Functional characterization and annotation of trait-associated genomic regions by transcriptome analysis

In this work, two novel implementations have been presented, which could assist in the design and data analysis of high-throughput genomic experiments. An efficient and flexible tiling probe selection pipeline utilizing the penalized uniqueness score has been implemented, which could be employed in the design of various types and scales of genome tiling task. A novel hidden semi-Markov model (HSMM) implementation is made available within the Bioconductor project, which provides a unified interface for segmenting genomic data in a wide range of research subjects.In dieser Arbeit werden zwei neuartige Implementierungen präsentiert, die im Design und in der Datenanalyse von genomischen Hochdurchsatz-Experiment hilfreich sein könnten. Die erste Implementierung bildet eine effiziente und flexible Auswahl-Pipeline für Tiling-Proben, basierend auf einem Eindeutigkeitsmaß mit einer Maluswertung. Als zweite Implementierung wurde ein neuartiges Hidden-Semi-Markov-Modell (HSMM) im Bioconductor Projekt verfügbar gemacht

A bioinformatics framework for management and analysis of high throughput CGH microarray projects

High throughput experimental techniques have revolutionised biological research; these techniques enable researchers, in an unbiased fashion to survey entire biological systems such as all the somatic mutations in a tumour in a single experiment. Due to the often complex informatics demands of these techniques, robust computational solutions are required to ensure high quality reproducible results are generated. The challenge of this thesis was to develop such a computational solution for the management and analysis of high throughput microarray Comparative Genomic Hybridisation (aCGH) projects. This task also provided an opportunity to test the hypothesis that agile software development approaches are well suited for bioinformatics projects and that formalised development practices produce better quality software. This is an important question as formalised software development practices have been underused so far in the eld of bioinformatics. This thesis describes the development and application of a bioinformatics framework for the management and analysis of microarray CGH projects. The framework includes: a Laboratory Information Management System (LIMS) that manages and records all aspects of microarray CGH experimentation; a set of easy to use visualisation tools for aCGH experimental data; and a suite of object oriented Perl modules providing a exible way to construct data pipelines quickly using the statistical programming language R for quality control, normalisation and analysis. In order to test the framework, it was successfully applied in the aCGH pro ling of 94 ovarian tumour samples. Subsequent analysis of these data identi ed 4 well supported genomic regions which appear to in uence patient survival. The evaluation of agile practices implemented in this thesis has demonstrated that they are well suited to the development of bioinformatics solutions as they enable developers to react to the changes of this rapidly evolving eld, to create successful software solutions such as the bioinformatics framework presented here

Algorithms And Tools For Computational Analysis Of Human Transcriptome Using Rna-Seq

Alternative splicing plays a key role in regulating gene expression, and more than 90% of human genes are alternatively spliced through different types of alternative splicing. Dysregulated alternative splicing events have been linked to a number of human diseases. Recently, high-throughput RNA-Seq technologies have provided unprecedented opportunities to better characterize and understand transcriptomes, in particular useful for the detection of splicing variants between healthy and diseased human transcriptomes. We have developed two novel algorithms and tools and a computational workflow to interrogate human transcriptomes between healthy and diseased conditions. The first is a read count-based Expectation-Maximization (EM) algorithm and tool, which is called RAEM. It estimates relative transcript isoform proportions by maximizing the likelihood in each gene. The RAEM algorithm has been encoded in our published software suite, SAMMate. We have employed RAEM to predict isoform-level microRNA-155 targets. The second is called dSpliceType, which is a read coverage-based algorithm and tool to detect differential splicing events. It utilizes sequential dependency of normalized base-wise read coverage signals and a change-point analysis, followed by a parametric statistical hypothesis test using Schwarz Information Criterion (SIC) to detect significant differential splicing events in the form of the five well-known splicing types. The results of both simulation and real-world studies demonstrate that dSpliceType is an efficient computational tool for detecting various types of differential splicing events from a wide range of expressed genes. Finally, we developed a novel computational workflow to jointly study human diseases in terms of both differential expression and differential splicing. The workflow has been used to detect differential splicing variants from non-differentially expressed genes of human idiopathic pulmonary fibrosis (IPF) lung disease

Genomic Evolution of Chemoresistance in Triple-Negative Breast Cancer Delineated by Single Cell Sequencing

