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Coxiella burnetii, the causative agent of Q fever, is an obligate intracellular bacterium and a highly infectious pathogen. The disease is a widespread zoonosis and is endemic throughout the world. An easy aerosol dissemination, environmental persistence, and high infectivity make the bacterium a serious threat for humans and animals. Lipopolysaccharide is considered one of the major factors of virulence expression and infection of the bacterium. Detailed glycomic studies enabled to better understand structural and functional peculiarities of this biopolymer and its role in pathogenesis and immunity of Q fever. Recent proteomic studies of C. burnetii have brought new approaches in accurate detection of the infectious agent and offered new insights into the inter- or intra-species relatedness. Thus, structure/function relationship studies are currently of utmost importance in the field. This paper will focus on glycomic and proteomic approaches providing information on unique glycan and protein species of the microorganism as the candidate molecules for the use in detection/diagnosis, therapy, and prophylaxis. [ABSTRACT FROM AUTHOR]

Background: Transcription factor binding site (TFBS) motifs can be accurately represented by position frequency matrices (PFM) or other equivalent forms. We often need to compare TFBS motifs using their PFMs in order to search for similar motifs in a motif database, or cluster motifs according to their binding preference. The majority of current methods for motif comparison involve a similarity metric for column-to-column comparison and a method to find the optimal position alignment between the two compared motifs. In some applications, alignment-free methods might be preferred; however, few such methods with high accuracy have been described. Methodology/Principal Findings: Here we describe a novel alignment-free method for quantifying the similarity of motifs using their PFMs by converting PFMs into k-mer vectors. The motifs could then be compared by measuring the similarity among their corresponding k-mer vectors. Conclusions/Significance: We demonstrate that our method in general achieves similar performance or outperforms the existing methods for clustering motifs according to their binding preference and identifying similar motifs of transcription factors of the same family. [ABSTRACT FROM AUTHOR]