Computational and experimental tools of MiRNAs in cancer

MicroRNAs (miRNAs) are short non-protein coding and single-stranded small RNA molecules with a critical role in the regulation of gene expression. These molecules are crucial regulatory elements in diverse biological processes such as apoptosis, development, and progression. miRNA genes have been associated with various human diseases, particularly cancer, and considered as a new biomarker. After the discovery of miRNAs, many researches have focused on identifying and characterizing miRNA genes in cancer. The various expression levels of miRNAs between cancer cells and normal cells are very crucial to diagnosis, prognosis, and treatment of many cancers. Many computational and experimental tools have been employed to characterize miRNAs. However, there exist some challenges in identifying miRNA using both computational and experimental tools due to miRNA features. The present review briefly introduced miRNA biology and certain computational and experimental tools for identifying and profiling miRNAs in cancer. Furthermore, we presented the advantages and challenges of these tools. © 2020, Shriaz University of Medical Sciences. All rights reserved

MicroRNA Interaction Networks

La tesi di Giorgio Bertolazzi è incentrata sullo sviluppo di nuovi algoritmi per la predizione dei legami miRNA-mRNA. In particolare, un algoritmo di machine-learning viene proposto per l'upgrade del web tool ComiR; la versione originale di ComiR considerava soltanto i siti di legame dei miRNA collocati nella regione 3'UTR dell'RNA messaggero. La nuova versione di ComiR include nella ricerca dei legami la regione codificante dell'RNA messaggero.Bertolazzi’s thesis focuses on developing and applying computational methods to predict microRNA binding sites located on messenger RNA molecules. MicroRNAs (miRNAs) regulate gene expression by binding target messenger RNA molecules (mRNAs). Therefore, the prediction of miRNA binding is important to investigate cellular processes. Moreover, alterations in miRNA activity have been associated with many human diseases, such as cancer. The thesis explores miRNA binding behavior and highlights fundamental information for miRNA target prediction. In particular, a machine learning approach is used to upgrade an existing target prediction algorithm named ComiR; the original version of ComiR considers miRNA binding sites located on mRNA 3’UTR region. The novel algorithm significantly improves the ComiR prediction capacity by including miRNA binding sites located on mRNA coding regions

Cell-Free Nucleic Acids

The deficits of mammography and the potential of noninvasive diagnostic testing using circulating miRNA profiles are presented in our first review article. Exosomes are important in the transfer of genetic information. The current knowledge on exosome-associated DNAs and on vesicle-associated DNAs and their role in pregnancy-related complications is presented in the next article. The major obstacle is the lack of a standardized technique for the isolation and measurement of exosomes. One review has summarized the latest results on cell-free nucleic acids in inflammatory bowel disease (IBD). Despite the extensive research, the etiology and exact pathogenesis are still unclear, although similarity to the cell-free ribonucleic acids (cfRNAs) observed in other autoimmune diseases seems to be relevant in IBD. Liquid biopsy is a useful tool for the differentiation of leiomyomas and sarcomas in the corpus uteri. One manuscript has collected the most important knowledge of mesenchymal uterine tumors and shows the benefits of noninvasive sampling. Microchimerism has also recently become a hot topic. It is discussed in the context of various forms of transplantation and transplantation-related advanced therapies, the available cell-free nucleic acid (cfNA) markers, and the detection platforms that have been introduced. Ovarian cancer is one of the leading serious malignancies among women, with a high incidence of mortality; the introduction of new noninvasive diagnostic markers could help in its early detection and treatment monitoring. Epigenetic regulation is very important during the development of diseases and drug resistance. Methylation changes are important signs during ovarian cancer development, and it seems that the CDH1 gene is a potential candidate for being a noninvasive biomarker in the diagnosis of ovarian cancer. Preeclampsia is a mysterious disease—despite intensive research, the exact details of its development are unknown. It seems that cell-free nucleic acids could serve as biomarkers for the early detection of this disease. Three research papers deal with the prenatal application of cfDNA. Copy number variants (CNVs) are important subjects for the study of human genome variations, as CNVs can contribute to population diversity and human genetic diseases. These are useful in NIPT as a source of population specific data. The reliability of NIPT depends on the accurate estimation of fetal fraction. Improvement in the success rate of in vitro fertilization (IVF) and embryo transfer (ET) is an important goal. The measurement of embryo-specific small noncoding RNAs in culture media could improve the efficiency of ET

