Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation

Detecting similarity in biological sequences is a key element to understanding the mechanisms of life. Researchers infer potential structural, functional or evolutionary relationships from similarity. However, the concept of similarity is complex in biology. Sequences consist of different molecules with different chemical properties, have short and long distance interactions, form 3D structures and change through evolutionary processes. Amino acids are one of the key molecules of life. Most importantly, a sequence of amino acids constitutes the building block for proteins which play an essential role in cellular processes. This thesis investigates similarity amongst proteins. In this area of research there are two important and closely related classification tasks – the detection of similar proteins and the discrimination amongst them. Hidden Markov Models (HMMs) have been successfully applied to the detection task as they model sequence similarity very well. From a Machine Learning point of view these HMMs are essentially one-class classifiers trained solely on a small number of similar proteins neglecting the vast number of dissimilar ones. Our basic assumption is that integrating this neglected information will be highly beneficial to the classification task. Thus, we transform the problem representation from a one-class to a binary one. Equipped with the necessary sound understanding of Machine Learning, especially concerning problem representation and statistically significant evaluation, our work pursues and combines two different avenues on this aforementioned transformation. First, we introduce a binary HMM that discriminates significantly better than the standard one, even when only a fraction of the negative information is used. Second, we interpret the HMM as a structured graph of information. This information cannot be accessed by highly optimised standard Machine Learning classifiers as they expect a fixed length feature vector representation. Propositionalisation is a technique to transform the former representation into the latter. This thesis introduces new propositionalisation techniques. The change in representation changes the learning problem from a one-class, generative to a propositional, discriminative one. It is a common assumption that discriminative techniques are better suited for classification tasks, and our results validate this assumption. We suggest a new way to significantly improve on discriminative power and runtime by means of terminating the time-intense training of HMMs early, subsequently applying propositionalisation and classifying with a discriminative, binary learner

Analysis of class C G-protein coupled receptors using supervised classification methods

G protein-coupled receptors (GPCRs) are cell membrane proteins with a key role in regulating the function of cells. This is the result of their ability to transmit extracellular signals, which makes them relevant for pharmacology and has led, over the last decade, to active research in the field of proteomics. The current thesis specifically targets class C of GPCRs, which are relevant in therapies for various central nervous system disorders, such as Alzheimer’s disease, anxiety, Parkinson’s disease and schizophrenia. The investigation of protein functionality often relies on the knowledge of crystal three dimensional (3-D) structures, which determine the receptor’s ability for ligand binding responsible for the activation of certain functionalities in the protein. The structural information is therefore paramount, but it is not always known or easily unravelled, which is the case of eukaryotic cell membrane proteins such as GPCRs. In the face of the lack of information about the 3-D structure, research is often bound to the analysis of the primary amino acid sequences of the proteins, which are commonly known and available from curated databases. Much research on sequence analysis has focused on the quantitative analysis of their aligned versions, although, recently, alternative approaches using machine learning techniques for the analysis of alignment-free sequences have been proposed. In this thesis, we focus on the differentiation of class C GPCRs into functional and structural related subgroups based on the alignment-free analysis of their sequences using supervised classification models. In the first part of the thesis, the main topic is the construction of supervised classification models for unaligned protein sequences based on physicochemical transformations and n-gram representations of their amino acid sequences. These models are useful to assess the internal data quality of the externally labeled dataset and to manage the label noise problem from a data curation perspective. In its second part, the thesis focuses on the analysis of the sequences to discover subtype- and region-speci¿c sequence motifs. For that, we carry out a systematic analysis of the topological sequence segments with supervised classification models and evaluate the subtype discrimination capability of each region. In addition, we apply different types of feature selection techniques to the n-gram representation of the amino acid sequence segments to find subtype and region specific motifs. Finally, we compare the findings of this motif search with the partially known 3D crystallographic structures of class C GPCRs.Los receptores acoplados a proteínas G (GPCRs) son proteínas de la membrana celular con un papel clave para la regulación del funcionamiento de una célula. Esto es consecuencia de su capacidad de transmisión de señales extracelulares, lo que les hace relevante en la farmacología y que ha llevado a investigaciones activas en la última década en el área de la proteómica. Esta tesis se centra específicamente en la clase C de GPCRs, que son relevante para terapias de varios trastornos del sistema nervioso central, como la enfermedad de Alzheimer, ansiedad, enfermedad de Parkinson y esquizofrenia. La investigación de la funcionalidad de proteínas muchas veces se basa en el conocimiento de la estructura cristalina tridimensional (3-D), que determina la capacidad del receptor para la unión con ligandos, que son responsables para la activación de ciertas funcionalidades en la proteína. El análisis de secuencias de amino ácidos se ha centrado en muchas investigaciones en el análisis cuantitativo de las versiones alineados de las secuencias, aunque, recientemente, se han propuesto métodos alternativos usando métodos de aprendizaje automático aplicados a las versiones no-alineadas de las secuencias. En esta tesis, nos centramos en la diferenciación de los GPCRs de la clase C en subgrupos funcionales y estructurales basado en el análisis de las secuencias no-alineadas utilizando modelos de clasificación supervisados. Estos modelos son útiles para evaluar la calidad interna de los datos a partir del conjunto de datos etiquetados externamente y para gestionar el problema del 'ruido de datos' desde la perspectiva de la curación de datos. En su segunda parte, la tesis enfoca el análisis de las secuencias para descubrir motivos de secuencias específicos a nivel de subtipo o región. Para eso, llevamos a cabo un análisis sistemático de los segmentos topológicos de la secuencia con modelos supervisados de clasificación y evaluamos la capacidad de discriminar entre subtipos de cada región. Adicionalmente, aplicamos diferentes tipos de técnicas de selección de atributos a las representaciones mediante n-gramas de los segmentos de secuencias de amino ácidos para encontrar motivos específicos a nivel de subtipo y región. Finalmente, comparamos los descubrimientos de la búsqueda de motivos con las estructuras cristalinas parcialmente conocidas para la clase C de GPCRs

