A central problem in the bioinformatics is to find the binding sites for regulatory motifs. This is a challenging problem that leads us to a platform to apply a variety of data mining methods. In the efforts described here, a combined motif discovery method that uses mutual information and Gibbs sampling was developed. A new scoring schema was introduced with mutual information and joint information content involved. Simulated tempering was embedded into classic Gibbs sampling to avoid local optima. This method was applied to the 18 pieces DNA sequences containing CRP binding sites validated by Stormo and the results were compared with Bioprospector. Based on the results, the new scoring schema can get over the defect that the basic model PWM only contains single positioin information. Simulated tempering proved to be an adaptive adjustment of the search strategy and showed a much increased resistance to local optima.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第21946号 / 理博第4524号 / 新制||理||1650(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)准教授 佐藤 ゆたか, 教授 高橋 淑子, 教授 中務 真人 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

No description available.

Background Promoters are key players in gene regulation. They receive signals from various sources (e.g. cell surface receptors) and control the level of transcription initiation, which largely determines gene expression. In vertebrates, transcription start sites and surrounding regulatory elements are often poorly defined. To support promoter analysis, we present CORG http://corg.molgen.mpg.de, a framework for studying upstream regions including untranslated exons (5' UTR). Description The automated annotation of promoter regions integrates information of two kinds. First, statistically significant cross-species conservation within upstream regions of orthologous genes is detected. Pairwise as well as multiple sequence comparisons are computed. Second, binding site descriptions (position-weight matrices) are employed to predict conserved regulatory elements with a novel approach. Assembled EST sequences and verified transcription start sites are incorporated to distinguish exonic from other sequences. As of now, we have included 5 species in our analysis pipeline (man, mouse, rat, fugu and zebrafish). We characterized promoter regions of 16,127 groups of orthologous genes. All data are presented in an intuitive way via our web site. Users are free to export data for single genes or access larger data sets via our DAS server http://tomcat.molgen.mpg.de:8080/das. The benefits of our framework are exemplarily shown in the context of phylogenetic profiling of transcription factor binding sites and detection of microRNAs close to transcription start sites of our gene set. Conclusion The CORG platform is a versatile tool to support analyses of gene regulation in vertebrate promoter regions. Applications for CORG cover a broad range from studying evolution of DNA binding sites and promoter constitution to the discovery of new regulatory sequence elements (e.g. microRNAs and binding sites).

No description available.

<p>OVARIAN cancer research focuses on answering important questions related to the disease, determining whether new approaches are feasible to contribute towards improving current treatments or discovering new ones. This study focused on the transcriptional regulation of genes that have been implicated in ovarian cancer, based on the occurrences of single nucleotide polymorphisms (SNPs) within transcription factor binding sites (TFBSs). Through the application of several in silico tools, databases and custom programs, this research aimed to contribute toward the identification of potentially bio-medically important genes or SNPs for pre-diagnosis and subsequent treatment planning of ovarian cancer. A total of 379 candidate genes that have been experimentally associated with ovarian cancer were analyzed. This led to the identification of 121 SNPs that were found to coincide with putative TFBSs potentially influencing a total of 57 transcription factors that would normally bind to these TFBSs. These SNPs with potential phenotypic effect were then evaluated among several population groups, defined by the International HapMap consortium resulting in the identification of three SNPs present in five or more of the eleven population groups that have been sampled.</p>

<p>OVARIAN cancer research focuses on answering important questions related to the disease, determining whether new approaches are feasible to contribute towards improving current treatments or discovering new ones. This study focused on the transcriptional regulation of genes that have been implicated in ovarian cancer, based on the occurrences of single nucleotide polymorphisms (SNPs) within transcription factor binding sites (TFBSs). Through the application of several in silico tools, databases and custom programs, this research aimed to contribute toward the identification of potentially bio-medically important genes or SNPs for pre-diagnosis and subsequent treatment planning of ovarian cancer. A total of 379 candidate genes that have been experimentally associated with ovarian cancer were analyzed. This led to the identification of 121 SNPs that were found to coincide with putative TFBSs potentially influencing a total of 57 transcription factors that would normally bind to these TFBSs. These SNPs with potential phenotypic effect were then evaluated among several population groups, defined by the International HapMap consortium resulting in the identification of three SNPs present in five or more of the eleven population groups that have been sampled.</p>