A Systematic Evaluation of Feature Selection and Classification Algorithms Using Simulated and Real miRNA Sequencing Data

Sequencing is widely used to discover associations between microRNAs (miRNAs) and diseases. However, the negative binomial distribution (NB) and high dimensionality of data obtained using sequencing can lead to low-power results and low reproducibility. Several statistical learning algorithms have been proposed to address sequencing data, and although evaluation of these methods is essential, such studies are relatively rare. The performance of seven feature selection (FS) algorithms, including baySeq, DESeq, edgeR, the rank sum test, lasso, particle swarm optimistic decision tree, and random forest (RF), was compared by simulation under different conditions based on the difference of the mean, the dispersion parameter of the NB, and the signal to noise ratio. Real data were used to evaluate the performance of RF, logistic regression, and support vector machine. Based on the simulation and real data, we discuss the behaviour of the FS and classification algorithms. The Apriori algorithm identified frequent item sets (mir-133a, mir-133b, mir-183, mir-937, and mir-96) from among the deregulated miRNAs of six datasets from The Cancer Genomics Atlas. Taking these findings altogether and considering computational memory requirements, we propose a strategy that combines edgeR and DESeq for large sample sizes

Gene Expression Profiling in Cancer

The contribution of modern-day genetics in designing efficient gene expression profiles for cancer is immense. The progress of technology and science in recent years provides the opportunity for discovery and application of new techniques for treating various diseases that affect humanity. Methods for finding and analyzing the profile of gene expression of infected cells give scientists the ability to develop more targeted and effective treatments, especially for diseases such as cancer. The development of gene expression profiling is one of the most important achievements in cancer genetics in our time. It is essentially the driving force behind personalized and precision medicine. This book highlights recent developments, applications, and breakthroughs in the field of gene expression profiling in cancer

Evaluation of blood-based microRNAs toward clinical use as biomarkers in common and rare diseases

