Metamotifs--a generative model for building families of nucleotide position weight matrices.

BACKGROUND: Development of high-throughput methods for measuring DNA interactions of transcription factors together with computational advances in short motif inference algorithms is expanding our understanding of transcription factor binding site motifs. The consequential growth of sequence motif data sets makes it important to systematically group and categorise regulatory motifs. It has been shown that there are familial tendencies in DNA sequence motifs that are predictive of the family of factors that binds them. Further development of methods that detect and describe familial motif trends has the potential to help in measuring the similarity of novel computational motif predictions to previously known data and sensitively detecting regulatory motifs similar to previously known ones from novel sequence. RESULTS: We propose a probabilistic model for position weight matrix (PWM) sequence motif families. The model, which we call the 'metamotif' describes recurring familial patterns in a set of motifs. The metamotif framework models variation within a family of sequence motifs. It allows for simultaneous estimation of a series of independent metamotifs from input position weight matrix (PWM) motif data and does not assume that all input motif columns contribute to a familial pattern. We describe an algorithm for inferring metamotifs from weight matrix data. We then demonstrate the use of the model in two practical tasks: in the Bayesian NestedMICA model inference algorithm as a PWM prior to enhance motif inference sensitivity, and in a motif classification task where motifs are labelled according to their interacting DNA binding domain. CONCLUSIONS: We show that metamotifs can be used as PWM priors in the NestedMICA motif inference algorithm to dramatically increase the sensitivity to infer motifs. Metamotifs were also successfully applied to a motif classification problem where sequence motif features were used to predict the family of protein DNA binding domains that would interact with it. The metamotif based classifier is shown to compare favourably to previous related methods. The metamotif has great potential for further use in machine learning tasks related to especially de novo computational sequence motif inference. The metamotif methods presented have been incorporated into the NestedMICA suite.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

iMotifs: an integrated sequence motif visualization and analysis environment.

MOTIVATION: Short sequence motifs are an important class of models in molecular biology, used most commonly for describing transcription factor binding site specificity patterns. High-throughput methods have been recently developed for detecting regulatory factor binding sites in vivo and in vitro and consequently high-quality binding site motif data are becoming available for increasing number of organisms and regulatory factors. Development of intuitive tools for the study of sequence motifs is therefore important. iMotifs is a graphical motif analysis environment that allows visualization of annotated sequence motifs and scored motif hits in sequences. It also offers motif inference with the sensitive NestedMICA algorithm, as well as overrepresentation and pairwise motif matching capabilities. All of the analysis functionality is provided without the need to convert between file formats or learn different command line interfaces. The application includes a bundled and graphically integrated version of the NestedMICA motif inference suite that has no outside dependencies. Problems associated with local deployment of software are therefore avoided. AVAILABILITY: iMotifs is licensed with the GNU Lesser General Public License v2.0 (LGPL 2.0). The software and its source is available at http://wiki.github.com/mz2/imotifs and can be run on Mac OS X Leopard (Intel/PowerPC). We also provide a cross-platform (Linux, OS X, Windows) LGPL 2.0 licensed library libxms for the Perl, Ruby, R and Objective-C programming languages for input and output of XMS formatted annotated sequence motif set files. CONTACT: [email protected]; [email protected]

The exchangeability of shape

<p>Abstract</p> <p>Background</p> <p>Landmark based geometric morphometrics (GM) allows the quantitative comparison of organismal shapes. When applied to systematics, it is able to score shape changes which often are undetectable by traditional morphological studies and even by classical morphometric approaches. It has thus become a fast and low cost candidate to identify cryptic species. Due to inherent mathematical properties, shape variables derived from one set of coordinates cannot be compared with shape variables derived from another set. Raw coordinates which produce these shape variables could be used for data exchange, however they contain measurement error. The latter may represent a significant obstacle when the objective is to distinguish very similar species.</p> <p>Results</p> <p>We show here that a single user derived dataset produces much less classification error than a multiple one. The question then becomes how to circumvent the lack of exchangeability of shape variables while preserving a single user dataset. A solution to this question could lead to the creation of a relatively fast and inexpensive systematic tool adapted for the recognition of cryptic species.</p> <p>Conclusions</p> <p>To preserve both exchangeability of shape and a single user derived dataset, our suggestion is to create a free access bank of reference images from which one can produce raw coordinates and use them for comparison with external specimens. Thus, we propose an alternative geometric descriptive system that separates 2-D data gathering and analyzes.</p

Hidden Sylvatic Foci of the Main Vector of Chagas Disease Triatoma infestans: Threats to the Vector Elimination Campaign?

