The detection of non-protein-coding RNA (ncRNA) genes in bacteria and their diverse regulatory mode of action moved the experimental and bio-computational analysis of ncRNAs into the focus of attention. Regulatory ncRNA transcripts are not translated to proteins but function directly on the RNA level. These typically small RNAs have been found to be involved in diverse processes such as (post-)transcriptional regulation and modification, translation, protein translocation, protein degradation and sequestration. Bacterial ncRNAs either arise from independent primary transcripts or their mature sequence is generated via processing from a precursor. Besides these autonomous transcripts, RNA regulators (e.g. riboswitches and RNA thermometers) also form chimera with protein-coding sequences. These structured regulatory elements are encoded within the messenger RNA and directly regulate the expression of their “host” gene. The quality and completeness of genome annotation is essential for all subsequent analyses. In contrast to protein-coding genes ncRNAs lack clear statistical signals on the sequence level. Thus, sophisticated tools have been developed to automatically identify ncRNA genes. Unfortunately, these tools are not part of generic genome annotation pipelines and therefore computational searches for known ncRNA genes are the starting point of each study. Moreover, prokaryotic genome annotation lacks essential features of protein-coding genes. Many known ncRNAs regulate translation via base-pairing to the 5’ UTR (untranslated region) of mRNA transcripts. Eukaryotic 5’ UTRs have been routinely annotated by sequencing of ESTs (expressed sequence tags) for more than a decade. Only recently, experimental setups have been developed to systematically identify these elements on a genome-wide scale in prokaryotes. The first part of this thesis, describes three experimental surveys of exploratory field studies to analyze transcript organization in pathogenic bacteria. To identify ncRNAs in Pseudomonas aeruginosa we used a combination of an experimental RNomics approach and ncRNA prediction. Besides already known ncRNAs we identified and validated the expression of six novel RNA genes. Global detection of transcripts by next generation RNA sequencing techniques unraveled an unexpectedly complex transcript organization in many bacteria. These ultra high-throughput methods give us the appealing opportunity to analyze the complete RNA output of any species at once. The development of the differential RNA sequencing (dRNA-seq) approach enabled us to analyze the primary transcriptome of Helicobacter pylori and Xanthomonas campestris. For the first time we generated a comprehensive and precise transcription start site (TSS) map for both species and provide a general framework for the analysis of dRNA-seq data. Focusing on computer-aided analysis we developed new tools to annotate TSS, detect small protein-coding genes and to infer homology of newly detected transcripts. We discovered hundreds of TSS in intergenic regions, upstream of protein-coding genes, within operons and antisense to annotated genes. Analysis of 5’ UTRs (spanning from the TSS to the start codon of the adjacent protein-coding gene) revealed an unexpected size diversity ranging from zero to several hundred nucleotides. We identified and validated the expression of about 60 and about 20 ncRNA candidates in Helicobacter and Xanthomonas, respectively. Among these ncRNA candidates we found several small protein-coding genes that have previously evaded annotation in both species. We showed that the combination of dRNA-seq and computational analysis is a powerful method to examine prokaryotic transcriptomes. Experimental setups are time consuming and often combined with huge costs. Another limitation of experimental approaches is that genes which are expressed in specific developmental stages or stress conditions are likely to be missed. Bioinformatic tools build an alternative to overcome such restraints. General approaches usually depend on comparative genomic data and evolutionary signatures are used to analyze the (non-)coding potential of multiple sequence alignments. In the second part of my thesis we present our major update of the widely used ncRNA gene finder RNAz and introduce RNAcode, an efficient tool to asses local protein-coding potential of genomic regions. RNAz has been successfully used to identify structured RNA elements in all domains of life. However, our own experience and the user feedback not only demonstrated the applicability of the RNAz approach, but also helped us to identify limitations of the current implementation. Using a much larger training set and a new classification model we significantly improved the prediction accuracy of RNAz. During transcriptome analysis we repeatedly identified small protein-coding genes that have not been annotated so far. Only a few of those genes are known to date and standard proteincoding gene finding tools suffer from the lack of training data. To avoid an excess of false positive predictions, gene finding software is usually run with an arbitrary cutoff of 40-50 amino acids and therefore misses the small sized protein-coding genes. We have implemented RNAcode which is