Triple-negative breast cancer (TNBC) is an aggressive subtype that displays extensive intratumor heterogeneity and frequently (46%) develops resistance to neoadjuvant chemotherapy (NAC). Currently, the genomic basis of chemoresistance remains poorly understood. An important question is whether resistance to chemotherapy is driven by the selection of rare pre-existing subclones with genomic mutations and transcriptional programs that confer resistance to chemotherapy (adaptive resistance) or by the spontaneous induction of new mutations and expression changes that confer a resistant phenotype (acquired resistance). To investigate this question we applied single cell DNA and RNA sequencing methods and deep-exome sequencing to longitudinal time-point samples collected from a cohort of 20 TNBC patients. Deep-exome sequencing of the cohort at three time points revealed patterns of both clonal extinction and clonal persistence, with a subset of patients displaying adaptive selection of pre-existing rare mutations. Single-cell copy number profiling of 900 cells from 8 patients also identified an adaptive resistance model, wherein minor subclones from the pre-treatment tumors were selected and expanded in response to NAC. In contrast, single cell RNA sequencing of 6,862 cells from 8 patients identified subclones with chemoresistant phenotypes that were reprogrammed in response to NAC. These data suggest that chemoresistance at the genotypic level evolves through the selection of pre-existing point mutations and copy number changes, while chemoresistance at the phenotypic level evolves through the reprogramming of expression changes in signaling pathways associated with chemoresistance. These characterizations of adaptive and acquired resistance shed light on the evolutionary trajectory of chemoresistance in TNBC patients

Biological predictors of survival in limphoma and mechanisms underlying follicular lymphoma transformaion into diffuse large B cell lymphoma (vol I e II)

RESUMO: Os biomarcadores tumorais permitem identificar os doentes com maior risco de recorrência da doença, predizer a resposta tumoral à terapêutica e, finalmente, definir candidatos a novos alvos terapêuticos. Novos biomarcadores são especialmente necessários na abordagem clínica dos linfomas. Actualmente, esses tumores são diagnosticados através de uma combinação de características morfológicas, fenotípicas e moleculares, mas o prognóstico e o planeamento terapêutico estão quase exclusivamente dependentes de características clínicas. Estes factores clínicos são, na maioria dos linfomas, insuficientes numa proporção significativa dos doentes, em particular, aqueles com pior prognóstico. O linfoma folicular (LF) é, globalmente, o segundo subtipo mais comum de linfoma. É tipicamente uma doença indolente com uma sobrevida média entre os 8 e 12 anos, mas é geralmente fatal quando se transforma num linfoma agressivo de alto grau, habitualmente o linfoma difuso de grandes células B (LDGCB). Morfologicamente e funcionalmente, as células do LF recapitulam as células normais do centro germinativo na sua dependência de sobrevivência do microambiente não-tumoral, especialmente das células do sistema imunológico. Biomarcadores preditivos de transformação não existem pelo que um melhor conhecimento da biologia intrínseca de progressão do LF poderá revelar novos candidatos. Nesta tese descrevo duas abordagens distintas para a descoberta de novos biomarcadores. A primeira, o estudo da expressão global de genes ('genomics') obtidos por técnicas de alto rendimento que analisam todo o genoma humano sequenciado, permitindo identificar novas anomalias genéticas que possam representar mecanismos biológicos importantes de transformação. São descritos novos genes e alterações genómicas associados à transformação do LF, sendo especialmente relevantes as relacionadas com os eventos iniciais de transformação em LDGCB. A segunda, baseou-se em várias hipóteses centradas no microambiente do LF, rico em vários tipos de células nãomalignas. Os estudos imunoarquitectural de macrófagos, células T regulatórias e densidade de microvasos efectuado em biopsias de diagnóstico de doentes com LF tratados uniformemente correlacionaram-se significativamente, e independentemente dos critérios clínicos, com a evolução clínica e, mais importante, com o risco de transformação em LDGCB. Nesta tese, foram preferencialmente utilizadas (e optimizadas) técnicas que permitam o uso de amostras fixadas em parafina e formalina (FFPET). Estas são facilmente acessíveis a partir das biopsias de diagnóstico de rotina presentes nos arquivos de todos os departamentos de patologia, facilitando uma transição rápida dos novos marcadores para a prática clínica. Embora o FL fosse o tema principal da tese, os novos achados permitiram estender facilmente hipóteses semelhantes a outros subtipos de linfoma. Assim, são propostos e validados vários biomarcadores promissores e relacionados com o microambiente não tumoral, sobretudo dependentes das células do sistema imunológico, como contribuintes importantes para a biologia dos linfomas. Estes sugerem novas opções para a abordagem clínica destas doenças e, eventualmente, novos alvos terapêuticos.------------- ABSTRACT: Cancer biomarkers provide an opportunity to identify those patients most at risk for disease recurrence, predict which tumours will respond to different therapeutic approaches and ultimately define candidate biomarkers that may serve as targets for personalized therapy. New biomarkers are especially needed in the management of lymphoid cancers. At present, these tumours are diagnosed using a combination of morphologic, phenotypic and molecular features but prognosis and overall survival are mostly dependent on clinical characteristics. In most lymphoma types, these imprecisely assess a significant proportion of patients, in particular, those with very poor outcomes. Follicular lymphoma (FL) is the second most common lymphoma subtype worldwide. It is typically an indolent disease with current median survivals in the range of 8-12 years, but is usually fatal when it transforms into an aggressive high-grade lymphoma, characteristically Diffuse Large B Cell Lymphoma (DLBCL). Morphologically and functionally it recapitulates the normal cells of the germinal center with its survival dependency on non-malignant immune and immunerelated cells. Informative markers of transformation related to the intrinsic biology of FL progression are needed. Within this thesis two separate approaches to biomarker discovery were employed. The first was to study the global expression of genes (‘genomics’) obtained using high-throughput, wholegenome-wide approaches that offered the possibility for discovery of new genetic abnormalities that might represent the important biological mechanisms of transformation. Gene signatures associated with early events of transformation were found. Another approach relied on hypothesis-driven concepts focusing upon the microenvironment, rich in several non-malignant cell types. The immunoarchitectural studies of macrophages, regulatory T cells and microvessel density on diagnostic biopsies of uniformly treated FL patients significantly predicted clinical outcome and, importantly, also informed on the risk of transformation. Techniques that enabled the use of routine formalin fixed paraffin embedded diagnostic specimens from the pathology department archives were preferentially used in this thesis with the goal of fulfilling a rapid bench-to-beside” translation for these new findings. Although FL was the main subject of the thesis the new findings and hypotheses allowed easy transition into other lymphoma types. Several promising biomarkers were proposed and validated including the implication of several non-neoplastic immune cells as important contributors to lymphoma biology, opening new options for better treatment planning and eventually new therapeutic targets and candidate therapeutics

