Statistical methods in detecting differential expressed genes, analyzing insertion tolerance for genes and group selection for survival data

The thesis is composed of three independent projects: (i) analyzing transposon-sequencing data to infer functions of genes on bacteria growth (chapter 2), (ii) developing semi-parametric Bayesian method method for differential gene expression analysis with RNA-sequencing data (chapter 3), (iii) solving group selection problem for survival data (chapter 4). All projects are motivated by statistical challenges raised in biological research. The first project is motivated by the need to develop statistical models to accommodate the transposon insertion sequencing (Tn-Seq) data, Tn-Seq data consist of sequence reads around each transposon insertion site. The detection of transposon insertion at a given site indicates that the disruption of genomic sequence at this site does not cause essential function loss and the bacteria can still grow. Hence, such measurements have been used to infer the functions of each gene on bacteria growth. We propose a zero-inflated Poisson regression method for analyzing the Tn-Seq count data, and derive an Expectation-Maximization (EM) algorithm to obtain parameter estimates. We also propose a multiple testing procedure that categorizes genes into each of the three states, hypo-tolerant, tolerant, and hyper-tolerant, while controlling false discovery rate. Simulation studies show our method provides good estimation of model parameters and inference on gene functions. In the second project, we model the count data from RNA-sequencing experiment for each gene using a Poisson-Gamma hierarchical model, or equivalently, a negative binomial (NB) model. We derive a full semi-parametric Bayesian approach with Dirichlet process as the prior for the fold changes between two treatment means. An inference strategy using Gibbs algorithm is developed for differential expression analysis. We evaluate our method with several simulation studies, and the results demonstrate that our method outperforms other methods including the popularly applied ones such as edgeR and DESeq. In the third project, we develop a new semi-parametric Bayesian method to address the group variable selection problem and study the dependence of survival outcomes on the grouped predictors using the Cox proportional hazard model. We use indicators for groups to induce sparseness and obtain the posterior inclusion probability for each group. Bayes factors are used to evaluate whether the groups should be selected or not. We compare our method with one frequentist method (HPCox) based on several simulation studies and show that our method performs better than HPCox method. In summary, this dissertation tackles several statistical problems raised in biological research, including high-dimensional genomic data analysis and survival analysis. All proposed methods are evaluated with simulation studies and show satisfactory performances. We also apply the proposed methods to real data analysis

Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression

BACKGROUND: Deep sequencing of transposon mutant libraries (or TnSeq) is a powerful method for probing essentiality of genomic loci under different environmental conditions. Various analytical methods have been described for identifying conditionally essential genes whose tolerance for insertions varies between two conditions. However, for large-scale experiments involving many conditions, a method is needed for identifying genes that exhibit significant variability in insertions across multiple conditions. RESULTS: In this paper, we introduce a novel statistical method for identifying genes with significant variability of insertion counts across multiple conditions based on Zero-Inflated Negative Binomial (ZINB) regression. Using likelihood ratio tests, we show that the ZINB distribution fits TnSeq data better than either ANOVA or a Negative Binomial (in a generalized linear model). We use ZINB regression to identify genes required for infection of M. tuberculosis H37Rv in C57BL/6 mice. We also use ZINB to perform a analysis of genes conditionally essential in H37Rv cultures exposed to multiple antibiotics. CONCLUSIONS: Our results show that, not only does ZINB generally identify most of the genes found by pairwise resampling (and vastly out-performs ANOVA), but it also identifies additional genes where variability is detectable only when the magnitudes of insertion counts are treated separately from local differences in saturation, as in the ZINB model

TnseqDiff: identification of conditionally essential genes in transposon sequencing studies