optimized for emerging applications not covered by standard protein-coding gene annotation software. In addition to complementing classical protein gene annotation, a major field of application of RNAcode is the functional classification of transcribed regions. RNA sequencing analyses are likely to falsely report transcript fragments (e.g. mRNA degradation products) as non-coding. Hence, an evaluation of the protein-coding potential of these fragments is an essential task. RNAcode reports local regions of high coding potential instead of complete protein-coding genes. A training on known protein-coding sequences is not necessary and RNAcode can therefore be applied to any species. We showed this with our analysis of the Escherichia coli genome where the current annotation could be accurately reproduced. We furthermore identified novel small protein-coding genes with RNAcode in this extensively studied genome. Using transcriptome and proteome data we found compelling evidence that several of the identified candidates are bona fide proteins. In summary, this thesis clearly demonstrates that bioinformatic methods are mandatory to analyze the huge amount of transcriptome data and to identify novel (non-)coding RNA genes. With the major update of RNAz and the implementation of RNAcode we contributed to complete the repertoire of gene finding software which will help to unearth hidden treasures of the RNA World.

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Die vorliegende Dissertation verbindet Ergebnisse aus neuroethologischen, verhaltensbiologischen, quantitativ genetischen und genomischen Ansätzen bei Feldgrillen (Gryllus), um neue Erkenntnisse über die Rolle von sexueller Selektion bei Artbildung zu erlangen. Es wird gezeigt dass multivariate Gesangspräferenzen von Grillenweibchen von wenigen Merkmalen abhängen und zwischen Arten ähnlich sind, während sich Männchengesänge in allen Merkmalen unterschieden. Verschiedene Ebenen der Gesangserkennung sind durch unterschiedliche Präferenzfunktionen charakterisiert. Multivariate Präferenzen können also gleichzeitig verschiedene Indikatoren für Paarungspartnerqualität aus den Gesangsmerkmalen erkennen. Eine polygene genetische Architektur der Gesangsmerkmale und der Präferenz wurde beobachtet und weist auf eine eher langsamere Divergenz hin, obwohl gonosomale Vererbung mehrerer Gesangsmerkmale höhere Evolutionsraten zulässt. Starke Kovarianz zwischen den Merkmalen die direkt sexueller Selektion unterliegen und Merkmale, die nicht direkt von Weibchen gewählt werden, zeigen, dass indirekte Selektion teilweise für die markante Divergenz der Gesänge verantwortlich sein könnte, trotz begrenzter Divergenz der Präferenzen. Ferner zeigte ein Artvergleich der multivariaten Gesangsmerkmale, dass die Form der Präferenzfunktion die Ausrichtung der Kovarianzen und damit die erwartete Selektionsantwort der männlichen Gesänge beeinflussen kann. Simulationen ergaben starke Hinweise auf Genfluss zwischen zwei nahverwandten Arten über einen langen Zeitraum . Nur wenige Contigs zeigten hohe genetische Divergenz und hohe Raten nicht-synonymer Polymorphismen. Diese stimmten aber mit Genen überein, die experimentell nachgewiesene Funktionen in neuromuskulärer Entwicklung und im Paarungsverhalten haben. Zusammen zeigen die Ergebnisse das Potential von sexueller Selektion bei der Entstehung und Aufrechterhaltung von reproduktiver Isolation zwischen Arten. / This thesis integrates insights from neuro-ethological, behavioural, quantitative genetics, and genomic approaches in field crickets to provide novel insights in the role of sexual selection in speciation, in particular focusing on speciation with gene flow. It was shown that song preferences depend on few traits and are similar across species while the male song has diverged strongly in all traits. Because the different levels of song recognition are characterized by different types of preference functions, it is conceivable that multivariate preferences can extract various cues for mate quality from different traits simultaneously. A polygenic genetic architecture was found for song traits and preferences, probably limiting divergence rates. However, sex-chromosomal inheritance of some song traits may have allowed for somewhat higher rates. Strong covariance was found between traits that are under sexual selection and traits that are not directly selected by females. This indicates that indirect selection may be responsible in part for striking multivariate divergence in the male calling song despite limited divergence in female preferences. Furthermore, comparing multivariate song traits among species showed that the shape of the preference function can affect the orientation of trait covariance and thereby the selection responses of the male song. Coalescent simulations revealed evidence for a long history of gene flow between two closely related cricket species. Only few contigs with high genetic divergence and high rates of non-synonymous SNPs were found, but many of those that were highly diverged matched genes with experimentally proven functions in neuromuscular development and courtship behavior. Together, these findings underline the potential for sexual selection to drive reproductive isolation.