Magister Scientiae - MSc / OVARIAN cancer research focuses on answering important questions related to the disease, determining whether new approaches are feasible to contribute towards improving current treatments or discovering new ones. This study focused on the transcriptional regulation of genes that have been implicated in ovarian cancer, based on the occurrences of single nucleotide polymorphisms (SNPs) within transcription factor binding sites (TFBSs). Through the application of several in silico tools, databases and custom programs, this research aimed to contribute toward the identification of potentially bio-medically important genes or SNPs for pre-diagnosis and subsequent treatment planning of ovarian cancer. A total of 379 candidate genes that have been experimentally associated with ovarian cancer were analyzed. This led to the identification of 121 SNPs that were found to coincide with putative TFBSs potentially influencing a total of 57 transcription factors that would normally bind to these TFBSs. These SNPs with potential phenotypic effect were then evaluated among several population groups, defined by the International HapMap consortium resulting in the identification of three SNPs present in five or more of the eleven population groups that have been sampled. / South Africa

Philosophiae Doctor - PhD / Tsetse flies (Glossina spp) are the biological vectors for Trypanosomes, the causative magents of Human African Trypanosomiasis (HAT). HAT is a debilitating disease that continues to present a major public health problem and a key factor limiting rural development in vast regions of tropical Africa. To augment vector control efforts, the International Glossina Genome Initiative (IGGI) was established in 2004 with the ultimate goal of generating a fully annotated whole genome sequence for Glossina morsitans. A working draft genome of Glossina morsitans was availed in 2011. In this thesis, transcriptional regulatory features in Glossina morsitans were analysed using the draft genome. A method for TSS identification in the newly sequenced Glossina morsitans genome was developed using TSS-seq tags sampled from two developmental stages of Glossina morsitans. High throughput next generation sequencing reads obtained from Glossina morsitans larvae and pupae were used to locate transcription start sites (TSS) in the Glossina morsitans genome. TSS-seq tag clusters, defined as a minimum number of reads at the 5’ predicted UTR or first coding exon, were used to define transcription start sites. A total of 3134 tag clusters were identified on the Glossina genome. Approximately 45.4% (1424) of the tag clusters mapped to the first coding exons or their proximal predicted 5’UTR regions and include 31 tag clusters that mapped to transposons. A total of 1101 (35.1%) tag clusters mapped outside the genic region and/or scaffolds without gene predictions and may correspond to previously un-annotated transcripts or noncoding RNA TSS. The core promoter regions were classified as narrow or broad based on the number of TSS positions within a TSS-seq cluster. Majority (95%) of the core promoters analysed in this study were of the broad type while only 5% were of the narrow type. Comparison of canonical core promoter motif occurences between random and bona fide core promoters showed that, generally, the number of motifs in biologically functional genomic windows in the true dataset exceeded those in the random dataset (p <= 0.00164, 0.00135, 0.00185 for the narrow, broad with peak and broad without peak categories respectively). Frequency of motif co-occurrence in core promoter was found to be fundamentally different across various initiation patterns. Narrow core promoters recorded higher frequency of the TATA-box and INR motifs and two-way motif co-occurrence showed that the TATA-box-INR pair is over-represented in the narrow category. Broad core promoters showed higher frequency of the BREd and MTE motifs and two-way motif co-occurrence showed that the MTE-DPE pair is over-represented in broad core promoters. TATA-less promoters account for 77% of the core promoters in this analysis. TATA-less core promoters showed a higher frequency of the MTE and INR motifs in contrast to observations in Drosophila where the DPE motif has been reported to occur frequently in TATA-less promoters. These motif combinations suggest their equal importance to transcription in their corresponding promoter classes in Glossina morsitans.

