Planktonic algae and cyanoprokaryotes as indicators of ecosystem quality in the Mooi River system in the North-West Province, South Africa

An ecologically healthy Mooi River system is important for maintaining the quality of potable water of Potchefstroom and surrounding areas. However, this system is under constant threat from anthropogenic pollution arising from both agricultural and mining activities in its catchment. A survey of planktonic algal and cyanoprokaryote assemblages in Klerkskraal, Boskop and Potchefstroom reservoirs was undertaken during 1999–2000 and 2010–2011. In all three dams, total algal and cyanoprokaryote concentrations were lower during the second survey (2010–2011), suggesting an improvement in ecosystem health. However, results also show a change from a Chrysophyceae-dominated community to one dominated by Bacillariophyceae. Increased numbers of diatom species that usually occur in eutrophic impoundments (Melosira varians, Cyclotella meneghiniana and Aulacoseira granulata) indicate an increase in the trophic status of the reservoirs, especially that of Boskop Dam, a trend mirrored by increases in conductivity as well as phosphorus and ammonium concentrations in all three reservoirs. It can therefore be concluded that although the ecosystem health of the Mooi River system is currently still good, further increases in nutrients such as phosphorus can cause proliferation of problem species (detected in enrichment cultures) and a deterioration of its water quality.Keywords: Mooi River reservoirs, algal communities, cyanoprokaryotes, water qualit

Genome re-annotation: a wiki solution?

The annotation of most genomes becomes outdated over time, owing in part to our ever-improving knowledge of genomes and in part to improvements in bioinformatics software. Unfortunately, annotation is rarely if ever updated and resources to support routine reannotation are scarce. Wiki software, which would allow many scientists to edit each genome's annotation, offers one possible solution

Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach

Background - The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy. Methodology/Principal Findings - We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability. Conclusions/Significance - This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic ris

Identification of functional genetic variation in exome sequence analysis

Recent technological advances have allowed us to study individual genomes at a base-pair resolution and have demonstrated that the average exome harbors more than 15,000 genetic variants. However, our ability to understand the biological significance of the identified variants and to connect these observed variants with phenotypes is limited. The first step in this process is to identify genetic variation that is likely to result in changes to protein structure and function, because detailed studies, either population based or functional, for each of the identified variants are not practicable. Therefore algorithms that yield valid predictions of a variant’s functional significance are needed. Over the past decade, several programs have been developed to predict the probability that an observed sequence variant will have a deleterious effect on protein function. These algorithms range from empirical programs that classify using known biochemical properties to statistical algorithms trained using a variety of data sources, including sequence conservation data, biochemical properties, and functional data. Using data from the pilot3 study of the 1000 Genomes Project available through Genetic Analysis Workshop 17, we compared the results of four programs (SIFT, PolyPhen, MAPP, and VarioWatch) used to predict the functional relevance of variants in 101 genes. Analysis was conducted without knowledge of the simulation model. Agreement between programs was modest ranging from 59.4% to 71.4% and only 3.5% of variants were classified as deleterious and 10.9% as tolerated across all four programs

Unique reporter-based sensor platforms to monitor signalling in cells

Introduction: In recent years much progress has been made in the development of tools for systems biology to study the levels of mRNA and protein, and their interactions within cells. However, few multiplexed methodologies are available to study cell signalling directly at the transcription factor level. <p/>Methods: Here we describe a sensitive, plasmid-based RNA reporter methodology to study transcription factor activation in mammalian cells, and apply this technology to profiling 60 transcription factors in parallel. The methodology uses two robust and easily accessible detection platforms; quantitative real-time PCR for quantitative analysis and DNA microarrays for parallel, higher throughput analysis. <p/>Findings: We test the specificity of the detection platforms with ten inducers and independently validate the transcription factor activation. <p/>Conclusions: We report a methodology for the multiplexed study of transcription factor activation in mammalian cells that is direct and not theoretically limited by the number of available reporters

Connecting the dots: Potential of data integration to identify regulatory snps in late-onset alzheimer's disease GWAS findings