Analysis of class C G-protein coupled receptors using supervised classification methods

G protein-coupled receptors (GPCRs) are cell membrane proteins with a key role in regulating the function of cells. This is the result of their ability to transmit extracellular signals, which makes them relevant for pharmacology and has led, over the last decade, to active research in the field of proteomics. The current thesis specifically targets class C of GPCRs, which are relevant in therapies for various central nervous system disorders, such as Alzheimer’s disease, anxiety, Parkinson’s disease and schizophrenia. The investigation of protein functionality often relies on the knowledge of crystal three dimensional (3-D) structures, which determine the receptor’s ability for ligand binding responsible for the activation of certain functionalities in the protein. The structural information is therefore paramount, but it is not always known or easily unravelled, which is the case of eukaryotic cell membrane proteins such as GPCRs. In the face of the lack of information about the 3-D structure, research is often bound to the analysis of the primary amino acid sequences of the proteins, which are commonly known and available from curated databases. Much research on sequence analysis has focused on the quantitative analysis of their aligned versions, although, recently, alternative approaches using machine learning techniques for the analysis of alignment-free sequences have been proposed. In this thesis, we focus on the differentiation of class C GPCRs into functional and structural related subgroups based on the alignment-free analysis of their sequences using supervised classification models. In the first part of the thesis, the main topic is the construction of supervised classification models for unaligned protein sequences based on physicochemical transformations and n-gram representations of their amino acid sequences. These models are useful to assess the internal data quality of the externally labeled dataset and to manage the label noise problem from a data curation perspective. In its second part, the thesis focuses on the analysis of the sequences to discover subtype- and region-speci¿c sequence motifs. For that, we carry out a systematic analysis of the topological sequence segments with supervised classification models and evaluate the subtype discrimination capability of each region. In addition, we apply different types of feature selection techniques to the n-gram representation of the amino acid sequence segments to find subtype and region specific motifs. Finally, we compare the findings of this motif search with the partially known 3D crystallographic structures of class C GPCRs.Los receptores acoplados a proteínas G (GPCRs) son proteínas de la membrana celular con un papel clave para la regulación del funcionamiento de una célula. Esto es consecuencia de su capacidad de transmisión de señales extracelulares, lo que les hace relevante en la farmacología y que ha llevado a investigaciones activas en la última década en el área de la proteómica. Esta tesis se centra específicamente en la clase C de GPCRs, que son relevante para terapias de varios trastornos del sistema nervioso central, como la enfermedad de Alzheimer, ansiedad, enfermedad de Parkinson y esquizofrenia. La investigación de la funcionalidad de proteínas muchas veces se basa en el conocimiento de la estructura cristalina tridimensional (3-D), que determina la capacidad del receptor para la unión con ligandos, que son responsables para la activación de ciertas funcionalidades en la proteína. El análisis de secuencias de amino ácidos se ha centrado en muchas investigaciones en el análisis cuantitativo de las versiones alineados de las secuencias, aunque, recientemente, se han propuesto métodos alternativos usando métodos de aprendizaje automático aplicados a las versiones no-alineadas de las secuencias. En esta tesis, nos centramos en la diferenciación de los GPCRs de la clase C en subgrupos funcionales y estructurales basado en el análisis de las secuencias no-alineadas utilizando modelos de clasificación supervisados. Estos modelos son útiles para evaluar la calidad interna de los datos a partir del conjunto de datos etiquetados externamente y para gestionar el problema del 'ruido de datos' desde la perspectiva de la curación de datos. En su segunda parte, la tesis enfoca el análisis de las secuencias para descubrir motivos de secuencias específicos a nivel de subtipo o región. Para eso, llevamos a cabo un análisis sistemático de los segmentos topológicos de la secuencia con modelos supervisados de clasificación y evaluamos la capacidad de discriminar entre subtipos de cada región. Adicionalmente, aplicamos diferentes tipos de técnicas de selección de atributos a las representaciones mediante n-gramas de los segmentos de secuencias de amino ácidos para encontrar motivos específicos a nivel de subtipo y región. Finalmente, comparamos los descubrimientos de la búsqueda de motivos con las estructuras cristalinas parcialmente conocidas para la clase C de GPCRs.Postprint (published version