Identifying biological associations from high-throughput datasets

High-throughput biological datasets are the basis for most modern basic research in the fields of genomics, systems biology, and disease diagnostics. Currently, one sample can contain thousands of measurements in some datasets. The omnipresence of such huge datasets created the urgent need for efficient and robust computational approaches to handle and analyze such database and to identify informative associations. This thesis deals with different types of large scale datasets and aims to identify with high confidence underlying biological associations. Our computational approach consists of four core parts. In the first part, we analyzed amino acid datasets of membrane transporters from different organisms for the purpose of transferring functional annotations of the transporters across species. Here, we mapped the experimentally validated functions of one protein to another one from a different organism based on their sequence similarity. Sequence similarity results in this work were combinations of similarity decisions of several tools (BLAST, HMMER, MEME). Initially, we defined confidence thresholds and which we then applied for predictions. We found that, up to certain thresholds, membrane transporters sharing high sequence similarity have similar functions or transporting mechanisms even if they exist in different organisms. Our second computational approach was designed to deal with expression and methylation datasets. We found that expression and methylation datasets often suffer from outliers at gene or sample levels. Performing analyses before dealing with outliers might lead to misleading results. Thus, we present an approach that includes several outlier detection algorithms for detecting sample and gene outliers in expression/methylation datasets. As some outlier algorithms report at least one outlier value even if there is none, we first defined the margin of allowed outlier observations. We tested how many outlier observations are needed to ruin a perfect co-expression and then fixed that threshold for the rest of analyses. Additionally, in this work we considered the distribution underlying he gene expression/methylation before outlier detection. However, outliers might carry useful information. Therefore, we labelled only extreme outliers for removal and marked those possibly carrying useful information for further analysis. In the next step, we used published expression and methylation datasets from GEO to analyse and confirm possible tumor markers for HCC, liver diseases, and breast cancer. These were later validated in the wetlab through our collaboration with the group of Prof. Kiemer in pharmacy. In addition to their possible roles in the change of survival rates, we also tested the role of several possible markers in tumor initiation and progression. The final part of this thesis dealt with large scale exon expression, methylation, and chromatin modification datasets for 11 different developmental stages from the Human Epigenome Atlas. Our aim in this genome wide analysis was to identify cases of differential exon usage in different dataset. Our findings suggested a set of strong associations of epigenetic modifications and alternative splicing especially in early human developmental stages. In summary, the combination of the approaches presented in this thesis may advance the current stages of tumor marker identification. Membrane transporters play key roles in cancer progression. Once their function is defined with the help of similar transporters in other organisms, one may compare their expression and methylation profiles in normal and tumor tissues. The expression/methylation datasets should be cleared first from outliers. Once a tumor marker is defined or confirmed, further analysis is suggested especially for possible different splice variants.Biologische Datensätze aus Hochdurchsatzverfahren sind meist die Basis zeitgemäßer Grundlagenforschung in Genomik, Systembiologie und Krankheitsdiagnstik. Eine Probe kann in manchen Datensätzen momentan tausende Messungen umfassen. Die Allgegenwärtigkeit solch enormer