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Zugl.: Basel, Univ., Diss.

Understanding the connection between an organism’s genotype and its phenotype is a key question in evolutionary biology and genetics. It has been shown that many changes of morphological or other complex phenotypic traits result from changes in the expression pattern of key developmental genes rather than from changes in the genes itself. Such altered gene expression arises often from changes in the gene regulatory regions. That usually means the loss of important transcription factor (TF) binding sites within these regulatory regions, because the interaction between TFs and specific sites on the DNA is a key element of gene regulation. An established approach for the genome-wide mapping of genomic regions to phenotypes is the Forward Genomics framework. This approach compares the genomic sequences of species with and without the phenotype of interest based upon two ideas. First, the initial loss of a phenotype relaxes selection on all phenotypically related genomic regions and, second, this can happen independently in multiple species. Of interest are such regions that diverged specifically in phenotype-loss species. Although this principle is general, the current implementation is only well-suited for the identification of phenotype related gene-coding regions and has a limited applicability on regulatory regions. The reason is its reliance on sequence conservation as divergence measure, which does not accurately measure functional divergence of regulatory elements. In this thesis, I developed REforge, a novel implementation of the Forward Genomics principle that takes functional information of regulatory elements in the form of known phenotype-related TF into account. The consideration of the flexible organization of TF binding sites within a regulatory region, both in terms of strength and order, allows the abstraction from the region’s sequence level to its functional level. Thus, functional divergence of regulatory regions is directly compared to phenotypical divergence, which tremendously improves performance compared to Forward Genomics, as I demonstrated on synthetic and real data. Additionally, I developed TFforge which follows the same approach but aims at identifying the TFs relevant for the given phenotype. Given a multi-species alignment with a phenotype annotation and a set of regulatory regions, TFforge systematically searches for TFs whose changes in binding affinity between species fit the phenotype signature. The reported output is a ranking of the TFs according to their level of correspondence. I prove the concept of this approach on both biological data and artificially generated regions. TFforge can be used as a standalone analysis tool and also to generate the input set of TFs for a subsequent REforge analysis. I demonstrate that REforge in combination with TFforge is able to substantially outperform standard Forward Genomics, i.e. even without foreknowledge of relevant TFs. Overall, the in this thesis introduced methods are examples for the power of computational tools in comparative genomics to catalyze biological insights. I did not only show a detailed description of the methods but also conducted a real data analysis as validation. REforge and TFforge have a wide applicability on endless phenotypes, both on their own in the association of TF and regulatory region to a phenotype. Moreover, particularly their combination constitutes in respect to gene regulatory network analyses a valuable tool set for evo-devo studies.

