Computational models of gene expression regulation

Throughout the last several decades, many efforts have been put into elucidating the genetic or epigenetic defects that result in various diseases. Gene regulation, i.e., the process of how genes are turned on and off in the right place and at the right time, is a paramount and prevailing question for researchers. Thanks to the discoveries made by researchers in this field, our understanding of interactions between proteins and DNA or proteins with themselves, as well as the dynamics of chromatin structure under different conditions, have substantially advanced. Even though there has been a lot achieved through these discoveries, there are still many unknown aspects about gene regulation. For instance, proteins called transcription factors (TFs) recognize and bind to specific regions of DNA and recruit the transcriptional machinery, which is essential for gene regulation. As there have been more than 2000 TFs identified in the human genome, it is important to study where they bind to or which genes they target. Computational approaches are important, in particular, as the biological experiments are often very expensive and cannot be done for all TFs. In 2016, a competition named DREAM Challenge was held encouraging researchers to develop novel computational tools for predicting the binding sites of several TFs. The first chapter of this thesis describes our machine learning approach to address this challenge within the scope of the competition. Using ensembles of random forest classifiers, we formulated our framework such that it is able to benefit from the tissue specificity inherent in the data leading to better generalization. Also, our models were tailored for spotting cofactors involved in the binding of TFs of interest. Comparing the important TFs that our computational models suggested with protein-protein association networks revealed that the models preferentially select motifs of TFs that are potential interaction partners in those networks. Another important aspect beyond predicting TF binding is to link epigeneomics, such as histone modification (HM) data, with gene expression. We, particularly, concentrated on predicting expression in a subset of genes called bidirectional. Bidirectional genes are referred to as pairs of genes that are located on opposite strands of DNA close to each other. As the sequencing technologies advance, more such bidirectional configurations are being detected. This indicates that in order to understand the gene regulatory mechanisms, it would be beneficial to account for such promoter architectures. In the second and third chapters, we focused on genes having bidirectional promoter architectures utilizing high resolution epigenomic signatures and single cell RNA-seq data to dissect the complex epigenetic architecture at these promoters. Using single-cell RNA-seq data as the estimate of gene expression, we were able to generate a hypothetical model for gene regulation in bidirectional promoters. We showed that bidirectional promoters can be categorized into three architecture types with distinct characteristics. Each of these categories corresponds to a unique gene expression profile at single cell level. The single cell RNA-seq data proved to be a powerful means for studying gene regulation. Therefore, in the last chapter, we proposed a novel approach for predicting gene expression at the single cell level using cis-regulatory motifs as well as epigenetic features. To achieve this, we designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. This allowed us to identify distinct TFs that show cell-type specific regulation in induced pluripotent stem cells. Our approach does not only limit to TFs, rather it can take any type of data that can potentially be used in explaining gene expression at single cell level. We believe that our findings can be used in drug discovery and development that can regulate the presence of TFs or other regulatory factors, which lead the cell fate into abnormal states, to prevent or cure diseases.In den letzten Jahrzehnten wurden große Anstrengungen unternommen, um die genetischen oder epigenetischen Defekte aufzuklären, die zu verschiedenen Krankheiten führen. Die Genregulation, d.h. der Prozess der Ein- und Abschaltung der Gene am richtigen Ort und zur richtigen Zeit reguliert, ist für die Forscher eine Frage von zentraler Bedeutung. Dank der Entdeckungen von Forschern