Datenmengen brachte den dringenden Bedarf an effizienten und robusten computergestützten Ansätzen mit sich, die diese Daten verarbeiten und analysieren können und die informative Assoziationen ermitteln. Diese Arbeit beschäftigt sich mit unterschiedlichen Arten von umfangreichen Datensätzen und beabsichtigt zu Grunde liegende biologische Zusammenhänge mit hoher Zuverlässigkeit zu erkennen. Unsere Methodik besteht aus vier Kernteilen. Im ersten Teil analysierten wir Aminosäure-Daten von Transporterproteinen aus verschiedenen Organismen um funktionelle Annotierungen der Membranproteine speziesübergreifend transferieren zu können. In unserem Fall bildeten wir anhand der Sequenzähnlichkeit die experimentell validierte Funktionen eines Proteins auf ein anderes aus einem anderen Organismus ab. Die Sequenzähnlichkeit in dieser Studie war eine Kombination aus Ähnlichkeitsmaßen verschiedener Softwarewerkzeuge (BLAST, HMMER, MEME). Zuerst definierten wir Vertrauensgrenzwerte (für besagte Werkzeuge) die wir dann für die Vorhersage anwendeten. Wir fanden heraus, dass Membrantransporter mit hoher Sequenzähnlichkeit bis zu gewissen Schwellenwerten sogar dann ähnliche Funktionen oder Transportmechanismen haben wenn sie aus unterschiedlichen Organismen stammen. Unser zweiter rechnergestützter Ansatz wurde entworfen um Expressions- und Methylierungsdaten zu handhaben. Wir sahen, dass diese Daten oft durch Ausreißer auf Gen- oder Probenebene in Mitleidenschaften gezogen werden. Das Durchführen von Untersuchungen vor einer Bereinigung dieser Ausreißer kann irreführende Ergebnisse zur Folge haben. Daher bieten wir eine Methode die mehrere Ausreißererkennungsalgorithmen beinhaltet um Proben- und Gensonderfälle in Expressions-/Methylierungsdatensätzen zu erkennen. Da einige Ausreißererkennungsmethoden auch dann zumindest einen Ausreißer melden wenn eigentlich keiner vorhanden ist, legten wir zuerst einen Grenzwert für erlaubte Ausnahmefälle fest. Wir prüften wie viele Ausreißerbeobachtungen benötigt wurden um perfekte Koexpression zunichte zu machen und setzten diesen Grenzwert dann für die verbleibende Analyse fest. Zusätzlich haben wir in dieser Arbeit die Verteilung von Genexprimierung/Methylierung vor der Ausreißererkennung bedacht. Dennoch könnten Ausreißer dienliche Information mit sich bringen. Daher markierten wir nur extreme Ausreißer explizit zur Entfernung und solche, die für weitere Untersuchungen potentiell nützliche Information beinhalteten, markierten wir gesondert. Im nächsten Schritt nutzten wir publizierte Expressions- und Methylierungsdatensätze von GEO um mögliche Tumormarker für HCC, Leberkrankheiten und Brustkrebs zu analysieren und zu bestätigen. Diese wurden später durch unsere pharmazeutischen Kollaborationspartner der Gruppe von Prof. Kiemer im Labor validiert. Zusätzlich zu ihren eventuellen Rollen in der Veränderung von Überlebensraten haben wir auch die Funktion mehrerer möglicher Marker bezüglich Tumorinitiierung- und progression untersucht. Der letzte Teil dieser Arbeit befasste sich mit umfangreichen Datensätzen für Exonexpression, Methylierung und Chromatinmodifikationen über 11 verschiedenen Entwicklungsstadien aus dem Human Epigenome Atlas. In dieser genomweiten Untersuchung war es unser Ziel Fälle von veränderter Exonnutzung in verschiedenen Datensätzen zu finden. Unsere Resultate legen insbesondere in frühen menschlichen Entwicklungsstadien einige gewichtige Zusammenhänge zwischen epigenetischen Modifikationen und alternativem Spleißen nahe. Zusammenfassend lässt sich sagen, dass die Kombination der hier präsentierten Ansätze gegenwärtige Stufen der Tumormarkererkennung beschleunigen/verbessern könnte. Membrantransporter haben Schlüsselrollen in der Krebsprogression inne. Sobald ihre Funktion mit der Hilfe ähnlicher Transporter in anderen Lebewesen aufgeklärt ist, könnte man ihre Expressions- und Methylierungsverläufe in gesundem und in Tumorgewebe vergleichen. Die Expressions/Methylierungsdaten sollten hierbei erst von Aureißern bereinigt werden. Sobald ein Tumormarker definiert oder bestätigt ist, ist weitere Untersuchung insbesondere im Hinblick auf verschiedene Spleißvarianten angeraten

