TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Estimating the evidence of selection and the reliability of inference in unigenic evolution

<p>Abstract</p> <p>Background</p> <p>Unigenic evolution is a large-scale mutagenesis experiment used to identify residues that are potentially important for protein function. Both currently-used methods for the analysis of unigenic evolution data analyze 'windows' of contiguous sites, a strategy that increases statistical power but incorrectly assumes that functionally-critical sites are contiguous. In addition, both methods require the questionable assumption of asymptotically-large sample size due to the presumption of approximate normality.</p> <p>Results</p> <p>We develop a novel approach, termed the Evidence of Selection (EoS), removing the assumption that functionally important sites are adjacent in sequence and and explicitly modelling the effects of limited sample-size. Precise statistical derivations show that the EoS score can be easily interpreted as an expected log-odds-ratio between two competing hypotheses, namely, the hypothetical presence or absence of functional selection for a given site. Using the EoS score, we then develop selection criteria by which functionally-important yet non-adjacent sites can be identified. An approximate power analysis is also developed to estimate the reliability of inference given the data. We validate and demonstrate the the practical utility of our method by analysis of the homing endonuclease <monospace>I-Bmol</monospace>, comparing our predictions with the results of existing methods.</p> <p>Conclusions</p> <p>Our method is able to assess both the evidence of selection at individual amino acid sites and estimate the reliability of those inferences. Experimental validation with <monospace>I-Bmol</monospace> proves its utility to identify functionally-important residues of poorly characterized proteins, demonstrating increased sensitivity over previous methods without loss of specificity. With the ability to guide the selection of precise experimental mutagenesis conditions, our method helps make unigenic analysis a more broadly applicable technique with which to probe protein function.</p> <p>Availability</p> <p>Software to compute, plot, and summarize EoS data is available as an open-source package called 'unigenic' for the 'R' programming language at <url>http://www.fernandes.org/txp/article/13/an-analytical-framework-for-unigenic-evolution</url>.</p

Vaccinia Virus G8R Protein: A Structural Ortholog of Proliferating Cell Nuclear Antigen (PCNA)

BACKGROUND: Eukaryotic DNA replication involves the synthesis of both a DNA leading and lagging strand, the latter requiring several additional proteins including flap endonuclease (FEN-1) and proliferating cell nuclear antigen (PCNA) in order to remove RNA primers used in the synthesis of Okazaki fragments. Poxviruses are complex viruses (dsDNA genomes) that infect eukaryotes, but surprisingly little is known about the process of DNA replication. Given our previous results that the vaccinia virus (VACV) G5R protein may be structurally similar to a FEN-1-like protein and a recent finding that poxviruses encode a primase function, we undertook a series of in silico analyses to identify whether VACV also encodes a PCNA-like protein. RESULTS: An InterProScan of all VACV proteins using the JIPS software package was used to identify any PCNA-like proteins. The VACV G8R protein was identified as the only vaccinia protein that contained a PCNA-like sliding clamp motif. The VACV G8R protein plays a role in poxvirus late transcription and is known to interact with several other poxvirus proteins including itself. The secondary and tertiary structure of the VACV G8R protein was predicted and compared to the secondary and tertiary structure of both human and yeast PCNA proteins, and a high degree of similarity between all three proteins was noted. CONCLUSIONS: The structure of the VACV G8R protein is predicted to closely resemble the eukaryotic PCNA protein; it possesses several other features including a conserved ubiquitylation and SUMOylation site that suggest that, like its counterpart in T4 bacteriophage (gp45), it may function as a sliding clamp ushering transcription factors to RNA polymerase during late transcription

Self-reported safety belt use among emergency department patients in Boston, Massachusetts