Late-onset Alzheimer's disease (LOAD) is a multifactorial disorder with over twenty loci associated with disease risk. Given the number of genome-wide significant variants that fall outside of coding regions, it is possible that some of these variants alter some function of gene expression rather than tagging coding variants that alter protein structure and/or function. RegulomeDB is a database that annotates regulatory functions of genetic variants. In this study, we utilized RegulomeDB to investigate potential regulatory functions of lead single nucleotide polymorphisms (SNPs) identified in five genome-wide association studies (GWAS) of risk and age-at onset (AAO) of LOAD, as well as SNPs in LD (r2≥0.80) with the lead GWAS SNPs. Of a total 614 SNPs examined, 394 returned RegulomeDB scores of 1-6. Of those 394 variants, 34 showed strong evidence of regulatory function (RegulomeDB score ,3), and only 3 of them were genome-wide significant SNPs (ZCWPW1/ rs1476679, CLU/rs1532278 and ABCA7/rs3764650). This study further supports the assumption that some of the non-coding GWAS SNPs are true associations rather than tagged associations and demonstrates the application of RegulomeDB to GWAS data.©2014 Rosenthal et al

Signatures of arithmetic simplicity in metabolic network architecture

Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that several of the properties predicted by the artificial chemistry model hold for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity

Millimeter-scale genetic gradients and community-level molecular convergence in a hypersaline microbial mat

To investigate the extent of genetic stratification in structured microbial communities, we compared the metagenomes of 10 successive layers of a phylogenetically complex hypersaline mat from Guerrero Negro, Mexico. We found pronounced millimeter-scale genetic gradients that were consistent with the physicochemical profile of the mat. Despite these gradients, all layers displayed near-identical and acid-shifted isoelectric point profiles due to a molecular convergence of amino-acid usage, indicating that hypersalinity enforces an overriding selective pressure on the mat community

GeneWaltz--A new method for reducing the false positives of gene finding

<p>Abstract</p> <p>Background</p> <p>Identifying protein-coding regions in genomic sequences is an essential step in genome analysis. It is well known that the proportion of false positives among genes predicted by current methods is high, especially when the exons are short. These false positives are problematic because they waste time and resources of experimental studies.</p> <p>Methods</p> <p>We developed GeneWaltz, a new filtering method that reduces the risk of false positives in gene finding. GeneWaltz utilizes a codon-to-codon substitution matrix that was constructed by comparing protein-coding regions from orthologous gene pairs between mouse and human genomes. Using this matrix, a scoring scheme was developed; it assigned higher scores to coding regions and lower scores to non-coding regions. The regions with high scores were considered candidate coding regions. One-dimensional Karlin-Altschul statistics was used to test the significance of the coding regions identified by GeneWaltz.</p> <p>Results</p> <p>The proportion of false positives among genes predicted by GENSCAN and Twinscan were high, especially when the exons were short. GeneWaltz significantly reduced the ratio of false positives to all positives predicted by GENSCAN and Twinscan, especially when the exons were short.</p> <p>Conclusions</p> <p>GeneWaltz will be helpful in experimental genomic studies. GeneWaltz binaries and the matrix are available online at <url>http://en.sourceforge.jp/projects/genewaltz/</url>.</p

Genome Desertification in Eutherians: Can Gene Deserts Explain the Uneven Distribution of Genes in Placental Mammalian Genomes?

The evolution of genome size as well as structure and organization of genomes belongs among the key questions of genome biology. Here we show, based on a comparative analysis of 30 genomes, that there is generally a tight correlation between the number of genes per chromosome and the length of the respective chromosome in eukaryotic genomes. The surprising exceptions to this pattern are placental mammalian genomes. We identify the number and, more importantly, the uneven distribution of gene deserts among chromosomes, i.e., long (>500 kb) stretches of DNA that do not encode for genes, as the main contributing factor for the observed anomaly of eutherian genomes. Gene-rich placental mammalian chromosomes have smaller proportions of gene deserts and vice versa. We show that the uneven distribution of gene deserts is a derived character state of eutherians. The functional and evolutionary significance of this particular feature of eutherian genomes remains to be explained