Identifying biological associations from high-throughput datasets

High-throughput biological datasets are the basis for most modern basic research in the fields of genomics, systems biology, and disease diagnostics. Currently, one sample can contain thousands of measurements in some datasets. The omnipresence of such huge datasets created the urgent need for efficient and robust computational approaches to handle and analyze such database and to identify informative associations. This thesis deals with different types of large scale datasets and aims to identify with high confidence underlying biological associations. Our computational approach consists of four core parts. In the first part, we analyzed amino acid datasets of membrane transporters from different organisms for the purpose of transferring functional annotations of the transporters across species. Here, we mapped the experimentally validated functions of one protein to another one from a different organism based on their sequence similarity. Sequence similarity results in this work were combinations of similarity decisions of several tools (BLAST, HMMER, MEME). Initially, we defined confidence thresholds and which we then applied for predictions. We found that, up to certain thresholds, membrane transporters sharing high sequence similarity have similar functions or transporting mechanisms even if they exist in different organisms. Our second computational approach was designed to deal with expression and methylation datasets. We found that expression and methylation datasets often suffer from outliers at gene or sample levels. Performing analyses before dealing with outliers might lead to misleading results. Thus, we present an approach that includes several outlier detection algorithms for detecting sample and gene outliers in expression/methylation datasets. As some outlier algorithms report at least one outlier value even if there is none, we first defined the margin of allowed outlier observations. We tested how many outlier observations are needed to ruin a perfect co-expression and then fixed that threshold for the rest of analyses. Additionally, in this work we considered the distribution underlying he gene expression/methylation before outlier detection. However, outliers might carry useful information. Therefore, we labelled only extreme outliers for removal and marked those possibly carrying useful information for further analysis. In the next step, we used published expression and methylation datasets from GEO to analyse and confirm possible tumor markers for HCC, liver diseases, and breast cancer. These were later validated in the wetlab through our collaboration with the group of Prof. Kiemer in pharmacy. In addition to their possible roles in the change of survival rates, we also tested the role of several possible markers in tumor initiation and progression. The final part of this thesis dealt with large scale exon expression, methylation, and chromatin modification datasets for 11 different developmental stages from the Human Epigenome Atlas. Our aim in this genome wide analysis was to identify cases of differential exon usage in different dataset. Our findings suggested a set of strong associations of epigenetic modifications and alternative splicing especially in early human developmental stages. In summary, the combination of the approaches presented in this thesis may advance the current stages of tumor marker identification. Membrane transporters play key roles in cancer progression. Once their function is defined with the help of similar transporters in other organisms, one may compare their expression and methylation profiles in normal and tumor tissues. The expression/methylation datasets should be cleared first from outliers. Once a tumor marker is defined or confirmed, further analysis is suggested especially for possible different splice variants.Biologische Datensätze aus Hochdurchsatzverfahren sind meist die Basis zeitgemäßer Grundlagenforschung in Genomik, Systembiologie und Krankheitsdiagnstik. Eine Probe kann in manchen Datensätzen momentan tausende Messungen umfassen. Die Allgegenwärtigkeit solch enormer