Non-coding RNAs (ncRNAs) are transcripts that function directly as RNA molecule without ever being translated to protein. The transcriptional output of eukaryotic cells is diverse, pervasive, and multi-layered. It consists of spliced as well as unspliced transcripts of both protein-coding messenger RNAs and functional ncRNAs. However, it also contains degradable non-functional by-products and artefacts - certainly a reason why ncRNAs have long been wrongly disposed as transcriptional noise. Today, RNA-controlled regulatory processes are broadly recognized for a variety of ncRNA classes. The thermoresponsive ROSE ncRNA (repression of heat shock gene expression) is only one example of a regulatory ncRNA acting at the post-transcriptional level via conformational changes of its secondary structure. Bioinformatics helps to identify novel ncRNAs in the bulk of genomic and transcriptomic sequence data which are produced at ever increasing rates. However, ncRNA annotation is unfortunately not part of generic genome annotation pipelines. Dedicated computational searches for particular ncRNAs are veritable research projects in their own right. Despite best efforts, ncRNAs across the animal phylogeny remain to a large extent uncharted territory. This thesis describes a comprehensive collection of exploratory bioinformatic field studies designed to de novo predict ncRNA genes in a series of computational screens and in a multitude of newly sequenced genomes. Non-coding RNAs can be divided into subclasses (families) according to peculiar functional, structural, or compositional similarities. A simple but eligible and frequently applied criterion to classify RNA species is length. In line, the thesis is structured into two parts: We present a series of pilot-studies investigating (1) the short and (2) the long ncRNA repertoire of several model species by means of state-of-the-art bioinformatic techniques. In the first part of the thesis, we focus on the detection of short ncRNAs exhibiting thermodynamically stable and evolutionary conserved secondary structures. We provide evidence for the presence of short structured ncRNAs in a variety of different species, ranging from bacteria to insects and higher eukaryotes. In particular, we highlight drawbacks and opportunities of RNAz-based ncRNA prediction at several hitherto scarcely investigated scenarios, as for example ncRNA prediction in the light of whole genome duplications. A recent microarray study provides experimental evidence for our approach. Differential expression of at least one-sixth of our drosophilid RNAz predictions has been reported. Beyond the means of RNAz, we moreover manually compile sophisticated annotation of short ncRNAs in schistosomes. Obviously, accumulating knowledge about the genetic material of malaria causing parasites which infect millions of humans world-wide is of utmost scientific interest. Since the performance of any comparative genomics approach is limited by the quality of its input alignments, we introduce a novel light-weight and performant genome-wide alignment approach: NcDNAlign. Although the tool is optimized for speed rather than sensitivity and requires only a minor fraction of CPU time compared to existing programs, we demonstrate that it is basically as sensitive and specific as competing approaches when applied to genome-wide ncRNA gene finding and analysis of ultra-conserved regions. By design, however, prediction approaches that search for regions with an excess of mutations that maintain secondary structure motifs will miss ncRNAs that are unstructured or whose structure is not well conserved in evolution. In the second part of the thesis, we therefore overcome secondary structure prediction and, based on splice site detection, develop novel strategies specifically designed to identify long ncRNAs in genomic sequences - probably the open problem in current RNA research. We perform splice site anchored gene-finding in drosophilids, nematodes, and vertebrate genomes and, at least for a subset of obtained candidate genes, provide experimental evidence for expression and the existence of novel spliced transcripts undoubtedly confirming our approach. In summary, we found evidence for a large number of previously undescribed RNAs which consolidates the idea of non-coding RNAs as an abundant class of regulatory active transcripts. Certainly, ncRNA prediction is a complex task. This thesis, however, rationally advises how to unveil the RNA complement of newly sequenced genomes. Since our results have already established both subsequent computational as well as experimental studies, we believe to have enduringly stimulated the field of RNA research and to have contributed to an enriched view on the subject.

Information processing functions are essential for organisms to perceive and react to their complex environment, and for humans to analyze and rationalize them. While our brain is extraordinary at processing complex information, winner-take-all, as a type of biased competition is one of the simplest models of lateral inhibition and competition among biological neurons. It has been implemented as DNAbased neural networks, for example, to mimic pattern recognition. However, the utility of DNA-based computation in information processing for real biotechnological applications remains to be demonstrated. In this paper, a biased competitionmethod for nonlinear manipulation and analysis ofmixtures of DNA sequences was developed. Unlike conventional biological experiments, selected species were not directly subjected to analysis. Instead, parallel computation among a myriad of different DNA sequences was carried out to reduce the information entropy. The method could be used for various oligonucleotideencoded libraries, as we have demonstrated its application in decoding and data analysis for selection experiments with DNA-encoded chemical libraries against protein targets.