduced a statistical model to describe the evolution of genomes considering both the DNA sequences and epi-genetic modifications. Based on the evolutionary model, we improved the current alignment algorithm with the information of epi-genetic modification distributions.