Datenmengen brachte den dringenden Bedarf an effizienten und robusten computergestützten Ansätzen mit sich, die diese Daten verarbeiten und analysieren können und die informative Assoziationen ermitteln. Diese Arbeit beschäftigt sich mit unterschiedlichen Arten von umfangreichen Datensätzen und beabsichtigt zu Grunde liegende biologische Zusammenhänge mit hoher Zuverlässigkeit zu erkennen. Unsere Methodik besteht aus vier Kernteilen. Im ersten Teil analysierten wir Aminosäure-Daten von Transporterproteinen aus verschiedenen Organismen um funktionelle Annotierungen der Membranproteine speziesübergreifend transferieren zu können. In unserem Fall bildeten wir anhand der Sequenzähnlichkeit die experimentell validierte Funktionen eines Proteins auf ein anderes aus einem anderen Organismus ab. Die Sequenzähnlichkeit in dieser Studie war eine Kombination aus Ähnlichkeitsmaßen verschiedener Softwarewerkzeuge (BLAST, HMMER, MEME). Zuerst definierten wir Vertrauensgrenzwerte (für besagte Werkzeuge) die wir dann für die Vorhersage anwendeten. Wir fanden heraus, dass Membrantransporter mit hoher Sequenzähnlichkeit bis zu gewissen Schwellenwerten sogar dann ähnliche Funktionen oder Transportmechanismen haben wenn sie aus unterschiedlichen Organismen stammen. Unser zweiter rechnergestützter Ansatz wurde entworfen um Expressions- und Methylierungsdaten zu handhaben. Wir sahen, dass diese Daten oft durch Ausreißer auf Gen- oder Probenebene in Mitleidenschaften gezogen werden. Das Durchführen von Untersuchungen vor einer Bereinigung dieser Ausreißer kann irreführende Ergebnisse zur Folge haben. Daher bieten wir eine Methode die mehrere Ausreißererkennungsalgorithmen beinhaltet um Proben- und Gensonderfälle in Expressions-/Methylierungsdatensätzen zu erkennen. Da einige Ausreißererkennungsmethoden auch dann zumindest einen Ausreißer melden wenn eigentlich keiner vorhanden ist, legten wir zuerst einen Grenzwert für erlaubte Ausnahmefälle fest. Wir prüften wie viele Ausreißerbeobachtungen benötigt wurden um perfekte Koexpression zunichte zu machen und setzten diesen Grenzwert dann für die verbleibende Analyse fest. Zusätzlich haben wir in dieser Arbeit die Verteilung von Genexprimierung/Methylierung vor der Ausreißererkennung bedacht. Dennoch könnten Ausreißer dienliche Information mit sich bringen. Daher markierten wir nur extreme Ausreißer explizit zur Entfernung und solche, die für weitere Untersuchungen potentiell nützliche Information beinhalteten, markierten wir gesondert. Im nächsten Schritt nutzten wir publizierte Expressions- und Methylierungsdatensätze von GEO um mögliche Tumormarker für HCC, Leberkrankheiten und Brustkrebs zu analysieren und zu bestätigen. Diese wurden später durch unsere pharmazeutischen Kollaborationspartner der Gruppe von Prof. Kiemer im Labor validiert. Zusätzlich zu ihren eventuellen Rollen in der Veränderung von Überlebensraten haben wir auch die Funktion mehrerer möglicher Marker bezüglich Tumorinitiierung- und progression untersucht. Der letzte Teil dieser Arbeit befasste sich mit umfangreichen Datensätzen für Exonexpression, Methylierung und Chromatinmodifikationen über 11 verschiedenen Entwicklungsstadien aus dem Human Epigenome Atlas. In dieser genomweiten Untersuchung war es unser Ziel Fälle von veränderter Exonnutzung in verschiedenen Datensätzen zu finden. Unsere Resultate legen insbesondere in frühen menschlichen Entwicklungsstadien einige gewichtige Zusammenhänge zwischen epigenetischen Modifikationen und alternativem Spleißen nahe. Zusammenfassend lässt sich sagen, dass die Kombination der hier präsentierten Ansätze gegenwärtige Stufen der Tumormarkererkennung beschleunigen/verbessern könnte. Membrantransporter haben Schlüsselrollen in der Krebsprogression inne. Sobald ihre Funktion mit der Hilfe ähnlicher Transporter in anderen Lebewesen aufgeklärt ist, könnte man ihre Expressions- und Methylierungsverläufe in gesundem und in Tumorgewebe vergleichen. Die Expressions/Methylierungsdaten sollten hierbei erst von Aureißern bereinigt werden. Sobald ein Tumormarker definiert oder bestätigt ist, ist weitere Untersuchung insbesondere im Hinblick auf verschiedene Spleißvarianten angeraten