A message passing framework with multiple data integration for miRNA-disease association prediction

Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach’s superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption

Identifying drug-target and drug-disease associations using computational intelligence

Background: Traditional drug development is an expensive process that typically requires the investment of a large number of resources in terms of finances, equipment, and time. However, sometimes these efforts do not result in a pharmaceutical product in the market. To overcome the limitations of this process, complementary—or in some cases, alternative—methods with high-throughput results are necessary. Computational drug discovery is a shortcut that can reduce the difficulties of traditional methods because of its flexible nature. Drug repositioning, which aims to find new applications for existing drugs, is one of the promising approaches in computational drug discovery. Considering the availability of different types of data in various public databases, drug-disease association identification and drug repositioning can be performed based on the interaction of drugs and biomolecules. Moreover, drug repositioning mainly focuses on the similarity of drugs and the similarity of agents interacting with drugs. It is assumed that if drug D is associated or interacts with target T, then drugs similar to drug D can be associated or interact with target T or targets similar to target T. Therefore, similarity-based approaches are widely used for drug repositioning. Research Objectives: Develop novel computational methods for drug-target and drug-disease association prediction to be used for drug repositioning. Results: In this thesis, the problem of drug-disease association identification and drug repositioning is divided into sub-problems. These sub-problems include drug-target interaction prediction and using targets as intermediaries for drug-disease association identification. Addressing these subproblems results in the development of three new computational models for drug-target interaction and drug-disease association prediction: MDIPA, NMTF-DTI, and NTD-DR. MDIPA is a nonnegative matrix factorization-based method to predict interaction scores of drug-microRNA pairs, where the interaction scores can effectively be used for drug repositioning. This method uses the functional similarity of microRNAs and structural similarity of drugs to make predictions. To include more biomolecules (e.g., proteins) in the study as well as achieve a more flexible model, we develop NMTF-DTI. This nonnegative matrix tri- factorization method uses multiple types of similarities for drugs and proteins to predict the associations between drugs and targets and their interaction score. To take another step towards drug repositioning, we identify the associations between drugs and disease. In this step, we develop NTD-DR, a nonnegative tensor decomposition approach where multiple similarities for drugs, targets, and diseases are used to identify the associations between drugs and diseases to be used for drug repositioning. The detail of each method is discussed in Chapters 3, 4, 5, respectively. Future work will focus on considering additional biomolecules as the drug target to identify drug-disease associations for drug repositioning. In summary, using nonnegative matrix factorization, nonnegative matrix tri-factorization, and nonnegative tensor decomposition, as well as applying different types of association information and multiple types of similarities, improve the performance of proposed methods over those methods that use single association or similarity information

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Inter-individual variation of the human epigenome & applications

