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To the Editor: Over the past two decades, the scale and complexity of genomics technologies and data have advanced from sequencing genomes of a few organisms to generating metagenomes, genome variation, gene expression, metabolites, and phenotype data for thousands of organisms and their communities. A major challenge in this data-rich age of biology is integrating heterogeneous and distributed data into predictive models of biological function, ranging from a single gene to entire organisms and their ecologies. Here we present the DOE Systems Biology Knowledgebase (KBase, http://kbase.us), an open-source software and data platform that enables data sharing, integration, and analysis of microbes, plants, and their communities. Once a Narrative has been shared or made public, other users can copy the Narrative and rerun it on their own data, or modify it to suit their scientific needs. [...]public Narratives serve as resources for the user community by capturing valuable data sets, associated computational analyses, and scientific context describing the rationale behind a scientific study in a form that is immediately reproducible and reusable.

Background Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. Results To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. Conclusions We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.

Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. To overcome this challenge, we developed methods and tools (http://coremodels.mcs.anl.gov) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.

Background Gene fusions are the most powerful type of in silico -derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availability of a large fusion dataset would help probe functional associations and enable systematic analysis of where and why fusion events occur. Results Here we present a systematic analysis of fusions in prokaryotes. We manually generated two training sets: (i) 121 fusions in the model organism Escherichia coli ; (ii) 131 fusions found in B vitamin metabolism. These sets were used to develop a fusion prediction algorithm that captured the training set fusions with only 7 % false negatives and 50 % false positives, a substantial improvement over existing approaches. This algorithm was then applied to identify 3.8 million potential fusions across 11,473 genomes. The results of the analysis are available in a searchable database at http://modelseed.org/projects/fusions/ . A functional analysis identified 3,000 reactions associated with frequent fusion events and revealed areas of metabolism where fusions are particularly prevalent. Conclusions Customary definitions of fusions were shown to be ambiguous, and a stricter one was proposed. Exploring the genes participating in fusion events showed that they most commonly encode transporters, regulators, and metabolic enzymes. The major rationales for fusions between metabolic genes appear to be overcoming pathway bottlenecks, avoiding toxicity, controlling competing pathways, and facilitating expression and assembly of protein complexes. Finally, our fusion dataset provides powerful clues to decipher the biological activities of domains of unknown function.

The representation and processing of alphanumeric symbols in the brain provides an appealing model system for probing mechanisms of brain function and development. Humans use abstract signs, such as letters and numbers, to efficiently tap into distributed brain networks involved in language and mathematics. The invention of written symbol systems occurred too recently in human history for dedicated brain circuits to have evolved for their used. Remarkably, however, after sufficient learning, numbers and letters are found to engage distinct areas along the ventral visual pathway within the occipitotemporal cortex (OTC), including bilateral regions responsive to numerals and a left-lateralized region responsive to letters. The ventral pathway supports object recognition, housing neural populations that represent “what” an input is (i.e., properties of its identity). In humans and non-human primates, the OTC contains a patchwork of areas whose spatial layout is consistent and largely organized around behaviorally- relevant categories of experience, including regions preferentially involved in processing faces, bodies, places, and manipulable objects. Numbers and letters carve out their respective niches within this pre-existing circuitry but, many outstanding questions remain. For instance, why are symbol areas segregated and consistently localized where they are? How and when do they come to acquire their preference for one set of symbols versus another?In this dissertation, I used symbol processing in the ventral stream as a model system for understanding how human learning interacts with innate brain architecture. Our findings were consistent with the idea that long-range connectivity patterns, in terms of axonal wiring and regional communication, determine the location and lateralization of newly-acquired functions in the brain. We further found that brain areas preferentially engaged by Arabic numerals in adults become increasingly tuned to numerals as children go through school, providing novel evidence of experience-dependent plasticity. Our work speaks to several theories of brain organization and development, but perhaps more importantly, opens the door to a number of future directions.