Modeling homo- and hetero-oligomers using in silico prediction of protein quaternary structure

Cellular processes often depend on interactions between proteins and the formation of macromolecular complexes. The impairment of such interactions can lead to deregulation of pathways resulting in disease states, and it is hence crucial to gain insights into the nature of the macromolecular assemblies. Detailed structural knowledge about complexes and protein-protein interactions is growing, but experimentally determined three-dimensional multimeric assemblies are outnumbered by complexes supported by non-structural experimental evidence. In this thesis, we aim to fill this gap by modeling multimeric structures by homology, and we ask which properties of proteins within a family can assist in the prediction of the correct quaternary structure. Specifically, we introduce a description of protein-protein interface conservation as a function of evolutionary distance. This enables us to reduce the noise in deep multiple sequence alignments where sequences of proteins organized in different oligomeric states are interspersed. We also define a distance measure to structurally compare homologous multimeric protein complexes. This allows us to hierarchically cluster protein structures and quantify the diversity of alternative biological assemblies known today in the Protein Data Bank (PDB). We find that a combination of conservation scores, structural clustering, and classical interface descriptors, is able to improve the selection of homologous protein templates leading to reliable models of protein complexes

Understanding the Structural and Functional Importance of Early Folding Residues in Protein Structures

Proteins adopt three-dimensional structures which serve as a starting point to understand protein function and their evolutionary ancestry. It is unclear how proteins fold in vivo and how this process can be recreated in silico in order to predict protein structure from sequence. Contact maps are a possibility to describe whether two residues are in spatial proximity and structures can be derived from this simplified representation. Coevolution or supervised machine learning techniques can compute contact maps from sequence: however, these approaches only predict sparse subsets of the actual contact map. It is shown that the composition of these subsets substantially influences the achievable reconstruction quality because most information in a contact map is redundant. No strategy was proposed which identifies unique contacts for which no redundant backup exists. The StructureDistiller algorithm quantifies the structural relevance of individual contacts and identifies crucial contacts in protein structures. It is demonstrated that using this information the reconstruction performance on a sparse subset of a contact map is increased by 0.4 A, which constitutes a substantial performance gain. The set of the most relevant contacts in a map is also more resilient to false positively predicted contacts: up to 6% of false positives are compensated before reconstruction quality matches a naive selection of contacts without any false positive contacts. This information is invaluable for the training to new structure prediction methods and provides insights into how robustness and information content of contact maps can be improved. In literature, the relevance of two types of residues for in vivo folding has been described. Early folding residues initiate the folding process, whereas highly stable residues prevent spontaneous unfolding events. The structural relevance score proposed by this thesis is employed to characterize both types of residues. Early folding residues form pivotal secondary structure elements, but their structural relevance is average. In contrast, highly stable residues exhibit significantly increased structural relevance. This implies that residues crucial for the folding process are not relevant for structural integrity and vice versa. The position of early folding residues is preserved over the course of evolution as demonstrated for two ancient regions shared by all aminoacyl-tRNA synthetases. One arrangement of folding initiation sites resembles an ancient and widely distributed structural packing motif and captures how reverberations of the earliest periods of life can still be observed in contemporary protein structures