Abstract Background Tn-Seq is a high throughput technique for analysis of transposon mutant libraries to determine conditional essentiality of a gene under an experimental condition. A special feature of the Tn-seq data is that multiple mutants in a gene provides independent evidence to prioritize that gene as being essential. The existing methods do not account for this feature or rely on a high-density transposon library. Moreover, these methods are unable to accommodate complex designs. Results The method proposed here is specifically designed for the analysis of Tn-Seq data. It utilizes two steps to estimate the conditional essentiality for each gene in the genome. First, it collects evidence of conditional essentiality for each insertion by comparing read counts of that insertion between conditions. Second, it combines insertion-level evidence for the corresponding gene. It deals with data from both low- and high-density transposon libraries and accommodates complex designs. Moreover, it is very fast to implement. The performance of the proposed method was tested on simulated data and experimental Tn-Seq data from Serratia marcescens transposon mutant library used to identify genes that contribute to fitness in a murine model of infection. Conclusion We describe a new, efficient method for identifying conditionally essential genes in Tn-Seq experiments with high detection sensitivity and specificity. It is implemented as TnseqDiff function in R package Tnseq and can be installed from the Comprehensive R Archive Network, CRAN.https://deepblue.lib.umich.edu/bitstream/2027.42/137673/1/12859_2017_Article_1745.pd

Fitness Landscape of the Fission Yeast Genome

The relationship between DNA sequence, biochemical function and molecular evolution is relatively well-described for protein-coding regions of genomes, but far less clear in non-coding regions, particularly in eukaryote genomes. In part, this is because we lack a complete description of the essential non-coding elements in a eukaryote genome. To contribute to this challenge, we used saturating transposon mutagenesis to interrogate the Schizosaccharomyces pombe genome. We generated 31 million transposon insertions, a theoretical coverage of 2.4 insertions per genomic site. We applied a five-state hidden Markov model (HMM) to distinguish insertion-depleted regions from insertion biases. Both raw insertion-density and HMM-defined fitness estimates showed significant quantitative relationships to gene knockout fitness, genetic diversity, divergence and expected functional regions based on transcription and gene annotations. Through several analyses, we conclude that transposon insertions produced fitness effects in 66-90% of the genome, including substantial portions of the non-coding regions. Based on the HMM, we estimate that 10% of the insertion depleted sites in the genome showed no signal of conservation between species and were weakly transcribed, demonstrating limitations of comparative genomics and transcriptomics to detect functional units. In this species, 3' and 5' untranslated regions were the most prominent insertion-depleted regions that were not represented in measures of constraint from comparative genomics. We conclude that the combination of transposon mutagenesis, evolutionary and biochemical data can provide new insights into the relationship between genome function and molecular evolution

Fitness Landscape of the Fission Yeast Genome.

The relationship between DNA sequence, biochemical function, and molecular evolution is relatively well-described for protein-coding regions of genomes, but far less clear in noncoding regions, particularly, in eukaryote genomes. In part, this is because we lack a complete description of the essential noncoding elements in a eukaryote genome. To contribute to this challenge, we used saturating transposon mutagenesis to interrogate the Schizosaccharomyces pombe genome. We generated 31 million transposon insertions, a theoretical coverage of 2.4 insertions per genomic site. We applied a five-state hidden Markov model (HMM) to distinguish insertion-depleted regions from insertion biases. Both raw insertion-density and HMM-defined fitness estimates showed significant quantitative relationships to gene knockout fitness, genetic diversity, divergence, and expected functional regions based on transcription and gene annotations. Through several analyses, we conclude that transposon insertions produced fitness effects in 66-90% of the genome, including substantial portions of the noncoding regions. Based on the HMM, we estimate that 10% of the insertion depleted sites in the genome showed no signal of conservation between species and were weakly transcribed, demonstrating limitations of comparative genomics and transcriptomics to detect functional units. In this species, 3'- and 5'-untranslated regions were the most prominent insertion-depleted regions that were not represented in measures of constraint from comparative genomics. We conclude that the combination of transposon mutagenesis, evolutionary, and biochemical data can provide new insights into the relationship between genome function and molecular evolution

