Analysis of wheat SAGE tags reveals evidence for widespread antisense transcription

BACKGROUND: Serial Analysis of Gene Expression (SAGE) is a powerful tool for genome-wide transcription studies. Unlike microarrays, it has the ability to detect novel forms of RNA such as alternatively spliced and antisense transcripts, without the need for prior knowledge of their existence. One limitation of using SAGE on an organism with a complex genome and lacking detailed sequence information, such as the hexaploid bread wheat Triticum aestivum, is accurate annotation of the tags generated. Without accurate annotation it is impossible to fully understand the dynamic processes involved in such complex polyploid organisms. Hence we have developed and utilised novel procedures to characterise, in detail, SAGE tags generated from the whole grain transcriptome of hexaploid wheat. RESULTS: Examination of 71,930 Long SAGE tags generated from six libraries derived from two wheat genotypes grown under two different conditions suggested that SAGE is a reliable and reproducible technique for use in studying the hexaploid wheat transcriptome. However, our results also showed that in poorly annotated and/or poorly sequenced genomes, such as hexaploid wheat, considerably more information can be extracted from SAGE data by carrying out a systematic analysis of both perfect and "fuzzy" (partially matched) tags. This detailed analysis of the SAGE data shows first that while there is evidence of alternative polyadenylation this appears to occur exclusively within the 3' untranslated regions. Secondly, we found no strong evidence for widespread alternative splicing in the developing wheat grain transcriptome. However, analysis of our SAGE data shows that antisense transcripts are probably widespread within the transcriptome and appear to be derived from numerous locations within the genome. Examination of antisense transcripts showing sequence similarity to the Puroindoline a and Puroindoline b genes suggests that such antisense transcripts might have a role in the regulation of gene expression. CONCLUSION: Our results indicate that the detailed analysis of transcriptome data, such as SAGE tags, is essential to understand fully the factors that regulate gene expression and that such analysis of the wheat grain transcriptome reveals that antisense transcripts maybe widespread and hence probably play a significant role in the regulation of gene expression during grain development

Granular packings with moving side walls

The effects of movement of the side walls of a confined granular packing are studied by discrete element, molecular dynamics simulations. The dynamical evolution of the stress is studied as a function of wall movement both in the direction of gravity as well as opposite to it. For all wall velocities explored, the stress in the final state of the system after wall movement is fundamentally different from the original state obtained by pouring particles into the container and letting them settle under the influence of gravity. The original packing possesses a hydrostatic-like region at the top of the container which crosses over to a depth-independent stress. As the walls are moved in the direction opposite to gravity, the saturation stress first reaches a minimum value independent of the wall velocity, then increases to a steady-state value dependent on the wall-velocity. After wall movement ceases and the packing reaches equilibrium, the stress profile fits the classic Janssen form for high wall velocities, while it has some deviations for low wall velocities. The wall movement greatly increases the number of particle-wall and particle-particle forces at the Coulomb criterion. Varying the wall velocity has only small effects on the particle structure of the final packing so long as the walls travel a similar distance.Comment: 11 pages, 10 figures, some figures in colo

A microarray analysis of gene expression in the free-living stages of the parasitic nematode Strongyloides ratti

BACKGROUND: The nematode Strongyloides ratti has two adult phases in its lifecycle: one obligate, female and parasitic and one facultative, dioecious and free-living. The molecular control of the development of this free-living generation remains to be elucidated. RESULTS: We have constructed an S. ratti cDNA microarray and used it to interrogate changes in gene expression during the free-living phase of the S. ratti life-cycle. We have found very extensive differences in gene expression between first-stage larvae (L1) passed in faeces and infective L3s preparing to infect hosts. In L1 stages there was comparatively greater expression of genes involved in growth. We have also compared gene expression in L2 stages destined to develop directly into infective L3s with those destined to develop indirectly into free-living adults. This revealed relatively small differences in gene expression. We find little evidence for the conservation of transcription profiles between S. ratti and S. stercoralis or C. elegans. CONCLUSION: This is the first multi-gene study of gene expression in S. ratti. This has shown that robust data can be generated, with consistent measures of expression within computationally determined clusters and contigs. We find inconsistencies between EST representation data and microarray hybridization data in the identification of genes with stage-specific expression and highly expressed genes. Many of the genes whose expression is significantly different between L1 and iL3s stages are unknown beyond alignments to predicted genes. This highlights the forthcoming challenge in actually determining the role of these genes in the life of S. ratti

Galaxy Integrated Omics:Web-based standards-compliant workflows for proteomics informed by transcriptomics

