A survey of DNA motif finding algorithms

Background: Unraveling the mechanisms that regulate gene expression is a major challenge in biology. An important task in this challenge is to identify regulatory elements, especially the binding sites in deoxyribonucleic acid (DNA) for transcription factors. These binding sites are short DNA segments that are called motifs. Recent advances in genome sequence availability and in high-throughput gene expression analysis technologies have allowed for the development of computational methods for motif finding. As a result, a large number of motif finding algorithms have been implemented and applied to various motif models over the past decade. This survey reviews the latest developments in DNA motif finding algorithms.Results: Earlier algorithms use promoter sequences of coregulated genes from single genome and search for statistically overrepresented motifs. Recent algorithms are designed to use phylogenetic footprinting or orthologous sequences and also an integrated approach where promoter sequences of coregulated genes and phylogenetic footprinting are used. All the algorithms studied have been reported to correctly detect the motifs that have been previously detected by laboratory experimental approaches, and some algorithms were able to find novel motifs. However, most of these motif finding algorithms have been shown to work successfully in yeast and other lower organisms, but perform significantly worse in higher organisms.Conclusion: Despite considerable efforts to date, DNA motif finding remains a complex challenge for biologists and computer scientists. Researchers have taken many different approaches in developing motif discovery tools and the progress made in this area of research is very encouraging. Performance comparison of different motif finding tools and identification of the best tools have proven to be a difficult task because tools are designed based on algorithms and motif models that are diverse and complex and our incomplete understanding of the biology of regulatory mechanism does not always provide adequate evaluation of underlying algorithms over motif models.Peer reviewedComputer Scienc

Covariation for microsatellite marker alleles associated with Rht8 and coleoptile length in winter wheat

Wheat (Triticum aestivum L.) cultivars with greater coleoptile elongation are preferred in low-precipitation dryland regions and in early-planted management systems of the Great Plains, but the presence of GA3 (gibberellin)-insensitive dwarfing genes tends to restrict coleoptile elongation. The agronomic value of Rht8 and the discovery of its diagnostic microsatellite marker, Xgwm 261, have accelerated breeders' interest in Rht8 as an alternative dwarfing gene. Our objectives were to determine allelic distributions at the marker locus in contemporary samples of hard winter and soft red winter wheat relative to samples of Chinese accessions from a Rht8-rich geographic region, and to compare coleoptile elongation in the presence or absence of Rht8 determined by the Xgwm 261 marker. The 165-bp (primarily hard winter wheats) and the 174-bp (primarily soft red winter wheats) alleles of Xgwm 261 were most frequent. About 8% of all U.S. accessions carried the 192-bp allele diagnostic for Rht8, compared with 64% of the Chinese accessions. Coleoptile length varied among accessions from 4.4 to 11.4 cm. Frequency distributions for 192- and non-192-bp genotypes showed no advantage of the 192-bp allele to coleoptile elongation. None of the 192-bp genotypes from the Great Plains showed greater coleoptile length than 'TAM 107', a hard red winter cultivar without Rht8 often chosen over contemporary cultivars for its greater emergence capacity with deeper seed placement. Since coleoptile elongation may be controlled by several quantitative trait loci, identifying only the presence of 192-bp allele of Xgwm 261 may be misleading if the primary motivation for its deployment is to increase coleoptile length in a semidwarf plant type.Peer reviewedPlant and Soil Science

Survey of Deoxyribonucleic Acid Motif Finding Algorithms

An important task in biology is to identify binding sites in DNA for transcription factors. These binding sites are short DNA segments which are called motifs. Given a set of DNA sequences, the motif finding problem is to detect overrepresented motifs that are good candidates for being transcription factor binding sites. The current study is a survey of motif finding algorithms. The study shows that a sensible approach to detect motif is to search for statistically overrepresented motifs in the promoter region of a set of co-regulated genes. The weak point of the available motif finding algorithms is that they tend to be sensitive to the noise, i.e., the presence of upstream sequences in data set that do not contain the motif. We conclude that instead of relying on a single motif finding tool, biologists should use a few complementary tools and pursue the top few predicted motifs of each.Computer Science Departmen

Gonadotropins and ovarian cancer

Ovarian epithelial cancer (OEC) accounts for 90% of all ovarian cancers and is the leading cause of death from gynecological cancers in North America and Europe. Despite its clinical significance, the factors that regulate the development and progression of ovarian cancer are among the least understood of all major human malignancies. The two gonadotropins, FSH and LH, are key regulators of ovarian cell functions, and the potential role of gonadotropins in the pathogenesis of ovarian cancer is suggested. Ovarian carcinomas have been found to express specific receptors for gonadotropins. The presence of gonadotropins in ovarian tumor fluid suggests the importance of these factors in the transformation and progression of ovarian cancers as well as being prognostic indicators. Functionally, there is evidence showing a direct action of gonadotropins on ovarian tumor cell growth. This review summarizes the key findings and recent advances in our understanding of these peptide hormones in ovarian cancer development and progression and their role in potential future cancer therapy. We will first discuss the supporting evidence and controversies in the "gonadotropin theory" and the use of animal models for exploring the involvement of gonadotropins in the etiology of ovarian cancer. The role of gonadotropins in regulating the proliferation, survival, and metastasis of OEC is next summarized. Relevant data from ovarian surface epithelium, which is widely believed to be the precursor of OEC, are also described. Finally, we will discuss the clinical applications of gonadotropins in ovarian cancer and the recent progress in drug development. Copyright © 2007 by The Endocrine Society.link_to_subscribed_fulltex