A single-cell multi-omic approach to the analysis of T cell differentiation

This thesis aims to investigate T cell differentiation through the bioinformatic analysis of single-cell multi-omic data. T cells are an important part of the adaptive immune system, involved in the immune response to infections and cancer. Single-cell technologies have advanced to the point where multiple modes of data, such as gene and protein expression, can be assayed on the same cells. Greater understanding of T cell differentiation pathways at the single-cell level can help in the design of immunotherapies to treat cancer and autoimmune disease. The thesis begins by presenting a multi-omic workflow that combines scRNA-seq and T cell receptor (TCR) sequence extraction. The principles developed for this workflow were applied to investigate T cell differentiation in two scenarios. The first scenario was an application of single-cell multi-omics to the study of CD8+ T cell peripheral tolerance mechanisms in a mouse model. This work demonstrated that tolerance is a distinct differentiation program to functional effector responses, and T cells progressively commit to the tolerised state over the first 60hrs post exposure to triggering antigen. A gene signature for the tolerised state was identified, containing genes uniquely upregulated in tolerised cells. Quiescent and Proliferating clusters were found in tolerised cells, indicating that a proportion of cells exit cell cycle within each division. The second scenario was an investigation of the differentiation of CD4+ CAR T cells in vivo, and the evolution of a lymphoma derived from these cells. Three cell types, proliferating, cytotoxic and resting, were observed within the malignant CAR T-cells, and these types were also observed within non-malignant CAR T and endogenous CD4+ T cells. The lymphoma was characterised by expression of the NF-κB transcription factor in all three cell types, while each cell type had differing expression levels for several other known oncogenes. This thesis has contributed to the understanding of T cell differentiation in tolerance and CAR T therapy, and has helped meet the challenge of increasingly large and complex single- cell datasets through the development of bioinformatic workflows to integrate samples from multiple patients and sequencing technologies, and integrate gene, protein, TCR sequence, cell division count and somatic mutation data at the single-cell level

Pemodelan Indikator Kejadian Penyakit Tetanus Neonatorum Pada Bayi Di Indonesia Menggunakan Regresi Zero-Inflated Poisson

Salah satu penyebab tingginya Angka Kematian Bayi (AKB) di Indonesia adalah penyakit Tetanus Neonatorum. Penyakit tersebut disebabkan oleh infeksi bakteri Clostridium tetani yang menyerang pada bayi usia ke-3 dan 28 setelah kelahiran melalui pemotongan tali pusat dengan alat yang tidak steril. Jumlah kasus Tetanus Neonatorum merupakan data count atau jumlahan dengan asumsi mengikuti distribusi Poisson, sehingga akan dianalisis dengan menggunakan regresi Poisson. Namun regresi Poisson tidak sesuai apabila digunakan pada kasus Tetanus Neonatorum yang observasinya terdapat nilai nol yang banyak, karena akan melanggar asumsi equidispersi. Untuk itu pada penelitian ini, akan digunakan metode regresi lainnya yaitu Zero-Inflated Poisson (ZIP) karena proporsi nilai nol pada data sangat besar yaitu 57,6 persen. Data yang digunakan dalam penelitian tugas akhir ini adalah data sekunder yang diambil dari Profil Kesehatan Indonesia tahun 2013. Dari hasil penelitian diperoleh bahwa pemodelan dengan regresi Poisson terindikasi terjadi overdispersi, sehingga kasus tersebut diselesaikan dengan regresi Zero-Inflated Poisson. Pada model regresi Zero-Inflated Poisson (ZIP) variabel prediktor yang mempengaruhi poisson state adalah variabel persentase cakupan imunisasi TT2+ terhadap jumlah ibu hamil, persentase jumlah tenaga kesehatan terhadap jumlah bayi, dan persentase penolong persalinan dilakukan oleh tenaga kesehatan. Sedangkan variabel prediktor yang mempengaruhi zero state adalah variabel persentase jumlah tenaga kesehatan terhadap jumlah bayi

