No description available.

No description available.

No description available.

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.

No description available.

No description available.

In der Studie werden zwei sprachliche Minderheiten in Europa miteinander verglichen: 16- bis 18-jährige sorbischsprachige Jugendliche in Deutschland und schwedischsprachige Jugend¬liche in Finnland. Der jeweilige soziohistorische Kontext schafft verschiedene sprachliche Rahmenbedingungen dieser Minderheiten, die Unterschiede z.B. im Sprach¬gebrauch erwarten lassen. Trotzdem sind Ähnlichkeiten feststellbar. Ein Vergleich zwischen zwei Sprachkontaktsituationen ist durch das von der Sozioprofilmethode angebotene Analyse¬muster möglich, in dem linguistische, geographische, geschichtliche und demo¬graphische Aspekte sowie der gesetzliche Status und die (offizielle) Sprachpolitik, die Präsenz und der Gebrauch der Sprache in verschiedenen Domänen sowie Identitätsaspekte behandelt werden. Als theoretischer Rahmen für die Studie wurde ein Spracheinstellungsmodell entwickelt, das den Sprachkontakt in einem soziohistorischen Kontext betrachtet. Als Einflussfaktoren von Spracheinstellungen gelten demographische Faktoren, Sprachkompetenz und Identitäts¬gefühle. Spracheinstellungen werden als ein Sammelbegriff für verschiedene sprachver¬bundene Einstellungen verstanden, die integrativ und/oder instrumentell oder beides sein können und aus den drei Dimensionen kognitiv, affektiv/evaluativ/normativ und konativ bestehen. Die empirische Untersuchung wurde 2003/2004 in einem sorbischsprachigen (Bautzen/ Budyšin) und drei schwedischsprachigen (Kimito/Kemiö, Pargas/Parainen, Turku/Åbo) Gym¬nasien (Minderheitenschulen) durchgeführt. Der Feldforschungsphase gingen ein umfang¬reiches Literatur¬studium, Multiplikatorengespräche sowie eine Beobachtungsphase voraus. Die Befragung wurde durch strukturierte Interviews mit jeweils ca. 100 Informanten durch¬geführt. Die deutlichsten Unterschiede zwischen den beiden Gruppen sind bezüglich der Sprachkompetenz sowie des Sprachgebrauchs in den offiziellen Domänen zu finden, obwohl keine der Gruppen die Möglichkeiten zur Verwendung der Minderheitssprache ausschöpft. Parallelen sind jedoch zahlreicher als Unterschiede: u.a. die häufige Verwendung der Minderheitssprache in privaten Domänen, Präferenz der Majoritätssprache in offiziellen Domänen sowie die wichtige Stellung der Majoritätssprache und des Englischen in Domänen der Jugendkultur. Beide Gruppen fühlen sich außerdem häufig unter Druck, von der Minderheitssprache zur Majoritätssprache zu wechseln. Weitere Ähnlichkeiten zwischen den beiden untersuchten Gruppen sind die starke Verbundenheit mit der Minderheitssprache, die sehr schwache Verbundenheit mit den Nachbarsprachen und der vor allem instrumentelle Wert der Majoritätssprache. Die Studie leistet einen Beitrag zur Theoriebildung in Spracheinstellungen sowie zur Diskussion über die Mehrsprachigkeit in Europa, in dem sie ausgehend von ihren Ergebnissen Anregungen für Sprachpolitik anbietet.

Zugl.: Mannheim, Univ., Diss., 2007

Zugl.: Mannheim, Univ., Diss., 2007

Zugl.: Siegen, Univ., Diss., 2007