Likelihood of protein structure determination

Strukturelle Genomanalyse (SG) beinhaltet die, mit hohem datendurchsatz verbundene bestimmung der dreidimensionalen struktur von makromolekülen durch experimentelle Methoden wie röntgenstrahlen-kristallographie und NMR spektroskopie. Eines der ziele von SG ist es, zeit und kosten der bestimmung von dreidimensionalen proteinstrukturen zu reduzieren, für die homologe strukturen noch nicht gelöst worden sind. Mehrere faktoren wie unregelmäßige conformationen, unzulässige selektion von domängrenzen und löslichkeit können die produktion von proteinkonstrukten für die strukturbiologie erschweren. Zuverlässige, auf aminosäuresequenz basierende prädiktoren zur berechnung von proteinkristallisation sind folglich von nöten. Die vorhersage von unregelmäßigen konformationen ist essentiell, da diese schwierigkeiten in der kristallisation verursachen können. In dieser arbeit wird eine neue methode präsentiert, die es erlaubt, ungeordnete residuen auf basis der aminosäuresequenz mit hoher genauigkeit vorherzusagen, indem verschiedene, auf einer konsensusmethode basierende vorhersagemittel verwendet werden. Die Leistung dieser neuen methode ist signifikant besser als von jedem einzelnen, bisher erwähnten Prädiktor. Zusätzlich ist es wichtig, die voraussetzungen für den quartärstatus eines proteins auf basis seiner sequenz vorherzusagen. Eine Proteinkette kann aus einem monomeren protein bestehen, oder kann, zusammen mit anderen ketten, oligomere komplexe formen, die entweder aus homo-oligomeren oder hetero-oligomeren bestehen können. Im letzten fall muss vermieden werden, die dreidimensionale struktur eines einzelnen protomers zu bestimmen, weil es nicht funktionell ist und auch extrem schwer in löslicher form zu exprimieren ist. Es ist daher erstrebenswert, ein berechnungsmittel zu nützen, das vorherzusagen erlaubt, ob ein potentielles genprodukt teil eines permanenten und obligaten hetero-oligomeren komplexes ist. Hier wird eine neue, auf der aminosäuresequenz basierende methode präsentiert, um hetero-oligomere von monomer und homo-oligomeren proteinen und auch um monomere von homo-oligomeren mit hoher genauigkeit zu unterscheiden. Das erfordernis von metallionen ist im design von strukturbiologischen experimenten ebenso wichtig. Metalloproteine bilden etwa ein drittel der proteoms. Die vorhersage von metalloproteinen hilft kristallographen, geeignetes wachstumsmedium für überexpressionsstudien auszuwählen und auch die wahrscheinlichkeit zu erhöhen, ein korrekt gefaltetes und funktionelles molekül zu erhalten. Hier wird gezeigt, dass die aufnahme von metallionen von proteinen auf basis der aminosäurenzusammensetzung und durch verwenden von lernfähigen analyseprogrammen mit hoher genauigkeit vorhergesagt werden kann. Die ergebnisse in der vorliegenden Doktorarbeit stellen die basis für das sorgfältige design von Proteinkonstrukten dar. Diese computer basierenden selektionsmethoden sind hilfreich, um die auswahl von unmöglichen Zielen zu vermeiden – ein Muss in Strukturbiologie und Proteomics.Structural Genomics (SG) involves the high-throughput determination of threedimensional structures of macromolecules by experimental methods such as X-ray crystallography and NMR spectroscopy. One of the aims of SG is to reduce the time and cost in the determination of three-dimensional protein structures for which a homologous structure had not yet been solved. Several factors such as conformational disorder, improper selection of domain boundaries and solubility can hamper the production of protein constructs for structural biology. Reliable computational protein crystallization propensity predictors, based on amino acid sequences, are consequently required. Prediction of protein conformational disorder is important since it can cause difficulty in crystallization. In this work, a new procedure is presented that allows one to predict disordered residues with high accuracy on the basis of amino acid sequences, by using a consensus method based on various prediction tools. The performance of this new procedure is significantly better than that of each individual predictor previously reported. Furthermore, it is important to be able to predict the quaternary status requirements of a protein on the basis of its sequence. A protein chain can be a monomeric protein or it can form, together with other chains, oligomeric assemblies, which can be either homooligomers or hetero-oligomers. In the later case, it must be avoided to determine the three-dimensional structure of a single protomer, since it will not be functional and it will also be extremely difficult to express in a soluble form. It is thus desirable to have a computational tool that allows one to predict if a potential gene product is a part of permanent and obligate hetero-oligomeric assembly. A new method is presented for discriminating hetero-oligomers from monomeric and homo-oligomeric proteins and also between monomers and homo-oliogmers with high accuracy on the basis of amino acid sequences. Metal ion requirements are also important in designing structural biology experiments. Metalloproteins constitute about one-third of the proteome. Prediction of metalloprotein helps crystallographers to select the proper growth medium for over-expression studies and also to increase the probability of obtaining a properly folded and functional molecule. Here it is shown that the uptake of metal ions by proteins can be predicted with high accuracy on the basis of the amino acid composition and by using machine learning methods. The results described in the present Thesis provide a basis for the careful design of protein constructs. These computational screening methods are helpful to avoid the selection of 'impossible' targets- a must in structural biology and proteomics