Environmental activity of the inoculant Pseudomonas veronii 1YdBTEX2 during bioaugmentation in natural and polluted soils

SUMMARY Bioaugmentation uses the capacities of specific bacterial strains inoculated into sites to enhance pollutant biodegradation. Bioaugmentation mainly involves introducing bacteria that deploy their metabolic properties and adaptation potential to survive and propagate in the contaminated environment using the target pollutant as a carbon, nutrient or energy source. Most of our understanding of biodegradation activity comes from experiments carried out under laboratory conditions. However, attempts to demonstrate the potential for bioaugmentation of biodegraders under in situ conditions have shown both success and failures, without clear understanding of the underlying reasons or mechanisms. We hypothesized that a better understanding is necessary of the processes occurring during the invasion of inoculants into complex environments, such as polluted soils. Only then can we identify the bottlenecks for bioaugmentation and fully exploit the natural diversity of biodegrader candidates. In this context, the overall aim of this work was to rationally study the soil survival capacity and contrast the metabolic and physiological strategies of the BTEX-degrading bacterium Pseudomonas veronii 1YdBTEX2 during adaptation and invasion to natural non-sterile and contaminated soils, with that in polluted liquid suspended cultures or standardized porous materials. In the general introduction, I present the state of the art in soil bioremediation and bioaugmentation, and degradation of BTEX compounds in particular. In the first research chapter, I focus on understanding the adaptive response of P. veronii 1YdBTEX2 during transition from liquid batch culture to contaminated soil. For this, I analyzed the short-term (1 h) changes in genome-wide gene expression in non-sterile sand compared to liquid medium, both in presence or absence of toluene. In a collaborative effort, we improved the genome sequence of P. veronii 1YdBETX2, and showed that it actually covers three individual replicons with a total size of 8 Mb, with a large proportion of genes with unknown functions. One-hour exposure to toluene, both in soil and liquid, triggered massive transcription (up to 208-fold induction) of multiple gene clusters, such as toluene degradation pathway(s), chemotaxis, and toluene efflux pumps. This clearly underlines their key role in the adaptive response to toluene. In comparison to liquid medium, cells in soil drastically changed the expression of genes involved in membrane functioning (e.g., lipid composition, lipid metabolism, cell fatty acid synthesis), osmotic stress response (e.g., polyamine or trehalose synthesis, uptake of potassium), and putrescine metabolism, highlighting the adaptive mechanisms of P. veronii to readjust its metabolism to sand. In the next chapter, I focused on understanding the potential changes in the P. veronii global transcriptome during actual growth and survival (stationary phase) in contaminated soils. In addition, I aimed to investigate differences in behaviour among three different non-sterile soils and a historically contaminated material with (polycyclic) aromatic hydrocarbons. I showed that P. veronii established itself in all except one soil, at the expense of toluene. The strain also grew in its absence of toluene but to a lower population density. This indicates its capacity to survive in real field conditions under contamination stress. I compared the transcriptomic responses during transition phase, exponential growth and stationary phase among the soils and to the behaviour induced in regular liquid culture or inert silica matrix. The transcriptomic analysis revealed that P. veronii displayed a versatile global program, characterized by commonly shared and soil-specific strategies, and signs of nutrient limitations in later growth phases, which explain its behaviour in the soil environments. We also observed a strikingly conserved core metabolic expression in the exponential phase irrespective of the growth environment. The non-growth in one soil that carried the highest resident background microbial community may be the result of predation by higher loads of resident protists, and/or of possible competition for substrate by resident microbes. In the absence of sizeable P. veronii population, however, I did not manage to isolate sufficient RNA for transcriptomics, and signs of predatory or competition stress could thus not be uncovered. Finally, in the third chapter, I focused on characterizing the genes that might be important for growth and survival of P. veronii in (contaminated) soils. For this I used a transposon-insertion scanning approach. With help I generated two independent genome-wide insertion libraries of P. veronii through conjugation of a non-replicating hyperactive mini-Tn5 delivering an aph gene insertion (Kanamycin resistance gene). I inoculated the libraries then either in liquid suspended culture media or in two different soils under presence of toluene. Libraries were propagated for 50 generations in the same environments. Deep sequencing of amplified boundaries between the inserted aph- and host