auf diesem Gebiet ist unser Verständnis der Wechselwirkungen zwischen zwischen den Proteinen und der DNA oder der Proteine untereinander sowie der Dynamik der Chromatinstruktur unter verschiedenen Bedingungen wesentlich fortgeschritten. Obwohl durch diese Entdeckungen viel erreicht wurde, gibt es noch viele unbekannte Aspekte der Genregulation. Beispielsweise erkennen Proteine, sogenannte Transkriptionsfaktoren (Transcription Factors, TFs), bestimmte Bereiche der DNA und binden an diese und rekrutieren die Transkriptionsmaschinerie, die für die Genregulation erforderlich ist. Da mehr als 2000 TFs im menschlichen Genom identifiziert wurden, ist es wichtig zu untersuchen, wo sie binden oder auf welche Gene sie abzielen. Rechnerische Ansätze sind insbesondere wichtig, da die biologischen Experimente oft sehr teuer sind und nicht für alle TFs durchgeführt werden können. Im Jahr 2016 fand ein Wettbewerb namens DREAM Challenge statt, bei dem Forscher aufgefordert wurden, neuartige Rechenwerkzeuge zur Vorhersage der Bindungsstellen mehrerer TFs zu entwickeln. Das erste Kapitel dieser Arbeit beschreibt unseren Ansatz des maschinellen Lernens, um diese Herausforderung im Rahmen des Wettbewerbs anzugehen. Unter Verwendung von Ensembles von Random Forest Klassifikatoren haben wir unser Framework so formuliert, dass es von der Gewebespezifität der Daten profitiert und damit zu einer besseren Generalisierung führt. Außerdem wurden unsere Modelle auf das Erkennen von Kofaktoren angepasst, die an der Bindung von TFs beteiligt sind, die für uns von Interesse sind. Der Vergleich der wichtigen TFs, die unsere Computermodelle mit Protein-Protein-Assoziationsnetzwerken vorschlugen, ergab, dass die Modelle bevorzugt Motive von TFs auswählen, die potenzielle Interaktionspartner in diesen Netzwerken sind. Ein weiterer wichtiger Aspekt, der über die Vorhersage der TF-Bindung hinausgeht, besteht darin, epigeneomische Faktoren wie Histonmodifikationsdaten (HM-Daten) mit der Genexpression zu verknüpfen. Wir konzentrierten uns insbesondere auf die Vorhersage der Expression in einer Untergruppe von Genen, die als bidirektional bezeichnet werden. Bidirektionale Gene werden als Paare von Genen bezeichnet, die sich auf gegenüberliegenden DNA-Strängen befinden und nahe beieinander liegen. Mit dem Fortschritt der Sequenzierungstechnologien werden immer mehr solche bidirektionalen Konfigurationen erkannt. Dies weist darauf hin, dass es zum Verständnis der Genregulationsmechanismen vorteilhaft wäre, solche Promotorarchitekturen zu berücksichtigen. Im zweiten und dritten Kapitel konzentrierten wir uns auf Gene mit bidirektionalen Promotorarchitekturen, um mit Hilfe von epigenomischen Signaturen und Einzelzell-RNA-Sequenzdaten die komplexe epigenetische Architektur an diesen Promotoren zu analysieren. Unter Verwendung von Einzelzell-RNA-Sequenzdaten als Schätzung der Genexpression konnten wir ein hypothetisches Modell für die Genregulation in bidirektionalen Promotoren aufstellen. Wir haben gezeigt, dass bidirektionale Promotoren in drei Architekturtypen mit unterschiedlichen Merkmalen eingeteilt werden können. Jede dieser Kategorien entspricht einem eindeutigen Genexpressionsprofil auf Einzelzellebene. Die Einzelzell-RNA-Sequenzdaten erwiesen sich als leistungsstarkes Mittel zur Untersuchung der Genregulation. Daher haben wir im letzten Kapitel einen neuen Ansatz zur Vorhersage der Genexpression auf Einzelzellebene unter Verwendung von cis-regulatorischen Motiven sowie epigenetischen Merkmalen vorgeschlagen. Um dies zu erreichen, haben wir ein baumgesteuertes Multitasking-Lernsystem entwickelt, das jede Zelle als eine Aufgabe betrachtet. Durch dieses Gerüst konnten wir die Einzelzellgenexpressionswerte entweder mit TF-Bindungsaffinitäten oder mit TF-ChIP-Sequenzdaten erklären, die in bestimmten Genomregionen gemessen wurden. Dies ermöglichte es uns, verschiedene TFs zu identifizieren, die eine zelltypspezifische Regulation in induzierten pluripotenten Stammzellen zeigen. Unser Ansatz beschränkt sich nicht nur auf TFs, sondern kann jede Art von Daten verwenden, die potentiell zur Erklärung der Genexpression auf Einzelzellebene verwendet werden können. Wir glauben, dass unsere Erkenntnisse für die Entdeckung und Entwicklung von Arzneimitteln verwendet werden können, die das Vorhandensein von TFs oder anderen regulatorischen Faktoren regulieren können, die die Zellen abnormal werden lassen, um Krankheiten zu verhindern oder zu heilen