From tools and databases to clinically relevant applications in miRNA research

While especially early research focused on the small portion of the human genome that encodes proteins, it became apparent that molecules responsible for many key functions were also encoded in the remaining regions. Originally, non-coding RNAs, i.e., molecules that are not translated into proteins, were thought to be composed of only two classes (ribosomal RNAs and transfer RNAs). However, starting from the early 1980s many other non-coding RNA classes were discovered. In the past two decades, small non-coding RNAs (sncRNAs) and in particular microRNAs (miRNAs), have become essential molecules in biological and biomedical research. In this thesis, five aspects of miRNA research have been addressed. Starting from the development of advanced computational software to analyze miRNA data (1), an in-depth understanding of human and non-human miRNAs was generated and databases hosting this knowledge were created (2). In addition, the effects of technological advances were evaluated (3). We also contributed to the understanding on how miRNAs act in an orchestrated manner to target human genes (4). Finally, based on the insights gained from the tools and resources of the mentioned aspects we evaluated the suitability of miRNAs as biomarkers (5). With the establishment of next-generation sequencing, the primary goal of this thesis was the creation of an advanced bioinformatics analysis pipeline for high-throughput miRNA sequencing data, primarily focused on human. Consequently, miRMaster, a web-based software solution to analyze hundreds sequencing samples within few hours was implemented. The tool was implemented in a way that it could support different sequencing technologies and library preparation techniques. This flexibility allowed miRMaster to build a consequent user-base, resulting in over 120,000 processed samples and 1,5 billion processed reads, as of July 2021, and therefore laid out the basis for the second goal of this thesis. Indeed, the implementation of a feature allowing users to share their uploaded data contributed strongly to the generation of a detailed annotation of the human small non-coding transcriptome. This annotation was integrated into a new miRNA database, miRCarta, modelling thousands of miRNA candidates and corresponding read expression profiles. A subset of these candidates was then evaluated in the context of different diseases and validated. The thereby gained knowledge was subsequently used to validate additional miRNA candidates and to generate an estimate of the number of miRNAs in human. The large collection of samples, gathered over many years with miRMaster was also integrated into a web server evaluating miRNA arm shifts and switches, miRSwitch. Finally, we published an updated version of miRMaster, expanding its scope to other species and adding additional downstream analysis capabilities. The second goal of this thesis was further pursued by investigating the distribution of miRNAs across different human tissues and body fluids, as well as the variability of miRNA profiles over the four seasons of the year. Furthermore, small non-coding RNAs in zoo animals were examined and a tissue atlas of small non-coding RNAs for mice was generated. The third goal, the assessment of technological advances, was addressed by evaluating the new combinatorial probe-anchor synthesis-based sequencing technology published by BGI, analyzing the effect of RNA integrity on sequencing data, analyzing low-input library preparation protocols, and comparing template-switch based library preparation protocols to ligation-based ones. In addition, an antibody-based labeling sequencing chemistry, CoolMPS, was