genes, served to quantify the relative abundances of insertions over time and condition. Fitness importance of genes for soil growth and survival was assessed from de- or increases of the relative abundance of insertions over generation times, and compared to random insertion models. Apart from typically considered ‘essential’ genes that were mostly absent already in the starting libraries, our data showed only a small number of genes implicated in fitness loss, that were overlapping between both soils and absent from liquid growth. These pointed to crucial functions of urea and short-chain fatty acid metabolism, oxidative stress defense, and nutrient/element transport for soil growth. Our analysis also showed that mutants inactivating flagella biosynthesis and motility had a strong fitness gain in soils. Long-term soil growth and survival of P. veronii in soils is thus a function of a variety of independent genes, but not necessarily of a coherent ‘soil-specific’ growth program. The results obtained in my thesis enabled to draw a global picture of the natural behavior, strategies, and capabilities of P. veronii to grow and survive under environmentally relevant conditions, such as posed by non-sterile and polluted soils. By comparing transcriptomic responses in different soils and materials, together with the transposon mutagenesis scanning at different growth phases, I am confident to have covered a broad range of conditions and scenarios that help our understanding of the mechanisms necessary for strain adaptation and survival upon inoculation. The integration of the results obtained throughout the above-described chapters showed that P. veronii is a robust strain. Its fast adaptability is explained by the large and redundant genome, which encodes several mechanisms to maintain its main central core metabolism despite the environment it is inoculated and to adjust efficiently to the constantly changing soil conditions. Finally, this work also shows evidence of possible reasons for which P. veronii would not be able to invade complex microbiomes. RÉSUMÉ La bioaugmentation utilise les capacités de certaines bactéries inoculées dans un site contaminé pour améliorer la dégradation de polluants. Ce procédé implique l’introduction des bactéries qui peuvent exercer leurs propriétés métaboliques et d’adaptation pour survivre et prospérer dans le site contaminé en utilisant le polluant comme source de carbone, nutriment et énergie. La plupart des connaissances à propos de l’activité de biodégradation proviennent des expériences menées en conditions de laboratoire. Cependant, les tentatives pour démontrer le potentiel pour la bioaugmentation de ces bactéries sur site ont montré des réussites, mais aussi des échecs, sans vraiment que l’on comprenne les raisons ou mécanismes de ces résultats. Nous croyons nécessaire une meilleure compréhension des processus qui ont lieu pendant l’invasion des bactéries inoculées dans des environnements complexes comme le sont les sites contaminés. De cette façon, on pourrait identifier les « problèmes » de la bioaugmentation et pouvoir ainsi exploiter au mieux la diversité naturelle des bactéries capables de dégrader des polluants. Dans ce contexte, le principal objectif de ce travail était d’étudier de façon rationnelle la capacité de survie et les stratégies métaboliques et physiologiques de la bactérie Pseudomonas veronii 1YdBTEX2 (qui dégradent les BTEX). On les a comparé pendant l'adaptation et l'invasion de cette bactérie dans les sols naturels non stériles et contaminés, avec celle des cultures en suspension dans des liquides pollués ou des matériaux poreux standardisés. Dans l’introduction général, je présente l’état des connaissances en bioremédiation de sols et le concept du bioaugmentation. En particulier je décris la dégradation de composants BTEX (Benzène, Toluène, Éthylbenzène et Xylènes), qui sont des substances volatiles très toxiques. Dans le premier chapitre de recherche, je me focalise sur la compréhension des réponses adaptatives de P. veronii 1YdBTEX2 pendant la transition de cultures liquides à des sols contaminés. Dans cette optique, j’ai étudié les changements à court terme (1 heure) de l’expression de gènes des bactéries inoculées dans le sable non stérile, versus des cultures liquides ; dans les deux cas, en présence ou absence de toluène. Au cours d’un travail collaboratif, nous avons amélioré la séquence génomique de P. veronii 1YdBTEX2 et nous avons montré qu’en réalité, il est composé de trois réplicons individuels avec une taille de 8 Mb et qui inclut une large proportion de gènes avec des fonctions inconnues. Une heure d’exposition au toluène, en culture liquide ou au sol, déclenche une transcription massive (induction jusqu’à 208 fois) de multiples groupes de gènes, comme codant par exemple les voies métaboliques de dégradation du toluène, la chimiotaxie ou les pompes d’efflux de toluène. Ces résultats montrent clairement le rôle clé d’une réponse adaptative au toluène. Comparativement au milieu liquide, les cellules dans le sol changent drastiquement l’expression des gènes impliqués dans le fonctionnement de la membrane (comme par exemple, la composition et métabolisme des graisses et la synthèse des acides gras