With the recent advent of RNA-seq technology the proteomics community has begun to generate sample-specific protein databases for peptide and protein identification, an approach we call proteomics informed by transcriptomics (PIT). This approach has gained a lot of interest, particularly among researchers who work with nonmodel organisms or with particularly dynamic proteomes such as those observed in developmental biology and host-pathogen studies. PIT has been shown to improve coverage of known proteins, and to reveal potential novel gene products. However, many groups are impeded in their use of PIT by the complexity of the required data analysis. Necessarily, this analysis requires complex integration of a number of different software tools from at least two different communities, and because PIT has a range of biological applications a single software pipeline is not suitable for all use cases. To overcome these problems, we have created GIO, a software system that uses the well-established Galaxy platform to make PIT analysis available to the typical bench scientist via a simple web interface. Within GIO we provide workflows for four common use cases: a standard search against a reference proteome; PIT protein identification without a reference genome; PIT protein identification using a genome guide; and PIT genome annotation. These workflows comprise individual tools that can be reconfigured and rearranged within the web interface to create new workflows to support additional use cases

Medium Truck Duty Cycle Data from Real-World Driving Environments: Final Report

Since the early part of the 20th century, the US trucking industry has provided a safe and economical means of moving commodities across the country. At present, nearly 80% of US domestic freight movement involves the use of trucks. The US Department of Energy (DOE) is spearheading a number of research efforts to improve heavy vehicle fuel efficiencies. This includes research in engine technologies (including hybrid and fuel cell technologies), lightweight materials, advanced fuels, and parasitic loss reductions. In addition, DOE is developing advanced tools and models to support heavy vehicle research and is leading the 21st Century Truck Partnership and the SuperTruck development effort. Both of these efforts have the common goal of decreasing the fuel consumption of heavy vehicles. In the case of SuperTruck, a goal of improving the overall freight efficiency of a combination tractor-trailer has been established. This Medium Truck Duty Cycle (MTDC) project is a critical element in DOE s vision for improved heavy vehicle energy efficiency; it is unique in that there is no other existing national database of characteristic duty cycles for medium trucks based on collecting data from Class 6 and 7 vehicles. It involves the collection of real-world data on medium trucks for various situational characteristics (e.g., rural/urban, freeway/arterial, congested/free-flowing, good/bad weather) and looks at the unique nature of medium trucks drive cycles (stop-and-go delivery, power takeoff, idle time, short-radius trips). This research provides a rich source of data that can contribute to the development of new tools for FE and modeling, provide DOE a sound basis upon which to make technology investment decisions, and provide a national archive of real-world-based medium-truck operational data to support energy efficiency research. The MTDC project involved a two-part field operational test (FOT). For the Part-1 FOT, three vehicles each from two vocations (urban transit and dry-box delivery) were instrumented for the collection of one year of operational data. The Part-2 FOT involved the towing and recovery and utility vocations for a second year of data collection. The vehicles that participated in the MTDC project did so through gratis partnerships in return for early access to the results of this study. Partnerships such as these are critical to FOTs in which real-world data is being collected. In Part 1 of the project, Oak Ridge National Laboratory (ORNL) established partnerships with the H.T. Hackney Company (HTH), one of the largest wholesale distributors in the country, distributing products to 21 states; and with Knoxville Area Transit (KAT), the city of Knoxville s transit system, which operates across Knoxville and parts of Knox County. These partnerships and agreements provided ORNL access to three Class-7 day-cab tractors that regularly haul 28 ft pup trailers (HTH) and three Class-7 buses for the collection of duty cycle data. In addition, ORNL collaborated with the Federal Motor Carrier Safety Administration (FMCSA) to determine if there were possible synergies between this duty cycle data collection effort and FMCSA s need to learn more about the operation and duty cycles of medium trucks. FMCSA s primary interest was in collecting safety data relative to the driver, carrier, and vehicle. In Part 2 of the project, ORNL partnered with the Knoxville Utilities Board, which made available three Class-8 trucks. Fountain City Wrecker Service was also a Part 2 partner, providing three Class-6 rollback trucks. In order to collect the duty cycle and safety-related data, ORNL developed a data acquisition system (DAS) that was placed on each test vehicle. Each signal recorded in this FOT was collected by means of one of the instruments incorporated into each DAS. Other signals were obtained directly from the vehicle s J1939 and J1708 data buses. A VBOX II Lite collected information available from a global positioning system (GPS), including speed, acceleration, and spatial location information at a rate of 5 Hz for the Part 1 FOT. For the Part 2 FOT, this information was obtained from DAS-based GPS instrumentation. The Air-Weigh LoadMaxx, a self-weighing system that determines the vehicle s gross weight by means of pressure transducers, was used to collect vehicle payload information for the combination, urban transit, and towing and recovery vehicles. A cellular modem, the Raven X EVDO V4221, facilitated the communication between the eDAQ-lite (the data collection engine of the system) and the user. The modem functioned as a wireless gateway, allowing data retrievals and system checks to be performed remotely. Also, in partnership with FMCSA, two additional safety sensors were installed on the combination vehicles: the MGM e-Stroke brake monitoring system and the Tire SafeGuard tire pressure monitoring system. All of these sensors posted data to the J1939 data bus, enabling the signals to be read withou..