Genome-wide association studies (GWAS) have led to the discovery of genetic variants influencing human phenotypes in health and disease. However, almost two decades later, most human traits can still not be accurately predicted from common genetic variants. Moreover, genetic variants discovered via GWAS mostly map to the non-coding genome and have historically resisted interpretation via mechanistic models. Alternatively, the epigenome lies in the cross-roads between genetics and the environment. Thus, there is great excitement towards the mapping of epigenetic inter-individual variation since its study may link environmental factors to human traits that remain unexplained by genetic variants. For instance, the environmental component of the epigenome may serve as a source of biomarkers for accurate, robust and interpretable phenotypic prediction on low-heritability traits that cannot be attained by classical genetic-based models. Additionally, its research may provide mechanisms of action for genetic associations at non-coding regions that mediate their effect via the epigenome. The aim of this thesis was to explore epigenetic inter-individual variation and to mitigate some of the methodological limitations faced towards its future valorisation.Chapter 1 is dedicated to the scope and aims of the thesis. It begins by describing historical milestones and basic concepts in human genetics, statistical genetics, the heritability problem and polygenic risk scores. It then moves towards epigenetics, covering the several dimensions it encompasses. It subsequently focuses on DNA methylation with topics like mitotic stability, epigenetic reprogramming, X-inactivation or imprinting. This is followed by concepts from epigenetic epidemiology such as epigenome-wide association studies (EWAS), epigenetic clocks, Mendelian randomization, methylation risk scores and methylation quantitative trait loci (mQTL). The chapter ends by introducing the aims of the thesis.Chapter 2 focuses on stochastic epigenetic inter-individual variation resulting from processes occurring post-twinning, during embryonic development and early life. Specifically, it describes the discovery and characterisation of hundreds of variably methylated CpGs in the blood of healthy adolescent monozygotic (MZ) twins showing equivalent variation among co-twins and unrelated individuals (evCpGs) that could not be explained only by measurement error on the DNA methylation microarray. DNA methylation levels at evCpGs were shown to be stable short-term but susceptible to aging and epigenetic drift in the long-term. The identified sites were significantly enriched at the clustered protocadherin loci, known for stochastic methylation in neurons in the context of embryonic neurodevelopment. Critically, evCpGs were capable of clustering technical and longitudinal replicates while differentiating young MZ twins. Thus, discovered evCpGs can be considered as a first prototype towards universal epigenetic fingerprint, relevant in the discrimination of MZ twins for forensic purposes, currently impossible with standard DNA profiling. Besides, DNA methylation microarrays are the preferred technology for EWAS and mQTL mapping studies. However, their probe design inherently assumes that the assayed genomic DNA is identical to the reference genome, leading to genetic artifacts whenever this assumption is not fulfilled. Building upon the previous experience analysing microarray data, Chapter 3 covers the development and benchmarking of UMtools, an R-package for the quantification and qualification of genetic artifacts on DNA methylation microarrays based on the unprocessed fluorescence intensity signals. These tools were used to assemble an atlas on genetic artifacts encountered on DNA methylation microarrays, including interactions between artifacts or with X-inactivation, imprinting and tissue-specific regulation. Additionally, to distinguish artifacts from genuine epigenetic variation, a co-methylation-based approach was proposed. Overall, this study revealed that genetic artifacts continue to filter through into the reported literature since current methodologies to address them have overlooked this challenge.Furthermore, EWAS, mQTL and allele-specific methylation (ASM) mapping studies have all been employed to map epigenetic variation but require matching phenotypic/genotypic data and can only map specific components of epigenetic inter-individual variation. Inspired by the previously proposed co-methylation strategy, Chapter 4 describes a novel method to simultaneously map inter-haplotype, inter-cell and inter-individual variation without these requirements. Specifically, binomial likelihood function-based bootstrap hypothesis test for co-methylation within reads (Binokulars) is a randomization test that can identify jointly regulated CpGs (JRCs) from pooled whole genome bisulfite sequencing (WGBS) data by solely relying on joint DNA methylation information available in reads spanning multiple CpGs. Binokulars was tested on pooled WGBS data in whole blood, sperm and combined, and benchmarked against EWAS and ASM. Our comparisons revealed that Binokulars can integrate a wide range of epigenetic phenomena under the same umbrella since it simultaneously discovered regions associated with imprinting, cell type- and tissue-specific regulation, mQTL, ageing or even unknown epigenetic processes. Finally, we verified examples of mQTL and polymorphic imprinting by employing another novel tool, JRC_sorter, to classify regions based on epigenotype models and non-pooled WGBS data in cord blood. In the future, we envision how this cost-effective approach can be applied on larger pools to simultaneously highlight regions of interest in the methylome, a highly relevant task in the light of the post-GWAS era.Moving towards future applications of epigenetic inter-individual variation, Chapters 5 and 6 are dedicated to solving some of methodological issues faced in translational epigenomics.Firstly, due to its simplicity and well-known properties, linear regression is the starting point methodology when performing prediction of a continuous outcome given a set of predictors. However, linear regression is incompatible with missing data, a common phenomenon and a huge threat to the integrity of data analysis in empirical sciences, including (epi)genomics. Chapter 5 describes the development of combinatorial linear models (cmb-lm), an imputation-free, CPU/RAM-efficient and privacy-preserving statistical method for linear regression prediction on datasets with missing values. Cmb-lm provide prediction errors that take into account the pattern of missing values in the incomplete data, even at extreme missingness. As a proof-of-concept, we tested cmb-lm in the context of epigenetic ageing clocks, one of the most popular applications of epigenetic inter-individual variation. Overall, cmb-lm offer a simple and flexible methodology with a wide range of applications that can provide a smooth transition towards the valorisation of linear models in the real world, where missing data is almost inevitable. Beyond microarrays, due to its high accuracy, reliability and sample multiplexing capabilities, massively parallel sequencing (MPS) is currently the preferred methodology of choice to translate prediction models for traits of interests into practice. At the same time, tobacco smoking is a frequent habit sustained by more than 1.3 billion people in 2020 and a leading (and preventable) health risk factor in the modern world. Predicting smoking habits from a persistent biomarker, such as DNA methylation, is not only relevant to account for self-reporting bias in public health and personalized medicine studies, but may also allow broadening forensic DNA phenotyping. Previously, a model to predict whether someone is a current, former, or never smoker had been published based on solely 13 CpGs from the hundreds of thousands included in the DNA methylation microarray. However, a matching lab tool with lower marker throughput, and higher accuracy and sensitivity was missing towards translating the model in practice. Chapter 6 describes the development of an MPS assay and data analysis pipeline to quantify DNA methylation on these 13 smoking-associated biomarkers for the prediction of smoking status. Though our systematic evaluation on DNA standards of known methylation levels revealed marker-specific amplification bias, our novel tool was still able to provide highly accurate and reproducible DNA methylation quantification and smoking habit prediction. Overall, our MPS assay allows the technological transfer of DNA methylation microarray findings and models to practical settings, one step closer towards future applications.Finally, Chapter 7 provides a general discussion on the results and topics discussed across Chapters 2-6. It begins by summarizing the main findings across the thesis, including proposals for follow-up studies. It then covers technical limitations pertaining bisulfite conversion and DNA methylation microarrays, but also more general considerations such as restricted data access. This chapter ends by covering the outlook of this PhD thesis, including topics such as bisulfite-free methods, third-generation sequencing, single-cell methylomics, multi-omics and systems biology.<br/

