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Woodside M.T.,University of Alberta | Woodside M.T.,Canadian National Institute For Nanotechnology | Block S.M.,Stanford University
Annual Review of Biophysics | Year: 2014

Folding may be described conceptually in terms of trajectories over a landscape of free energies corresponding to different molecular configurations. In practice, energy landscapes can be difficult to measure. Single-molecule force spectroscopy (SMFS), whereby structural changes are monitored in molecules subjected to controlled forces, has emerged as a powerful tool for probing energy landscapes. We summarize methods for reconstructing landscapes from force spectroscopy measurements under both equilibrium and nonequilibrium conditions. Other complementary, but technically less demanding, methods provide a model-dependent characterization of key features of the landscape. Once reconstructed, energy landscapes can be used to study critical folding parameters, such as the characteristic transition times required for structural changes and the effective diffusion coefficient setting the timescale for motions over the landscape. We also discuss issues that complicate measurement and interpretation, including the possibility of multiple states or pathways and the effects of projecting multiple dimensions onto a single coordinate. Copyright © 2014 by Annual Reviews. All rights reserved.

Xia J.,McGill University | Sinelnikov I.V.,University of Alberta | Han B.,University of Alberta | Wishart D.S.,Canadian National Institute For Nanotechnology
Nucleic acids research | Year: 2015

MetaboAnalyst (www.metaboanalyst.ca) is a web server designed to permit comprehensive metabolomic data analysis, visualization and interpretation. It supports a wide range of complex statistical calculations and high quality graphical rendering functions that require significant computational resources. First introduced in 2009, MetaboAnalyst has experienced more than a 50X growth in user traffic (>50 000 jobs processed each month). In order to keep up with the rapidly increasing computational demands and a growing number of requests to support translational and systems biology applications, we performed a substantial rewrite and major feature upgrade of the server. The result is MetaboAnalyst 3.0. By completely re-implementing the MetaboAnalyst suite using the latest web framework technologies, we have been able substantially improve its performance, capacity and user interactivity. Three new modules have also been added including: (i) a module for biomarker analysis based on the calculation of receiver operating characteristic curves; (ii) a module for sample size estimation and power analysis for improved planning of metabolomics studies and (iii) a module to support integrative pathway analysis for both genes and metabolites. In addition, popular features found in existing modules have been significantly enhanced by upgrading the graphical output, expanding the compound libraries and by adding support for more diverse organisms. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Bergren A.J.,Canadian National Institute For Nanotechnology | McCreery R.L.,University of Alberta
Annual Review of Analytical Chemistry | Year: 2011

This review discusses the analytical characterization of molecular electronic devices and structures relevant thereto. In particular, we outline the methods for probing molecular junctions, which contain an ensemble of molecules between two contacts. We discuss the analytical methods that aid in the fabrication and characterization of molecular junctions, beginning with the confirmation of the placement of a molecular layer on a conductive or semiconductive substrate. We emphasize methods that provide information about the molecular layer in the junction and outline techniques to ensure molecular layer integrity after the complete fabrication of a device. In addition, we discuss the analytical information derived during the actual device operation. Copyright © 2011 by Annual Reviews. All rights reserved.

Torres E.,Canadian National Institute For Nanotechnology | Dilabio G.A.,Canadian National Institute For Nanotechnology | Dilabio G.A.,University of Alberta
Journal of Physical Chemistry Letters | Year: 2012

B3LYP is the most widely used density-functional theory (DFT) approach because it is capable of accurately predicting molecular structures and other properties. However, B3LYP is not able to reliably model systems in which noncovalent interactions are important. Here we present a method that corrects this deficiency in B3LYP by using dispersion-correcting potentials (DCPs). DCPs are utilized by simple modifications to input files and can be used in any computational package that can read effective-core potentials. Therefore, the technique requires no programming. DCPs (developed for H, C, N, and O) produce the best results when used in conjunction with 6-31+G(2d,2p) basis sets. The B3LYP-DCP approach was tested on the S66, S22, and HSG-A benchmark sets of noncovalently interacting dimers and trimers and was found to, on average, significantly outperform almost all other DFT-based methods that were designed to treat van der Waals interactions. Users of B3LYP who wish to model systems in which noncovalent interactions (viz., steric repulsion, hydrogen bonding, π-stacking) are present, should consider B3LYP-DCP. © Published 2012 by the American Chemical Society.