investigated. Deriving an understanding of the orchestrated regulation by miRNAs, the fourth goal of this thesis, was pursued in a first step by the implementation of a web server visualizing miRNA-gene interaction networks, miRTargetLink. Subsequently, miRPathDB, a database incorporating pathways affected by miRNAs and their targets was implemented, as well as miEAA 2.0, a web server offering quick miRNA set enrichment analyses in over 130,000 categories spanning 10 different species. In addition, miRSNPdb, a database evaluating the effects of single nucleotide polymorphisms and variants in miRNAs or in their target genes was created. Finally, the fifth goal of the thesis, the evaluation of the suitability of miRNAs as biomarkers for human diseases was tackled by investigating the expression profiles of miRNAs with machine learning. An Alzheimer's disease cohort with over 400 individuals was analyzed, as well as another neurodegenerative disease cohort with multiple time points of Parkinson's disease patients and healthy controls. Furthermore, a lung cancer cohort covering 3,000 individuals was examined to evaluate the suitability of an early detection test. In addition, we evaluated the expression profile changes induced by aging on a cohort of 1,334 healthy individuals and over 3,000 diseased patients. Altogether, the herein described tools, databases and research papers present valuable advances and insights into the miRNA research field and have been used and cited by the research community over 2,000 times as of July 2021.Während insbesondere die frühe Genetik-Forschung sich auf den kleinen Teil des menschlichen Genoms konzentrierte, der für Proteine kodiert, wurde deutlich, dass auch in den übrigen Regionen Moleküle kodiert werden, die für viele wichtige Funktionen verantwortlich sind. Ursprünglich ging man davon aus, dass nicht codierende RNAs, d. h. Moleküle, die nicht in Proteine übersetzt werden, nur aus zwei Klassen bestehen (ribosomale RNAs und Transfer-RNAs). Seit den frühen 1980er Jahren wurden jedoch viele andere nicht-kodierende RNA-Klassen entdeckt. In den letzten zwei Jahrzehnten sind kleine nichtcodierende RNAs (sncRNAs) und insbesondere microRNAs (miRNAs) zu wichtigen Molekülen in der biologischen und biomedizinischen Forschung geworden. In dieser Arbeit werden fünf Aspekte der miRNA-Forschung behandelt. Ausgehend von der Entwicklung fortschrittlicher Computersoftware zur Analyse von miRNA-Daten (1) wurde ein tiefgreifendes Verständnis menschlicher und nicht-menschlicher miRNAs entwickelt und Datenbanken mit diesem Wissen erstellt (2). Darüber hinaus wurden die Auswirkungen des technologischen Fortschritts bewertet (3). Wir haben auch dazu beigetragen, zu verstehen, wie miRNAs koordiniert agieren, um menschliche Gene zu regulieren (4). Schließlich bewerteten wir anhand der Erkenntnisse, die wir mit den Tools und Ressourcen der genannten Aspekte gewonnen hatten, die Eignung von miRNAs als Biomarker (5). Mit der Etablierung der Sequenzierung der nächsten Generation war das primäre Ziel dieser Arbeit die Schaffung einer fortschrittlichen bioinformatischen Analysepipeline für Hochdurchsatz-MiRNA-Sequenzierungsdaten, die sich in erster Linie auf den Menschen konzentriert. Daher wurde miRMaster, eine webbasierte Softwarelösung zur Analyse von Hunderten von Sequenzierproben innerhalb weniger Stunden, implementiert. Das Tool wurde so implementiert, dass es verschiedene Sequenzierungstechnologien und Bibliotheksvorbereitungstechniken unterstützen kann. Diese Flexibilität ermöglichte es miRMaster, eine konsequente Nutzerbasis aufzubauen, die im Juli 2021 über 120.000 verarbeitete Proben und 1,5 Milliarden verarbeitete Reads umfasste, womit die Grundlage für das zweite Ziel