According to the GLOBOCAN project of the International Agency for Research on Cancer, the top three common cancer diseases worldwide in the year 2020 were breast, lung and colorectal cancer. These are usually diagnosed via imaging methods (e.g. computer tomography) or invasive methods (e.g. biopsy). However, these techniques are potentially risky and expensive and thus not accessible to all patients, resulting in most cancers being detected in an advanced stage. Since the discovery of small non-coding RNAs and specifically microRNAs and their role as gene regulators, many researchers investigate their association with disease development. In particular, researchers examine body fluid based microRNAs which could present potential cost-effective and minimally- or non-invasive alternatives to the previously described established diagnosis methods. This dissertation focuses on microRNAs and investigates their suitability as minimally-invasive blood-borne biomarkers for potential diagnostic purposes. More specifically, the goals of this work are (1) to implement a new method to predict novel microRNAs, (2) to understand stability and characteristics of these small non-coding RNAs, possibly relevant for the last goal, (3) to discover potential diagnostic biomarkers in common and rare diseases. The first goal was addressed by developing miRMaster, a web service to predict new microRNAs. The tool uses machine learning and high-throughput sequencing data to find microRNA candidates that follow the known biogenesis pathways. The second goal was pursued in four publications. First, we performed a large scale evaluation of miRMaster by generating a high-resolution map of the human small non-coding RNA transcriptome for which we analyzed and validated potential microRNA candidates. Next, we examined the influence of seasonal effects on microRNA expression profiles and observed the largest difference between spring and the other seasons. Additionally, we evaluated the evolutionary conservation of small non-coding RNAs in zoo animals and showed that the distribution of sncRNA classes varies across species, while common microRNA families are present in more diverse organisms than assumed so far. Furthermore, we analyzed if microRNAs are technically stable, and whether biological variation is preserved when using capillary dried blood spots as an alternative sample collection device to venous blood specimens. Finally, we investigated the suitability of microRNAs as biomarkers for two diseases: lung cancer and Marfan disease. We identified blood-borne biomarker candidates for lung cancer detection in a large-scale multi-center study via machine learning. For the rare Marfan disease we analyzed the paired messenger RNA and microRNA expression levels in whole-blood samples. This highlighted several significantly deregulated microRNAs and messenger RNAs, which we subsequently validated in an independent cohort. In summary, this thesis provides valuable results toward potential clinical use of microRNAs, and the herein described projects represent comprehensive analyses of them from different perspectives: starting with microRNA discovery, addressing various technical and biological questions and ending with the potential use as biomarkers.Nach Angaben des GLOBOCAN-Projekts der International Agency for Research on Cancer sind die drei häufigsten Krebserkrankungen weltweit im Jahr 2020 Brust-, Lungen- und Darmkrebs. Diese werden in der Regel durch bildgebende Verfahren (z.B. Computertomographie) oder invasive Methoden (z.B. Biopsie) diagnostiziert. Diese Verfahren sind jedoch potenziell risikoreich und teuer und daher nicht für alle Patienten zugänglich. Dies führt dazu, dass die meisten Krebsarten erst in einem fortgeschrittenen Stadium entdeckt werden. Seit der Entdeckung der kurzen nichtkodierenden RNAs und insbesondere der microRNAs und ihrer Rolle als Genregulatoren untersuchen viele Forscher ihren Zusammenhang mit der Krankheitsentwicklung. Insbesondere untersuchen die Forscher die in Körperflüssigkeiten vorkommenden microRNAs, die potenziell kosteneffiziente und minimal- oder nicht-invasive Alternativen zu den bisher beschriebenen etablierten Diagnosemethoden darstellen könnten. Diese Dissertation konzentriert sich auf microRNAs und untersucht deren Eignung als minimal-invasive blutbasierte Biomarker für potenzielle diagnostische Zwecke. Genauer gesagt sind die Ziele dieser Arbeit (1) die Implementierung einer neuen Methode zur Vorhersage neuartiger microRNAs, (2) das Verständnis über die Stabilität und Charakteristika dieser kurzen nicht-kodierenden RNAs, die möglicherweise für das nächste Ziel relevant sind, (3) die Entdeckung potenzieller diagnostischer Biomarker für verschiedene Anwendungen. Das erste Ziel wurde durch die Entwicklung von miRMaster verfolgt, einem Webdienst zur Vorhersage neuer microRNAs. Das Tool nutzt maschinelles Lernen und Hochdurchsatz-Sequenzierungsdaten, um microRNA-Kandidaten zu finden, die den bekannten Wege der Biogenese folgen. Das zweite Ziel wurde in vier Veröffentlichungen verfolgt. Zunächst führten wir eine groß angelegte Evaluierung von miRMaster durch, indem wir eine High-Resolution Map des menschlichen Transkriptoms kurzer nichtkodierender RNAs erstellten, für die wir potenzielle microRNA-Kandidaten analysierten und validierten. Anschließend untersuchten wir den Einfluss saisonaler Effekte auf die microRNA-Expressionsprofile und beobachteten den größten Unterschied zwischen dem Frühling und den anderen Jahreszeiten. Darüber hinaus untersuchten wir die evolutionäre Erhaltung kurzer nichtkodierender RNAs in Zoo-Tieren und zeigten, dass die Verteilung der kurzer nichtkodierenden RNA-Klassen zwischen den Arten variiert, während gemeinsame microRNA-Familien in verschiedeneren Organismen vorkommen als bisher angenommen. Darüber hinaus analysierten wir, ob microRNAs technisch stabil sind und ob die biologische Variation erhalten bleibt, wenn kapillares Trockenblut als alternatives Probenentnahmeverfahren zu venösen Blutproben verwendet werden. Schließlich untersuchten wir die Eignung von microRNAs als Biomarker für zwei Krankheiten: Lungenkrebs und Marfan-Krankheit. In einer groß angelegten multizentrischen Studie identifizierten wir mit Hilfe von maschinellem Lernen Biomarker-Kandidaten aus dem Blut für die Erkennung von Lungenkrebs. Für die seltene Marfan-Krankheit analysierten wir die gepaarten Expressionsniveaus von messengerRNA und microRNA in Vollblutproben. Dabei wurden mehrere signifikant deregulierte microRNAs und messengerRNAs festgestellt, die wir anschließend in einer unabhängigen Kohorte validierten. Zusammenfassend lässt sich sagen, dass diese Arbeit wertvolle Ergebnisse im Hinblick auf die potenzielle klinische Verwendung von microRNAs liefert. Die hier beschriebenen Projekte stellen umfassende Analysen aus verschiedenen Blickwinkeln dar: angefangen bei der Entdeckung von microRNAs, über verschiedene technische und biologische Fragen bis hin zur potenziellen Verwendung als Biomarker