Luber E.J.,Canadian National Institute For Nanotechnology | Luber E.J.,University of Alberta | Buriak J.M.,Canadian National Institute For Nanotechnology | Buriak J.M.,University of Alberta
ACS Nano | Year: 2013

Research into organic photovoltaics (OPVs) is rapidly growing worldwide because it offers a route to low temperature, inexpensive processing of lightweight, flexible solar cells that can be mass manufactured cheaply. Unlike silicon or other inorganic semiconductors (e.g., CdTe, CIGs), OPVs are complicated by the requirement of having multiple materials and layers that must be integrated to enable the cell to function. The enormous number of research hours required to optimize all aspects of OPVs and to integrate them successfully is typically boiled down to one number-the power conversion efficiency (PCE) of the device. The PCE is the value by which comparisons are routinely made when modifications are made to devices; new bulk heterojunction materials, electron- and hole-transport layers, electrodes, plasmonic additives, and many other new advances are incorporated into OPV devices and compared with one, or a series of, control device(s). The concern relates to the statistical significance of this all-important efficiency/PCE value: is the observed change or improvement in performance truly greater than experimental error? If it is not, then the field can and will be misled by improper reporting of efficiencies, and future research in OPVs could be frustrated and, ultimately, irreversibly damaged. In this Perspective, the dangers of, for instance, cherry-picking of data and poor descriptions of experimental procedures, are outlined, followed by a discussion of a real data set of OPV devices, and how a simple and easy statistical treatment can help to distinguish between results that are indistinguishable experimentally, and those that do appear to be different. © 2013 American Chemical Society.

Xia J.,University of Alberta | Wishart D.S.,University of Alberta | Wishart D.S.,Canadian National Institute For Nanotechnology
Nature Protocols | Year: 2011

MetaboAnalyst is an integrated web-based platform for comprehensive analysis of quantitative metabolomic data. It is designed to be used by biologists (with little or no background in statistics) to perform a variety of complex metabolomic data analysis tasks. These include data processing, data normalization, statistical analysis and high-level functional interpretation. This protocol provides a step-wise description on how to format and upload data to MetaboAnalyst, how to process and normalize data, how to identify significant features and patterns through univariate and multivariate statistical methods and, finally, how to use metabolite set enrichment analysis and metabolic pathway analysis to help elucidate possible biological mechanisms. The complete protocol can be executed in ∼45 min. © 2011 Nature America, Inc. All rights reserved.

Buriak J.M.,University of Alberta | Buriak J.M.,Canadian National Institute For Nanotechnology
Chemistry of Materials | Year: 2014

Silicon is the cornerstone material of the semiconductor industry. As feature sizes on chips continue to decrease in size, the ratio of surface to bulk increases, and as a result, the role of surface defects, surface states and other subtle features play larger roles in the functioning of the device. Although silicon oxides have served the industry well as the passivation chemistry of choice, there is interest in expanding the repertoire of accessible and efficient chemical functional strategies available for use, and to fully understand the nature of these interfaces. For new applications such as molecular electronics on silicon and biochips, for example, there is a need to avoid the layer of intervening insulating oxide: A well-defined linkage of organic molecules through a silicon-carbon bond has great promise and appeal. Hydrosilylation, the insertion of an alkene or alkyne into a surface Si-H bond, is an ideal approach to producing these covalent Si-C bonds, and can be carried out in a number of ways. Light-promoted hydrosilylation is promising because it is clean and direct and can be patterned via masking; it requires no additional reagents such as catalysts or input of thermal energy and thus may have reduced surface contamination and numbers of defects. In this perspective, we start by making connections between the molecular silane literature, and the first reports of UV-mediated hydrosilylation of an alkene on a silicon surface, a reaction that was assumed to operate via a radical mechanism. We then describe the unexpected development of four new mechanisms that have no obvious parallels with the molecular silane literature, and take place as a result of the solid state electronics of the underlying silicon itself. From exciton involvement, to the influence of plasmonics, to the role of photoemission, the area of silicon surface hydrosilylation has become incredibly rich, and undoubtedly still contains new reactivity to be discovered. © 2013 American Chemical Society.