BACKGROUND: Safety belt use is 80% nationally, yet only 63% in Massachusetts. Safety belt use among potentially at-risk groups in Boston is unknown. We sought to assess the prevalence and correlates of belt non-use among emergency department (ED) patients in Boston. METHODS: A cross-sectional survey with systematic sampling was conducted on non-urgent ED patients age ≥18. A closed-ended survey was administered by interview. Safety belt use was defined via two methods: a single-item and a multiple-item measure of safety belt use. Each was scored using a 5-point frequency scale. Responses were used to categorize safety belt use as 'always' or less than 'always'. Outcome for multivariate logistic regression analysis was safety belt use less than 'always'. RESULTS: Of 478 patients approached, 381 (80%) participated. Participants were 48% female, 48% African-American, 40% White, median age 39. Among participants, 250 (66%) had been in a car crash; 234 (61%) had a valid driver's license, and 42 (11%) had been ticketed for belt non-use. Using two different survey measures, a single-item and a multiple-item measure, safety belt use 'always' was 51% and 36% respectively. According to separate regression models, factors associated with belt non-use included male gender, alcohol consumption >5 drinks in one episode, riding with others that drink and drive, ever receiving a citation for belt non-use, believing that safety belt use is 'uncomfortable', and that 'I just forget', while 'It's my usual habit' was protective. CONCLUSION: ED patients at an urban hospital in Boston have considerably lower self-reported safety belt use than state or national estimates. An ED-based intervention to increase safety belt use among this hard-to-reach population warrants consideration

L1pred: A Sequence-Based Prediction Tool for Catalytic Residues in Enzymes with the L1-logreg Classifier

To understand enzyme functions, identifying the catalytic residues is a usual first step. Moreover, knowledge about catalytic residues is also useful for protein engineering and drug-design. However, to experimentally identify catalytic residues remains challenging for reasons of time and cost. Therefore, computational methods have been explored to predict catalytic residues. Here, we developed a new algorithm, L1pred, for catalytic residue prediction, by using the L1-logreg classifier to integrate eight sequence-based scoring functions. We tested L1pred and compared it against several existing sequence-based methods on carefully designed datasets Data604 and Data63. With ten-fold cross-validation, L1pred showed the area under precision-recall curve (AUPR) and the area under ROC curve (AUC) of 0.2198 and 0.9494 on the training dataset, Data604, respectively. In addition, on the independent test dataset, Data63, it showed the AUPR and AUC values of 0.2636 and 0.9375, respectively. Compared with other sequence-based methods, L1pred showed the best performance on both datasets. We also analyzed the importance of each attribute in the algorithm, and found that all the scores contributed more or less equally to the L1pred performance

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Selection acting on genomes

C. K. is supported by a grant of the Vienna Science and Technology Fund (WWTF—MA016-061). M. A. receives funding from the Swiss National Science Foundation (grant 31003A_176316).Populations evolve as mutations arise in individual organisms and, through hereditary transmission, may become “fixed” (shared by all individuals) in the population. Most mutations are lethal or have negative fitness consequences for the organism. Others have essentially no effect on organismal fitness and can become fixed through the neutral stochastic process known as random drift. However, mutations may also produce a selective advantage that boosts their chances of reaching fixation. Regions of genomes where new mutations are beneficial, rather than neutral or deleterious, tend to evolve more rapidly due to positive selection. Genes involved in immunity and defense are a well-known example; rapid evolution in these genes presumably occurs because new mutations help organisms to prevail in evolutionary “arms races” with pathogens. In recent years genome-wide scans for selection have enlarged our understanding of the genome evolution of various species. In this chapter, we will focus on methods to detect selection on the genome. In particular, we will discuss probabilistic models and how they have changed with the advent of new genome-wide data now available.Publisher PD

A comparative study of conservation and variation scores

<p>Abstract</p> <p>Background</p> <p>Conservation and variation scores are used when evaluating sites in a multiple sequence alignment, in order to identify residues critical for structure or function. A variety of scores are available today but it is not clear how different scores relate to each other.</p> <p>Results</p> <p>We applied 25 conservation and variation scores to alignments from the Catalytic Site Atlas (CSA). We calculated distances among scores based on correlation coefficients, and constructed a dendrogram of the scores by average linking cluster analysis. The cluster analysis showed that most scores fall into one of two groups--substitution matrix based group and frequency based group respectively. We also evaluated the scores' performance in predicting catalytic sites and found that frequency based scores generally perform best.</p> <p>Conclusions</p> <p>Conservation and variation scores can be classified into mainly two large groups. When using a score to predict catalytic sites, frequency based scores that also consider a background distribution are most successful.</p