Low Resolution Face Recognition Using Mixture of Experts

Abstract-Human activity is a major concern in a wide variety of applications, such as video surveillance, human computer interface and face image database management. Detecting and recognizing faces is a crucial step in these applications. Furthermore, major advancements and initiatives in security applications in the past years have propelled face recognition technology into the spotlight. The performance of existing face recognition systems declines significantly if the resolution of the face image falls below a certain level. This is especially critical in surveillance imagery where often, due to many reasons, only low-resolution video of faces is available. If these low-resolution images are passed to a face recognition system, the performance is usually unacceptable. Hence, resolution plays a key role in face recognition systems. In this paper we introduce a new low resolution face recognition system based on mixture of expert neural networks. In order to produce the low resolution input images we down-sampled the 48 × 48 ORL images to 12 × 12 ones using the nearest neighbor interpolation method and after that applying the bicubic interpolation method yields enhanced images which is given to the Principal Component Analysis feature extractor system. Comparison with some of the most related methods indicates that the proposed novel model yields excellent recognition rate in low resolution face recognition that is the recognition rate of 100% for the training set and 96.5% for the test set

Comparative study approaches to higher education in graduate section in different countries

Aim: The present study is intended with literature review in universities and experiences in different countries, analyzed approaches to higher education in graduate section. Research Methods: This article is a comparative study which uses a qualitative content analysis with inductive category development studying and comparing of different to higher education in graduate section in different countries such as the USA, Malaysia, and China. Results: Most important factors change in the USA of development and industrial is free market, democracy, advancement of technology, communications, and multiculturalism. Experience China in higher education is reform policies in the graduate section. In Malaysia, Academic Research plays a role in development plans, policymaking and education human resources. Conclusions: Graduate programs in the USA focused on resorting to the latest scientific and research achievements, labor productivity and educating experts and professionals. Policies China include the policy of the free market system, entry democracy in political territory, globalization of activities, humanist changing of industries related to worker to knowledge-based industries, pass of economic development to people-oriented development, and changes societies. The important policies in the graduate section in Malaysia include activities research by awareness of market demands and industry needs. Establish fitness between programs and academic disciplines in graduate section and attracting the best brains

Predicting transcription factor binding using ensemble random forest models [version 2; peer review: 2 approved]

Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs). Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups. Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier built based upon data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal. Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697)

Predicting transcription factor binding using ensemble random forest models [version 1; peer review: 2 approved with reservations]

Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs). Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups. Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier applied to the data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal. Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697

Prediction of single-cell gene expression for transcription factor analysis

BACKGROUND: Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. RESULTS: Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. CONCLUSION: Our proposed method allows us to identify distinct TFs that show cell type–specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate

CVD-associated SNPs with regulatory potential reveal novel non-coding disease genes

Abstract Background Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) appearing in non-coding genomic regions in CVDs. The SNPs may alter gene expression by modifying transcription factor (TF) binding sites and lead to functional consequences in cardiovascular traits or diseases. To understand the underlying molecular mechanisms, it is crucial to identify which variations are involved and how they affect TF binding. Methods The SNEEP (SNP exploration and analysis using epigenomics data) pipeline was used to identify regulatory SNPs, which alter the binding behavior of TFs and link GWAS SNPs to their potential target genes for six CVDs. The human-induced pluripotent stem cells derived cardiomyocytes (hiPSC-CMs), monoculture cardiac organoids (MCOs) and self-organized cardiac organoids (SCOs) were used in the study. Gene expression, cardiomyocyte size and cardiac contractility were assessed. Results By using our integrative computational pipeline, we identified 1905 regulatory SNPs in CVD GWAS data. These were associated with hundreds of genes, half of them non-coding RNAs (ncRNAs), suggesting novel CVD genes. We experimentally tested 40 CVD-associated non-coding RNAs, among them RP11-98F14.11, RPL23AP92, IGBP1P1, and CTD-2383I20.1, which were upregulated in hiPSC-CMs, MCOs and SCOs under hypoxic conditions. Further experiments showed that IGBP1P1 depletion rescued expression of hypertrophic marker genes, reduced hypoxia-induced cardiomyocyte size and improved hypoxia-reduced cardiac contractility in hiPSC-CMs and MCOs. Conclusions IGBP1P1 is a novel ncRNA with key regulatory functions in modulating cardiomyocyte size and cardiac function in our disease models. Our data suggest ncRNA IGBP1P1 as a potential therapeutic target to improve cardiac function in CVDs

Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters

Background: Bidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single-cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single-cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single-cell technologies are not yet established, in the context of BPs. Results: We performed integrative analyses of novel human single-cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles. Conclusions: Our results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data

Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters

Abstract Background Bidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single-cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single-cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single-cell technologies are not yet established, in the context of BPs. Results We performed integrative analyses of novel human single-cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles. Conclusions Our results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data