BARCRAWL and BARTAB: software tools for the design and implementation of barcoded primers for highly multiplexed DNA sequencing

<p>Abstract</p> <p>Background</p> <p>Advances in automated DNA sequencing technology have greatly increased the scale of genomic and metagenomic studies. An increasingly popular means of increasing project throughput is by multiplexing samples during the sequencing phase. This can be achieved by covalently linking short, unique "barcode" DNA segments to genomic DNA samples, for instance through incorporation of barcode sequences in PCR primers. Although several strategies have been described to insure that barcode sequences are unique and robust to sequencing errors, these have not been integrated into the overall primer design process, thus potentially introducing bias into PCR amplification and/or sequencing steps.</p> <p>Results</p> <p><it>Barcrawl </it>is a software program that facilitates the design of barcoded primers, for multiplexed high-throughput sequencing. The program <it>bartab </it>can be used to deconvolute DNA sequence datasets produced by the use of multiple barcoded primers. This paper describes the functions implemented by <it>barcrawl </it>and <it>bartab </it>and presents a proof-of-concept case study of both programs in which barcoded rRNA primers were designed and validated by high-throughput sequencing.</p> <p>Conclusion</p> <p><it>Barcrawl </it>and <it>bartab </it>can benefit researchers who are engaged in metagenomic projects that employ multiplexed specimen processing. The source code is released under the GNU general public license and can be accessed at <url>http://www.phyloware.com</url>.</p

Development of satiating and palatable high-protein meat products by using experimental design in food technology

Background and objectives: Foods high in protein are known to satiate more fully than foods high in other constituents. One challenge with these types of food is the degree of palatability. This study was aimed at developing the frankfurter style of sausages that would regulate food intake as well as being the preferred food choice of the consumer. Design and measures: 16 sausage varieties with commercial (PE% 20) or higher amount of protein (PE% 40), being modified with vegetable fat (3% of rapeseed oil), and smoked or not, underwent a sensory descriptive analysis, in which the information was used to choose a subsample of four sausages for a satiety test. Twenty-seven subjects were recruited based on liking and frequency of sausage consumption. The participants ranged in age from 20 to 28, and in body mass index (BMI) between 19.6 and 30.9. The students were served a sausage meal for five consecutive days and then filled out a questionnaire to describe their feelings of hunger, satiety, fullness, desire to eat an their prospective consumption on a visual analogue scale (VAS) starting from right before, right after the meal, every half hour for 4 h until the next meal was served, and right after the second meal. Results and conclusion: The higher protein sausages were less juicy, oily, fatty, adhesive, but harder and more granular than with lower amount of protein. The high-protein sausages were perceived as more satiating the first 90&#x2009;min after the first meal. Some indication of satiety effect of added oil versus meat fat. No significant differences in liking among the four sausage varieties

Bandit Models of Human Behavior: Reward Processing in Mental Disorders

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for multi-armed bandit problem, which extends the standard Thompson Sampling approach to incorporate reward processing biases associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. We demonstrate empirically that the proposed parametric approach can often outperform the baseline Thompson Sampling on a variety of datasets. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions.Comment: Conference on Artificial General Intelligence, AGI-1

Initial steps towards a production platform for DNA sequence analysis on the grid

ABSTRACT: BACKGROUND: Bioinformatics is confronted with a new data explosion due to the availability of high throughput DNA sequencers. Data storage and analysis becomes a problem on local servers, and therefore it is needed to switch to other IT infrastructures. Grid and workflow technology can help to handle the data more efficiently, as well as facilitate collaborations. However, interfaces to grids are often unfriendly to novice users. RESULTS: In this study we reused a platform that was developed in the VL-e project for the analysis of medical images. Data transfer, workflow execution and job monitoring are operated from one graphical interface. We developed workflows for two sequence alignment tools (BLAST and BLAT) as a proof of concept. The analysis time was signicantly reduced. All workflows and executables are available for the members of the Dutch Life Science Grid and the VL-e Medical virtual organizations. All components are open source and can be transported to other grid infrastructures. CONCLUSIONS: The availability of in-house expertise and tools facilitates the usage of grid resources by new users. Our first results indicate that this is a practical, powerful and scalable solution to address the capacity and collaboration issues raised by the deployment of next generation sequencers. We currently adopt this methodology on a daily basis for DNA sequencing and other applications. More information and source code is available via http://www.bioinformaticslaboratory.nl