dans la cellule), la réponse au stress osmotique (par exemple, la synthèse de polyamines ou tréhalose, et l’absorption de potassium) et le métabolisme de la putrescine, mettant en évidence les mécanismes adaptatives de P. veronii pour réajuster son métabolisme au sable. Dans le chapitre suivant je tente de comprendre le changements du transcriptome global de P. veronii pendant la croissance et survie à long terme (phase stationaire) dans des sols contaminés. En outre, j’ai cherché à étudier les différences de comportement dans 3 types de sols non stériles et un « sol » historiquement contaminé avec des hydrocarbures aromatiques polycycliques. J’ai constaté que P. veronii arrive à s’établir dans tous, sauf un, au détriment du toluène. La souche croit aussi en absence de toluène, mais avec une densité de population moindre. Ces résultats montrent une capacité de survie dans des conditions réelles de terrain, sous le stress de la contamination. J’ai comparé les réponses du transcriptome pendant la phase de transition (court terme) ainsi que la croissance exponentielle et stationnaire dans le sol avec le comportement induit dans les cultures liquides ou dans une matrice de silice inerte. L’analyse révèle un programme global et versatile de P. veronii, caractérisé par des stratégies communes ou spécifiques au sol et des évidences de limitation de nutriments dans les phases de croissance tardives, ce qui explique son comportement dans les environnements du sol. Nous avons aussi observé une expression métabolique centrale remarquablement conservée dans la phase exponentielle, sans distinction de l’environnement de croissance. L’absence de croissance dans un des sols, qui contenait une communauté complexe de microbes, est peu être le résultat de prédation de la part d’une abondante présence de protistes résidants et/ou une possible compétition pour le substrat par les microbes déjà présents. En raison de la quantié insuffisante de P. veronii pour extraire suffisamment d’ARN pour l’analyse du transcriptome, des signes de prédation ou de stress compétitif n’ont pas pu être élucidés. Finalement, dans le quatrième chapitre, j’ai caractérisé les gènes qui pourraient être importants pour la croissance et la survie de P. veronii dans les sols (contaminés). Pour ce faire, j’ai utilisé la technique des insertions massives de transposons. De cette manière, on a pu générer deux librairies indépendantes d’insertions de transposons dans tout le génome de P. veronii à travers l’utilisation d’un transposon mini-Tn5 hyperactive non réplicatif avec un gène (aph) qui confère de la résistance a la kanamycine. Ces librairies on été inoculées dans un milieu de culture liquide et dans deux sols différents en présence de toluène et on les a laissées se propager pendant 50 générations dans le même environnement. Le séquençage profond des limites amplifiées entre l’insertion du gène aph et les gènes de la bactérie ont servi à quantifier la relative abondance des insertions en fonction du temps et des conditions de croissance. L'importance de la valeur adaptative des gènes pour la croissance et la survie au sol a été évaluée à partir de la diminution ou de l'augmentation de l'abondance relative des insertions au cours des générations, et comparée à des modèles d'insertion aléatoire. En excluant les gènes typiquement considérés comme essentiels qui ont été majoritairement absents dans les librairies de départ, nos résultats montrent seulement un petit nombre de gènes impliqués dans la perte de valeur adaptative communs aux deux sols et absents de la culture liquide. Ces résultats indiquent des fonctions cruciales du métabolisme de l’urée et des acides gras à chaîne courte, de la défense contre le stress oxydatif et du transport des nutriments/éléments pour la croissance au sol. Notre analyse a aussi montré que les mutants qui inactivent la biosynthèse des flagelles et la motilité ont un fort gain de valeur adaptative dans le sol. La croissance et survie à long terme de P. veronii dans le sol est plutôt en fonction d’une variété des gènes indépendants et pas nécessairement d’un programme cohérent spécifique au sol. Les résultats obtenus dans ma thèse permettent de dessiner un tableau global du comportement naturel, des stratégies et de la capacité de P. veronii à croitre et à survivre dans des conditions environnementales particulières telles que celles des sols non stériles et pollués. En comparant les réponses transcriptomiques dans diffèrent sols et matériaux et le scan de mutants par transposon en différentes phases de croissance, je pense avoir couvert une ample variété de conditions et de scenarii qui aident à notre compression des mécanismes nécessaires à l’adaptation de la souche et à la survie après inoculation. L’intégration de l’ensemble des résultats montrent que P.veronii est une souche robuste, sa prompte adaptabilité est expliquée par son large et redondant génome qui code plusieurs mécanismes pour maintenir son métabolisme principal, malgré l’environnement où il est inoculé, en s’ajustant de manière efficace aux conditions changeantes du sol. Finalement, ce travail met en évidence des possibles raisons pour lesquelles P. veronii ne serait pas en mesure d’envahir des microbiomes complexes