Field Cancerisation in Breast Cancer

Since the national breast screening programme was established in 1987, the number of breast cancer cases detected at an early stage has risen. This has allowed patients with these tumours to undergo breast-conserving therapy (BCT), a surgery where the tumour and clear margins are excised from the breast while preserving it cosmetically as much as possible. However, 40% of these patients suffer from recurrence at the surgical margin. This could be due to microscopic genetic or biochemical changes, defined as “cancerised” fields which may appear histologically normal for a long time, leading pathologists, and surgeons to classify them as normal. This PhD thesis uses computational biology, high-throughput sequencing and proteomics applied to a unique cohort of patient samples. The aim is to generate signatures that identify morphologically normal but genetically altered breast tissues – a concept termed field cancerisation – and to determine whether information from these tissues could be applied to direct the modern management of breast cancer: tailored surgery/therapy, risk assessment, early detection, monitoring, and primary chemoprevention. Analysis of gene expression, clustering, gene fusion, mutation and splicing were implemented. Four subgroups were identified within a new transcriptional classification of histologically normal samples, termed metabolic, immune, matrisome/epithelialmesenchymal transition and non-coding-enriched. Patients from the TCGA dataset whose adjacent sample was classified in the metabolic subgroup had the worst survival compared to the others. Hotspots of gene fusion were detected on chromosome 6, 9, and 14, which have been already presented as potential markers for a good prognosis. We show that molecular changes in histologically normal tissues, including driver mutations in cancer genes, are independent of the distance from the primary index tumour, supporting the hypothesis of field cancerisation

Correction to: RNA Bioinformatics.

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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