Brassica ASTRA: an integrated database for Brassica genomic research

Brassica ASTRA is a public database for genomic information on Brassica species. The database incorporates expressed sequences with Swiss-Prot and GenBank comparative sequence annotation as well as secondary Gene Ontology (GO) annotation derived from the comparison with Arabidopsis TAIR GO annotations. Simple sequence repeat molecular markers are identified within resident sequences and mapped onto the closely related Arabidopsis genome sequence. Bacterial artificial chromosome (BAC) end sequences derived from the Multinational Brassica Genome Project are also mapped onto the Arabidopsis genome sequence enabling users to identify candidate Brassica BACs corresponding to syntenic regions of Arabidopsis. This information is maintained in a MySQL database with a web interface providing the primary means of interrogation. The database is accessible at http://hornbill.cspp.latrobe.edu.au

Machine learning to refine decision making within a syndromic surveillance service

Background: Worldwide, syndromic surveillance is increasingly used for improved and timely situational awareness and early identification of public health threats. Syndromic data streams are fed into detection algorithms, which produce statistical alarms highlighting potential activity of public health importance. All alarms must be assessed to confirm whether they are of public health importance. In England, approximately 100 alarms are generated daily and, although their analysis is formalised through a risk assessment process, the process requires notable time, training, and maintenance of an expertise base to determine which alarms are of public health importance. The process is made more complicated by the observation that only 0.1% of statistical alarms are deemed to be of public health importance. Therefore, the aims of this study were to evaluate machine learning as a tool for computer-assisted human decision-making when assessing statistical alarms. Methods: A record of the risk assessment process was obtained from Public Health England for all 67505 statistical alarms between August 2013 and October 2015. This record contained information on the characteristics of the alarm (e.g. size, location). We used three Bayesian classifiers- naïve Bayes, tree-augmented naïve Bayes and Multinets - to examine the risk assessment record in England with respect to the final ‘Decision’ outcome made by an epidemiologist of ‘Alert’, ‘Monitor’ or ‘No-action’. Two further classifications based upon tree-augmented naïve Bayes and Multinets were implemented to account for the predominance of ‘No-action’ outcomes. Results: The attributes of each individual risk assessment were linked to the final decision made by an epidemiologist, providing confidence in the current process. The naïve Bayesian classifier performed best, correctly classifying 51.5% of ‘Alert’ outcomes. If the ‘Alert’ and ‘Monitor’ actions are combined then performance increases to 82.6% correctly classified. We demonstrate how a decision support system based upon a naïve Bayes classifier could be operationalised within an operational syndromic surveillance system. Conclusions: Within syndromic surveillance systems, machine learning techniques have the potential to make risk assessment following statistical alarms more automated, robust, and rigorous. However, our results also highlight the importance of specialist human input to the process

Simultaneous measurement of the muon neutrino charged-current cross section on oxygen and carbon without pions in the final state at T2K

This paper reports the first simultaneous measurement of the double differential muon neutrino charged-current cross section on oxygen and carbon without pions in the final state as a function of the outgoing muon kinematics, made at the ND280 off-axis near detector of the T2K experiment. The ratio of the oxygen and carbon cross sections is also provided to help validate various models’ ability to extrapolate between carbon and oxygen nuclear targets, as is required in T2K oscillation analyses. The data are taken using a neutrino beam with an energy spectrum peaked at 0.6 GeV. The extracted measurement is compared with the prediction from different Monte Carlo neutrino-nucleus interaction event generators, showing particular model separation for very forward-going muons. Overall, of the models tested, the result is best described using local Fermi gas descriptions of the nuclear ground state with RPA suppression

A novel class of CoA-transferase involved in short-chain fatty acid metabolism in butyrate-producing human colonic bacteria

Peer reviewedPublisher PD