Understanding transcriptional regulation through computational analysis of single-cell transcriptomics

Gene expression is tightly regulated by complex transcriptional regulatory mechanisms to achieve specific expression patterns, which are essential to facilitate important biological processes such as embryonic development. Dysregulation of gene expression can lead to diseases such as cancers. A better understanding of the transcriptional regulation will therefore not only advance the understanding of fundamental biological processes, but also provide mechanistic insights into diseases. The earlier versions of high-throughput expression profiling techniques were limited to measuring average gene expression across large pools of cells. In contrast, recent technological improvements have made it possible to perform expression profiling in single cells. Single-cell expression profiling is able to capture heterogeneity among single cells, which is not possible in conventional bulk expression profiling. In my PhD, I focus on developing new algorithms, as well as benchmarking and utilising existing algorithms to study the transcriptomes of various biological systems using single-cell expression data. I have developed two different single-cell specific network inference algorithms, BTR and SPVAR, which are based on two different formalisms, Boolean and autoregression frameworks respectively. BTR was shown to be useful for improving existing Boolean models with single-cell expression data, while SPVAR was shown to be a conservative predictor of gene interactions using pseudotime-ordered single-cell expression data. In addition, I have obtained novel biological insights by analysing single-cell RNAseq data from the epiblast stem cells reprogramming and the leukaemia systems. Three different driver genes, namely Esrrb, Klf2 and GY118F, were shown to drive reprogramming of epiblast stem cells via different reprogramming routes. As for the leukaemia system, FLT3-ITD and IDH1-R132H mutations were shown to interact with each other and potentially predispose some cells for developing acute myeloid leukaemia.Wellcome Trust and Cambridge Trus