dieser Arbeit geschaffen wurde. Die Implementierung einer Funktion, die es den Nutzern ermöglicht, ihre hochgeladenen Daten mit anderen zu teilen, trug wesentlich zur Erstellung einer detaillierten Annotation des menschlichen kleinen nicht-kodierenden Transkriptoms bei. Diese Annotation wurde in eine neue miRNA-Datenbank, miRCarta, integriert, die Tausende von miRNA-Kandidaten und entsprechende Expressionsprofile abbildet. Eine Teilmenge dieser Kandidaten wurde dann im Zusammenhang mit verschiedenen Krankheiten bewertet und validiert. Die so gewonnenen Erkenntnisse wurden anschließend genutzt, um weitere miRNA-Kandidaten zu validieren und eine Schätzung der Anzahl der miRNAs im Menschen vorzunehmen. Die große Sammlung von Proben, die über viele Jahre mit miRMaster gesammelt wurde, wurde auch in einen Webserver integriert, der miRNA-Armverschiebungen und -Wechsel auswertet, miRSwitch. Schließlich haben wir eine aktualisierte Version von miRMaster veröffentlicht, die den Anwendungsbereich auf andere Spezies ausweitet und zusätzliche Downstream-Analysefunktionen hinzufügt. Das zweite Ziel dieser Arbeit wurde weiterverfolgt, indem die Verteilung von miRNAs in verschiedenen menschlichen Geweben und Körperflüssigkeiten sowie die Variabilität der miRNA-Profile über die vier Jahreszeiten hinweg untersucht wurde. Darüber hinaus wurden kleine nichtkodierende RNAs in Zootieren untersucht und ein Gewebeatlas der kleinen nichtkodierenden RNAs für Mäuse erstellt. Das dritte Ziel, die Einschätzung des technologischen Fortschritts, wurde angegangen, indem die neue kombinatorische Sonden-Anker-Synthese-basierte Sequenzierungstechnologie, die vom BGI veröffentlicht wurde, bewertet wurde, die Auswirkungen der RNA-Integrität auf die Sequenzierungsdaten analysiert wurden, Protokolle für die Bibliotheksvorbereitung mit geringem Input analysiert wurden und Protokolle für die Bibliotheksvorbereitung auf der Basis von Template-Switch mit solchen auf Ligationsbasis verglichen wurden. Darüber hinaus wurde eine auf Antikörpern basierende Labeling-Sequenzierungschemie, CoolMPS, untersucht. Das vierte Ziel dieser Arbeit, das Verständnis der orchestrierten Regulation durch miRNAs, wurde in einem ersten Schritt durch die Implementierung eines Webservers zur Visualisierung von miRNA-Gen-Interaktionsnetzwerken, miRTargetLink, verfolgt. Anschließend wurde miRPathDB implementiert, eine Datenbank, die von miRNAs und ihren Zielgenen beeinflusste Pfade enthält, sowie miEAA 2.0, ein Webserver, der schnelle miRNA-Anreicherungsanalysen in über 130.000 Kategorien aus 10 verschiedenen Spezies bietet. Darüber hinaus wurde miRSNPdb, eine Datenbank zur Bewertung der Auswirkungen von Einzelnukleotid-Polymorphismen und Varianten in miRNAs oder ihren Zielgenen, erstellt. Schließlich wurde das fünfte Ziel der Arbeit, die Bewertung der Eignung von miRNAs als Biomarker für menschliche Krankheiten, durch die Untersuchung der Expressionsprofile von miRNAs anhand von maschinellem Lernen angegangen. Eine Alzheimer-Kohorte mit über 400 Personen wurde analysiert, ebenso wie eine weitere neurodegenerative Krankheitskohorte mit Parkinson-Patienten an mehreren Zeitpunkten der Krankheit und gesunden Kontrollen. Außerdem wurde eine Lungenkrebskohorte mit 3.000 Personen untersucht, um die Eignung eines Früherkennungstests zu bewerten. Darüber hinaus haben wir die altersbedingten Veränderungen des Expressionsprofils bei einer Kohorte von 1.334 gesunden Personen und über 3.000 kranken Patienten untersucht. Insgesamt stellen die hier beschriebenen Tools, Datenbanken und Forschungsarbeiten wertvolle Fortschritte und Erkenntnisse auf dem Gebiet der miRNA-Forschung dar und wurden bis Juli 2021 von der Forschungsgemeinschaft über 2.000 Mal verwendet und zitiert