Wishart D.S.,Canadian National Institute For Nanotechnology | Wishart D.S.,University of Alberta
Progress in Nuclear Magnetic Resonance Spectroscopy | Year: 2011

A review of the Progress in Nuclear Magnetic Resonance Spectroscopy journal discusses the roles that chemical shifts can play in understanding and interpreting protein structure and protein dynamics. The focus of the review is limited primarily to the interpretation of reported peptide and protein chemical shifts, which are diamagnetic and isotropic in character. It is expected that kind of review will help increase the awareness of chemical shifts in the NMR community and that it will clarify certain issues pertaining to chemical shifts in biomolecular NMR. It is expected that it will offer new insights into how chemical shifts can be used in protein NMR and give greater confidence to those spectroscopists who are specifically 'NOE-centric' or those who are relatively new to the field to start routinely using chemical shifts in analyzing their protein structures.

Xia J.,University of Alberta | Wishart D.S.,University of Alberta | Wishart D.S.,Canadian National Institute For Nanotechnology
Nucleic Acids Research | Year: 2010

Gene set enrichment analysis (GSEA) is a widely used technique in transcriptomic data analysis that uses a database of predefined gene sets to rank lists of genes from microarray studies to identify significant and coordinated changes in gene expression data. While GSEA has been playing a significant role in understanding transcriptomic data, no similar tools are currently available for understanding metabolomic data. Here, we introduce a web-based server, called Metabolite Set Enrichment Analysis (MSEA), to help researchers identify and interpret patterns of human or mammalian metabolite concentration changes in a biologically meaningful context. Key to the development of MSEA has been the creation of a library of ~1000 predefined metabolite sets covering various metabolic pathways, disease states, biofluids, and tissue locations. MSEA also supports user-defined or custom metabolite sets for more specialized analysis. MSEA offers three different enrichment analyses for metabolomic studies including overrepresentation analysis (ORA), single sample profiling (SSP) and quantitative enrichment analysis (QEA). ORA requires only a list of compound names, while SSP and QEA require both compound names and compound concentrations. MSEA generates easily understood graphs or tables embedded with hyperlinks to relevant pathway images and disease descriptors. For nonmammalian or more specialized metabolomic studies, MSEA allows users to provide their own metabolite sets for enrichment analysis. The MSEA server also supports conversion between metabolite common names, synonyms, and major database identifiers. MSEA has the potential to help users identify obvious as well as 'subtle but coordinated' changes among a group of related metabolites that may go undetected with conventional approaches. MSEA is freely available at http://www.msea.ca. © The Author(s) 2010. Published by Oxford University Press.

Xia J.,Canadian National Institute For Nanotechnology | Mandal R.,Canadian National Institute For Nanotechnology | Sinelnikov I.V.,Canadian National Institute For Nanotechnology | Broadhurst D.,Canadian National Institute For Nanotechnology | Wishart D.S.,Canadian National Institute For Nanotechnology
Nucleic Acids Research | Year: 2012

First released in 2009, MetaboAnalyst (www.metaboanalyst.ca) was a relatively simple web server designed to facilitate metabolomic data processing and statistical analysis. With continuing advances in metabolomics along with constant user feedback, it became clear that a substantial upgrade to the original server was necessary. MetaboAnalyst 2.0, which is the successor to MetaboAnalyst, represents just such an upgrade. MetaboAnalyst 2.0 now contains dozens of new features and functions including new procedures for data filtering, data editing and data normalization. It also supports multi-group data analysis, two-factor analysis as well as time-series data analysis. These new functions have also been supplemented with: (i) a quality-control module that allows users to evaluate their data quality before conducting any analysis, (ii) a functional enrichment analysis module that allows users to identify biologically meaningful patterns using metabolite set enrichment analysis and (iii) a metabolic pathway analysis module that allows users to perform pathway analysis and visualization for 15 different model organisms. In developing MetaboAnalyst 2.0 we have also substantially improved its graphical presentation tools. All images are now generated using anti-aliasing and are available over a range of resolutions, sizes and formats (PNG, TIFF, PDF, PostScript, or SVG). To improve its performance, MetaboAnalyst 2.0 is now hosted on a much more powerful server with substantially modified code to take advantage the server's multi-core CPUs for computationally intensive tasks. MetaboAnalyst 2.0 also maintains a collection of 50 or more FAQs and more than a dozen tutorials compiled from user queries and requests. A downloadable version of MetaboAnalyst 2.0, along detailed instructions for local installation is now available as well. © 2012 The Author(s).

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