Philosophiae Doctor - PhD / It was postulated that each gene has three main sets of transcriptional elements: one which is gene-specific, one which is family-specific, and a third which is tissue-specific.The starting hypothesis for this project had been: “Each family of genes has a distinct set of transcriptional elements that is unique onto this family”. The primary aim of this project was therefore the identification of the family-specific set of transcriptional elements within the AMPA receptor gene family. The question then is how does one measure or identify this uniqueness within the promoters of this family of genes. The answer seemed to lie in making an assessment of the promoters of this family of genes against a background of a comprehensive set of promoter sequences and in the process,to try to find the transcriptional elements that were present in the AMPA receptor gene promoters but were not so common in the general population of gene promoters.To achieve the primary aim of this project, it was essential that a comprehensive dataset of promoter sequences was available. There are ample data freely available through the web. However, it is often not available in a form that we might want it in. Another problem that one constantly encounters is the lack of general consensus among the research community in agreeing on a standard annotation. For example, a gene can sometimes be given 2 or 3 different names by different laboratories which have successfully cloned the same gene. This, in turn, hinders the data collection process. At the start of this project, there was an existing curated database of experimentally-verified eukaryotic promoter sequences called the Eukaryotic Promoter Database (EPD) and a software called Promoter Extraction from GenBank (PEG) which, as its name implies, extracts promoter sequences available through GenBank (Cavin Périer et al., 1998;Zhang & Zhang, 2001; Praz et al., 2002; Schmid et al., 2004). However, limitations existed in both these resources. For EPD, the number of curated promoter sequences available was low and also, the length of these promoter sequences was short. For PEG,the main limitation was that the extraction from GenBank would result in extraction of sequences of variable lengths.Therefore, the 5’-end Information Extraction (FIE)system was developed for the expressed purpose of collecting promoter sequences without the limitations of PEG. This software relies on the alignment of multiple mRNA/cDNA sequences that are representative of a gene on the human genomic sequence to determine the transcription start site (TSS) of the gene and thus, with this information, extract the promoter sequence for the gene from the available human genomic sequence. This was the first promoter extraction software to work on this principle (Chong et al., 2002). This method was later supported by experimental work carried out by Coleman and colleagues (2002). Using the FIE2 software (Chong et al.,2003), some 10,000-odd human promoter sequences was extracted, starting at 1500bp uptream and ending at 1000bp downstream of the 5’-most TSS.Following the collection of the human promoter sequences, the approach developed by Bajic et al. (2004) was applied to study the promoters of the AMPA receptor genes. This approach relies on both the MATCH program to map putative transcription factor binding sites (TFBSs) to the promoter sequences and a software developed by Bajic etal. (2004) that calculates to the density for each TFBS or composite element. Having calculated the densities for the TFBSs and composite elements for both the target promoters (in this case, the AMPA receptor gene promoters) and the background promoters (the 10,000-odd human promoters), the software then calculates the degree of over-representation of each TFBS and composite element in the target promoters(measured against the background promoters) and then ranks the “singles”, “pairs” and “triplets” in the order of their degree of over-representation. Using this method, I identified the top 3 ranked “single”, “pair” and “triplet” transcriptional elements found commonly within the AMPA receptor promoters. In addition, a conventional phylogenetic footprinting study was also carried out for the human, mouse and rat GRIA1 promoter to identify key transcriptional elements within this subunit’s promoter.While the approach developed by Bajic et al. (2004) identifies key family-specific transcriptional elements, the phylogenetic footprinting study helps identify key genespecific transcriptional elements. Thus, they complement one another.The approach developed by Bajic et al. (2004) yielded an interesting result. It was found that the combination of the top 3 ranked “single”, “pair” and “triplet” transcriptional elements found in the AMPA receptor promoters were also found in 47 other genes. It was postulated that these 47 genes might, in fact, be co-regulated / co-expressed with the GRIAs and thus, explaining the existence of a shared promoter profile with the GRIA promoters. In support of this hypothesis, supporting evidence was found in published literature that 7 of these 47 genes (VAMP4, Rab3B, FKBP8, 3-OST-3A, CLSTN3,SOCS1 and IκBβ) might indeed be involved in the expression and functioning of the AMPA receptors.