Development of a simple artificial intelligence method to accurately subtype breast cancers based on gene expression barcodes

>Magister Scientiae - MScINTRODUCTION: Breast cancer is a highly heterogeneous disease. The complexity of achieving an accurate diagnosis and an effective treatment regimen lies within this heterogeneity. Subtypes of the disease are not simply molecular, i.e. hormone receptor over-expression or absence, but the tumour itself is heterogeneous in terms of tissue of origin, metastases, and histopathological variability. Accurate tumour classification vastly improves treatment decisions, patient outcomes and 5-year survival rates. Gene expression studies aided by transcriptomic technologies such as microarrays and next-generation sequencing (e.g. RNA-Sequencing) have aided oncology researcher and clinician understanding of the complex molecular portraits of malignant breast tumours. Mechanisms governing cancers, which include tumorigenesis, gene fusions, gene over-expression and suppression, cellular process and pathway involvementinvolvement, have been elucidated through comprehensive analyses of the cancer transcriptome. Over the past 20 years, gene expression signatures, discovered with both microarray and RNA-Seq have reached clinical and commercial application through the development of tests such as Mammaprint®, OncotypeDX®, and FoundationOne® CDx, all which focus on chemotherapy sensitivity, prediction of cancer recurrence, and tumour mutational level. The Gene Expression Barcode (GExB) algorithm was developed to allow for easy interpretation and integration of microarray data through data normalization with frozen RMA (fRMA) preprocessing and conversion of relative gene expression to a sequence of 1's and 0's. Unfortunately, the algorithm has not yet been developed for RNA-Seq data. However, implementation of the GExB with feature-selection would contribute to a machine-learning based robust breast cancer and subtype classifier. METHODOLOGY: For microarray data, we applied the GExB algorithm to generate barcodes for normal breast and breast tumour samples. A two-class classifier for malignancy was developed through feature-selection on barcoded samples by selecting for genes with 85% stable absence or presence within a tissue type, and differentially stable between tissues. A multi-class feature-selection method was employed to identify genes with variable expression in one subtype, but 80% stable absence or presence in all other subtypes, i.e. 80% in n-1 subtypes. For RNA-Seq data, a barcoding method needed to be developed which could mimic the GExB algorithm for microarray data. A z-score-to-barcode method was implemented and differential gene expression analysis with selection of the top 100 genes as informative features for classification purposes. The accuracy and discriminatory capability of both microarray-based gene signatures and the RNA-Seq-based gene signatures was assessed through unsupervised and supervised machine-learning algorithms, i.e., K-means and Hierarchical clustering, as well as binary and multi-class Support Vector Machine (SVM) implementations. RESULTS: The GExB-FS method for microarray data yielded an 85-probe and 346-probe informative set for two-class and multi-class classifiers, respectively. The two-class classifier predicted samples as either normal or malignant with 100% accuracy and the multi-class classifier predicted molecular subtype with 96.5% accuracy with SVM. Combining RNA-Seq DE analysis for feature-selection with the z-score-to-barcode method, resulted in a two-class classifier for malignancy, and a multi-class classifier for normal-from-healthy, normal-adjacent-tumour (from cancer patients), and breast tumour samples with 100% accuracy. Most notably, a normal-adjacent-tumour gene expression signature emerged, which differentiated it from normal breast tissues in healthy individuals. CONCLUSION: A potentially novel method for microarray and RNA-Seq data transformation, feature selection and classifier development was established. The universal application of the microarray signatures and validity of the z-score-to-barcode method was proven with 95% accurate classification of RNA-Seq barcoded samples with a microarray discovered gene expression signature. The results from this comprehensive study into the discovery of robust gene expression signatures holds immense potential for further R&F towards implementation at the clinical endpoint, and translation to simpler and cost-effective laboratory methods such as qtPCR-based tests

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