The Marvelous World of tRNAs: From Accurate Mapping to Chemical Modifications

Since the discovery of transfer RNAs (tRNAs) as decoders of the genetic code, life science has transformed. Particularly, as soon as the importance of tRNAs in protein synthesis has been established, researchers recognized that the functionality of tRNAs in cellular regulation exceeds beyond this paradigm. A strong impetus for these discoveries came from advances in large-scale RNA sequencing (RNA-seq) and increasingly sophisticated algorithms. Sequencing tRNAs is challenging both experimentally and in terms of the subsequent computational analysis. In RNA-seq data analysis, mapping tRNA reads to a reference genome is an error-prone task. This is in particular true, as chemical modifications introduce systematic reverse transcription errors while at the same time the genomic loci are only approximately identical due to the post-transcriptional maturation of tRNAs. Additionally, their multi-copy nature complicates the precise read assignment to its true genomic origin. In the course of the thesis a computational workflow was established to enable accurate mapping of tRNA reads. The developed method removes most of the mapping artifacts introduced by simpler mapping schemes, as demonstrated by using both simulated and human RNA-seq data. Subsequently, the resulting mapping profiles can be used for reliable identification of specific chemical tRNA modifications with a false discovery rate of only 2%. For that purpose, computational analysis methods were developed that facilitates the sensitive detection and even classification of most tRNA modifications based on their mapping profiles. This comprised both untreated RNA-seq data of various species, as well as treated data of Bacillus subtilis that has been designed to display modifications in a specific read-out in the mapping profile. The discussion focuses on sources of artifacts that complicate the profiling of tRNA modifications and strategies to overcome them. Exemplary studies on the modification pattern of different human tissues and the developmental stages of Dictyostelium discoideum were carried out. These suggested regulatory functions of tRNA modifications in development and during cell differentiation. The main experimental difficulties of tRNA sequencing are caused by extensive, stable secondary structures and the presence of chemical modifications. Current RNA-seq methods do not sample the entire tRNA pool, lose short tRNA fragments, or they lack specificity for tRNAs. Within this thesis, the benchmark and improvement of LOTTE-seq, a method for specific selection of tRNAs for high-throughput sequencing, exhibited that the method solves the experimental challenges and avoids the disadvantages of previous tRNA-seq protocols. Applying the accurate tRNA mapping strategy to LOTTE-seq and other tRNA-specific RNA- seq methods demonstrated that the content of mature tRNAs is highest in LOTTE-seq data, ranging from 90% in Spinacia oleracea to 100% in D. discoideum. Additionally, the thesis addressed the fact that tRNAs are multi-copy genes that undergo concerted evolution which keeps sequences of paralogous genes effectively identical. Therefore, it is impossible to distinguish orthologs from paralogs by sequence similarity alone. Synteny, the maintenance of relative genomic positions, is helpful to disambiguate evolutionary relationships in this situation. During this thesis a workflow was computed for synteny-based orthology identification of tRNA genes. The workflow is based on the use of pre-computed genome-wide multiple sequence alignment blocks as anchors to establish syntenic conservation of sequence intervals. Syntenic clusters of concertedly evolving genes of different tRNA families are then subdivided and processed by cograph editing to recover their duplication histories. A useful outcome of this study is that it highlights the technical problems and difficulties associated with an accurate analysis of the evolution of multi-copy genes. To showcase the method, evolution of tRNAs in primates and fruit flies were reconstructed. In the last decade, a number of reports have described novel aspects of tRNAs in terms of the diversity of their genes. For example, nuclear-encoded mitochondrial-derived tRNAs (nm-tRNAs) have been reported whose presence provokes intriguing questions about their functionality. Within this thesis an annotation strategy was developed that led to the identification of 335 and 43 novel nm-tRNAs in human and mouse, respectively. Interestingly, downstream analyses showed that the localization of several nm-tRNAs in introns and the over-representation of conserved RNA-binding sites of proteins involved in splicing suggest a potential regulatory function of intronic nm-tRNAs in splicing