Triatoma infestans, a highly domesticated species and historically the main vector of Trypanosoma cruzi, is the target of an insecticide-based elimination program in the southern cone countries of South America since 1991. Only limited success has been achieved in the Gran Chaco region due to repeated reinfestations. We conducted full-coverage spraying of pyrethroid insecticides of all houses in a well-defined rural area in northwestern Argentina, followed by intense monitoring of house reinfestation and searches for triatomine bugs in sylvatic habitats during the next two years, to establish the putative sources of new bug colonies. We found low-density sylvatic foci of T. infestans in trees located within the species' flight range from the nearest infested house detected before control interventions. Using multiple methods (fine-resolution satellite imagery, geographic information systems, spatial statistics, genetic markers and wing geometric morphometry), we corroborated the species identity of the sylvatic bugs as T. infestans and found they were indistinguishable from or closely related to local domestic or peridomestic bug populations. Two sylvatic foci were spatially associated to the nearest peridomestic bug populations found before interventions. Sylvatic habitats harbor hidden foci of T. infestans that may represent a threat to vector suppression attempts

A large outbreak of Legionnaires’ Disease in an industrial town in Portugal

Background We describe the investigation and control of an outbreak of Legionnaires’ disease in Portugal in October, November and December 2014. Methods Confirmed cases were individuals with pneumonia, laboratory evidence of Legionella pneumophila serogroup 1 and exposure, by residence, occupational or leisure to the affected municipalities. 49 possible sources were reduced to four potential sources, all industries with wet cooling system, following risk assessment. We geo-referenced cases’ residences and the location of cooling towers defining four study areas 10 km buffer centered on each cooling tower system. We compared the number of cases with expected numbers, calculated from the outbreak's attack rates applied to 2011 census population. Using Stones’ Test, we tested observed to expected ratios for decline in risk, with distance up to 10 km four directions. Isolates of Legionella pneumophila were compared using molecular methods. Results We identified 403 cases, 377 of which were confirmed, 14 patients died. Patients became ill between 14 October and 2 December. A NE wind and thermal inversion were recorded during the estimated period of exposure. Disease risk was highest in people living south west from all of the industries identified and decreased with distance (p < 0.001). 71 clinical isolates demonstrated an identical SBT profile to an isolate from a cooling tower. Whole genome sequencing identified an unusual L. pneumophila subsp. fraseri serogroup 1 as the outbreak causative strain, and confirmed isolates’ relatedness. Conclusions Industrial wet cooling systems, bacteria with enhanced survival characteristics and a combination of climatic conditions contributed to the second largest outbreak of Legionnaires’ disease recorded internationally.info:eu-repo/semantics/publishedVersio

Catalysis of iron core formation in Pyrococcus furiosus ferritin

The hollow sphere-shaped 24-meric ferritin can store large amounts of iron as a ferrihydrite-like mineral core. In all subunits of homomeric ferritins and in catalytically active subunits of heteromeric ferritins a diiron binding site is found that is commonly addressed as the ferroxidase center (FC). The FC is involved in the catalytic Fe(II) oxidation by the protein; however, structural differences among different ferritins may be linked to different mechanisms of iron oxidation. Non-heme ferritins are generally believed to operate by the so-called substrate FC model in which the FC cycles by filling with Fe(II), oxidizing the iron, and donating labile Fe(III)–O–Fe(III) units to the cavity. In contrast, the heme-containing bacterial ferritin from Escherichia coli has been proposed to carry a stable FC that indirectly catalyzes Fe(II) oxidation by electron transfer from a core that oxidizes Fe(II). Here, we put forth yet another mechanism for the non-heme archaeal 24-meric ferritin from Pyrococcus furiosus in which a stable iron-containing FC acts as a catalytic center for the oxidation of Fe(II), which is subsequently transferred to a core that is not involved in Fe(II)-oxidation catalysis. The proposal is based on optical spectroscopy and steady-state kinetic measurements of iron oxidation and dioxygen consumption by apoferritin and by ferritin preloaded with different amounts of iron. Oxidation of the first 48 Fe(II) added to apoferritin is spectrally and kinetically different from subsequent iron oxidation and this is interpreted to reflect FC building followed by FC-catalyzed core formation

UbcH10 overexpression may represent a marker of anaplastic thyroid carcinomas

The hybridisation of an Affymetrix HG_U95Av2 oligonucleotide array with RNAs extracted from six human thyroid carcinoma cell lines and a normal human thyroid primary cell culture led us to the identification of the UbcH10 gene that was upregulated by 150-fold in all of the carcinoma cell lines in comparison to the primary culture cells of human normal thyroid origin. Immunohistochemical studies performed on paraffin-embedded tissue sections showed abundant UbcH10 levels in thyroid anaplastic carcinoma samples, whereas no detectable UbcH10 expression was observed in normal thyroid tissues, in adenomas and goiters. Papillary and follicular carcinomas were only weakly positive. These results were further confirmed by RT–PCR and Western blot analyses. The block of UbcH10 protein synthesis induced by RNA interference significantly reduced the growth rate of thyroid carcinoma cell lines. Taken together, these results would indicate that UbcH10 overexpression is involved in thyroid cell proliferation, and may represent a marker of thyroid anaplastic carcinomas