The RNA component of telomerase (telomerase RNA; TER) varies substantially both in sequence composition and size (from ;150 nucleotides [nt] to >1500 nt) across species. This dramatic divergence has hampered the identification of TER genes and a large-scale comparative analysis of TER sequences and structures among distantly related species. To identify by phylogenetic analysis conserved sequences and structural features of TER that are of general importance, it is essential to obtain TER sequences from evolutionarily distant groups of species, providing enough conservation within each group and enough variation among the groups. To this end, we identified TER genes in several yeast species with relatively large (>20 base pairs) and nonvariant telomeric repeats, mostly from the genus Candida. Interestingly, several of the TERs reported here are longer than all other yeast TERs known to date. Within these TERs, we predicted a pseudoknot containing U-AÁU base triples (conserved in vertebrates, budding yeasts, and ciliates) and a three-way junction element (conserved in vertebrates and budding yeasts). In addition, we identified a novel conserved sequence (CS2a) predicted to reside within an internal-loop structure, in all the budding yeast TERs examined. CS2a is located near the Est1p-binding bulge-stem previously identified in Saccharomyces cerevisiae. Mutational analyses in both budding yeasts S. cerevisiae and Kluyveromyces lactis demonstrate that CS2a is essential for in vivo telomerase function. The comparative and mutational analyses of conserved TER elements reported here provide novel insights into the structure and function of the telomerase ribonucleoprotein complex.

The telomerase ribonucleoprotein copies a short template within its integral RNA moiety onto eukaryotic chromosome ends, compensating for incomplete replication and degradation. Non-template regions of telomerase RNA (TER) are also crucial for telomerase function, yet they are highly divergent in sequence among species and their roles are largely unclear. Using both phylogenetic and mutational analyses, we predicted secondary structures for TERs from Kluyveromyces budding yeast species. A comparison of these secondary structure models with the published model for the Saccharomyces cerevisiae TER reveals a common arrangement into three long arms, a templating domain in the center and several conserved elements in the same positions within the structure. One of them, a three-way junction element, is highly conserved in budding yeast TERs. This element also shows sequence and structure similarity to the critical CR4-CR5 activating domain of vertebrate TERs. Mutational analysis in Kluyveromyces lactis confirmed that this element, and in particular the residues conserved across yeast and vertebrates, is critical for telomerase action both in vivo and in vitro. These findings demonstrate that despite the extreme divergence of TER sequences from different organisms, they do share conserved elements, which presumably carry out common roles in telomerase function.

We present an efficient and noise robust template matching method based on asymmetric correlation (ASC). The ASC similarity function is invariant to affine illumination changes and robust to extreme noise. It correlates the given non-normalized template with a normalized version of each image window in the frequency domain. We show that this asymmetric normalization is more robust to noise than other cross correlation variants, such as the correlation coefficient. Direct computation of ASC is very slow, as a DFT needs to be calculated for each image window independently. To make the template matching efficient, we develop a much faster algorithm, which carries out a prediction step in linear time and then computes DFTs for only a few promising candidate windows. We extend the proposed template matching scheme to deal with partial occlusion and spatially varying light change. Experimental results demonstrate the robustness of the proposed ASC similarity measure compared to state-of-the-art template matching methods.

This paper presents a simple and efficient method to convolve an image with a Gaussian kernel. The computation is performed in a constant number of operations per pixel using running sums along the image rows and columns. We investigate the error function used for kernel approximation and its relation to the properties of the input signal. Based on natural image statistics we propose a quadratic form kernel error function so that the SSD error of the output image is minimized. We apply the proposed approach to approximate the Gaussian kernel by linear combination of constant functions. This results in a very efficient Gaussian filtering method. Our experiments show that the proposed technique is faster than state of the art methods while preserving similar accuracy.

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