Effects of air pollution and the introduction of the London Low Emission Zone on the prevalence of respiratory and allergic symptoms in schoolchildren in East London: a sequential cross-sectional study

The adverse effects of traffic-related air pollution on children’s respiratory health have been widely reported, but few studies have evaluated the impact of traffic-control policies designed to reduce urban air pollution. We assessed associations between traffic-related air pollutants and respiratory/allergic symptoms amongst 8–9 year-old schoolchildren living within the London Low Emission Zone (LEZ). Information on respiratory/allergic symptoms was obtained using a parent-completed questionnaire and linked to modelled annual air pollutant concentrations based on the residential address of each child, using a multivariable mixed effects logistic regression analysis. Exposure to traffic-related air pollutants was associated with current rhinitis: NOx (OR 1.01, 95% CI 1.00–1.02), NO2 (1.03, 1.00–1.06), PM10 (1.16, 1.04–1.28) and PM2.5 (1.38, 1.08–1.78), all per μg/m3 of pollutant, but not with other respiratory/allergic symptoms. The LEZ did not reduce ambient air pollution levels, or affect the prevalence of respiratory/allergic symptoms over the period studied. These data confirm the previous association between traffic-related air pollutant exposures and symptoms of current rhinitis. Importantly, the London LEZ has not significantly improved air quality within the city, or the respiratory health of the resident population in its first three years of operation. This highlights the need for more robust measures to reduce traffic emissions

Microbial ligand costimulation drives neutrophilic steroid-refractory asthma

Funding: The authors thank the Wellcome Trust (102705) and the Universities of Aberdeen and Cape Town for funding. This research was also supported, in part, by National Institutes of Health GM53522 and GM083016 to DLW. KF and BNL are funded by the Fonds Wetenschappelijk Onderzoek, BNL is the recipient of an European Research Commission consolidator grant and participates in the European Union FP7 programs EUBIOPRED and MedALL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Improving SNR and reducing training time of classifiers in large datasets via kernel averaging

Kernel methods are of growing importance in neuroscience research. As an elegant extension of linear methods, they are able to model complex non-linear relationships. However, since the kernel matrix grows with data size, the training of classifiers is computationally demanding in large datasets. Here, a technique developed for linear classifiers is extended to kernel methods: In linearly separable data, replacing sets of instances by their averages improves signal-to-noise ratio (SNR) and reduces data size. In kernel methods, data is linearly non-separable in input space, but linearly separable in the high-dimensional feature space that kernel methods implicitly operate in. It is shown that a classifier can be efficiently trained on instances averaged in feature space by averaging entries in the kernel matrix. Using artificial and publicly available data, it is shown that kernel averaging improves classification performance substantially and reduces training time, even in non-linearly separable data

Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

Does publication bias inflate the apparent efficacy of psychological treatment for major depressive disorder? A systematic review and meta-analysis of US national institutes of health-funded trials

Background The efficacy of antidepressant medication has been shown empirically to be overestimated due to publication bias, but this has only been inferred statistically with regard to psychological treatment for depression. We assessed directly the extent of study publication bias in trials examining the efficacy of psychological treatment for depression. Methods and Findings We identified US National Institutes of Health grants awarded to fund randomized clinical trials comparing psychological treatment to control conditions or other treatments in patients diagnosed with major depressive disorder for the period 1972–2008, and we determined whether those grants led to publications. For studies that were not published, data were requested from investigators and included in the meta-analyses. Thirteen (23.6%) of the 55 funded grants that began trials did not result in publications, and two others never started. Among comparisons to control conditions, adding unpublished studies (Hedges’ g = 0.20; CI95% -0.11~0.51; k = 6) to published studies (g = 0.52; 0.37~0.68; k = 20) reduced the psychotherapy effect size point estimate (g = 0.39; 0.08~0.70) by 25%. Moreover, these findings may overestimate the "true" effect of psychological treatment for depression as outcome reporting bias could not be examined quantitatively. Conclusion The efficacy of psychological interventions for depression has been overestimated in the published literature, just as it has been for pharmacotherapy. Both are efficacious but not to the extent that the published literature would suggest. Funding agencies and journals should archive both original protocols and raw data from treatment trials to allow the detection and correction of outcome reporting bias. Clinicians, guidelines developers, and decision makers should be aware that the published literature overestimates the effects of the predominant treatments for depression