Concept: Microsoft Excel
The spreadsheet software Microsoft Excel, when used with default settings, is known to convert gene names to dates and floating-point numbers. A programmatic scan of leading genomics journals reveals that approximately one-fifth of papers with supplementary Excel gene lists contain erroneous gene name conversions.
SUMMARY: Advances in sequencing technology have greatly reduced the costs incurred in collecting raw sequencing data. Academic laboratories and researchers therefore now have access to very large datasets of genomic alterations but limited time and computational resources to analyze their potential biological importance. Here, we provide a web-based application, Cancer-Related Analysis of VAriants Toolkit (CRAVAT), designed with an easy-to-use interface to facilitate the high-throughput assessment and prioritization of genes and missense alterations important for cancer tumorigenesis. CRAVAT provides predictive scores for germline variants, somatic mutations, and relative gene importance, as well as annotations from published literature and databases. Results are emailed to users as MS Excel spreadsheets and/or tab-separated text files. AVAILABILITY: http://www.cravat.us/ CONTACT: email@example.com SUPPLEMENTARY INFORMATION: Available at Bioinformatics online.
Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R’s multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Availability and implementation: Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010).
Enzyme kinetic parameters are usually determined from initial rates nevertheless, laboratory instruments only measure substrate or product concentration versus reaction time (progress curves). To overcome this problem we present a methodology which uses integrated models based on Michaelis-Menten equation. The most severe practical limitation of progress curve analysis occurs when the enzyme shows a loss of activity under the chosen assay conditions. To avoid this problem it is possible to work with the same experimental points utilized for initial rates determination. This methodology is illustrated by the use of integrated kinetic equations with the well-known reaction catalyzed by alkaline phosphatase enzyme. In this work nonlinear regression was performed with the Solver supplement (Microsoft Office Excel). It is easy to work with and track graphically the convergence of SSE (sum of square errors). The diagnosis of enzyme inhibition was performed according to Akaike information criterion.
Excel2Genie, a simple and user-friendly Microsoft Excel interface, has been developed to the Genie-2000 Spectroscopic Software of Canberra Industries. This Excel application can directly control Canberra Multichannel Analyzer (MCA), process the acquired data and visualize them. Combination of Genie-2000 with Excel2Genie results in remarkably increased flexibility and a possibility to carry out repetitive data acquisitions even with changing parameters and more sophisticated analysis. The developed software package comprises three worksheets: display parameters and results of data acquisition, data analysis and mathematical operations carried out on the measured gamma spectra. At the same time it also allows control of these processes. Excel2Genie is freely available to assist gamma spectrum measurements and data evaluation by the interested Canberra users. With access to the Visual Basic Application (VBA) source code of this application users are enabled to modify the developed interface according to their intentions.
Contaminated site remediation is generally difficult, time consuming, and expensive. As a result ranking may aid in efficient allocation of resources. In order to rank the priorities of contaminated sites, input parameters relevant to contaminant fate and transport, and exposure assessment should be as accurate as possible. Yet, in most cases these parameters are vague or not precise. Most of the current remediation priority ranking methodologies overlook the vagueness in parameter values or do not go beyond assigning a contaminated site to a risk class. The main objective of this study is to develop an alternative remedial priority ranking system (RPRS) for contaminated sites in which vagueness in parameter values is considered. RPRS aims to evaluate potential human health risks due to contamination using sufficiently comprehensive and readily available parameters in describing the fate and transport of contaminants in air, soil, and groundwater. Vagueness in parameter values is considered by means of fuzzy set theory. A fuzzy expert system is proposed for the evaluation of contaminated sites and a software (ConSiteRPRS) is developed in Microsoft Office Excel 2007 platform. Rankings are employed for hypothetical and real sites. Results show that RPRS is successful in distinguishing between the higher and lower risk cases.
Graphing is socially significant for behavior analysts; however, graphing can be difficult to learn. Video modeling (VM) may be a useful instructional method but lacks evidence for effective teaching of computer skills. A between-groups design compared the effects of VM, text-based instruction, and no instruction on graphing performance. Participants who used VM constructed graphs significantly faster and with fewer errors than those who used text-based instruction or no instruction. Implications for instruction are discussed.
A new method was developed and implemented into an Excel Visual Basic for Applications (VBAs) algorithm utilizing trigonometry laws in an innovative way to overlap recession segments of time series and create master recession curves (MRCs). Based on a trigonometry approach, the algorithm horizontally translates succeeding recession segments of time series, placing their vertex, that is, the highest recorded value of each recession segment, directly onto the appropriate connection line defined by measurement points of a preceding recession segment. The new method and algorithm continues the development of methods and algorithms for the generation of MRC, where the first published method was based on a multiple linear/nonlinear regression model approach (Posavec et al. ). The newly developed trigonometry-based method was tested on real case study examples and compared with the previously published multiple linear/nonlinear regression model-based method. The results show that in some cases, that is, for some time series, the trigonometry-based method creates narrower overlaps of the recession segments, resulting in higher coefficients of determination R(2) , while in other cases the multiple linear/nonlinear regression model-based method remains superior. The Excel VBA algorithm for modeling MRC using the trigonometry approach is implemented into a spreadsheet tool (MRCTools v3.0 written by and available from Kristijan Posavec, Zagreb, Croatia) containing the previously published VBA algorithms for MRC generation and separation. All algorithms within the MRCTools v3.0 are open access and available free of charge, supporting the idea of running science on available, open, and free of charge software.
Despite theoretical evidence that the model commonly referred to as the 3500-kcal rule grossly overestimates actual weight loss, widespread application of the 3500-kcal formula continues to appear in textbooks, on respected government- and health-related websites, and scientific research publications. Here we demonstrate the risk of applying the 3500-kcal rule even as a convenient estimate by comparing predicted against actual weight loss in seven weight loss experiments conducted in confinement under total supervision or objectively measured energy intake. We offer three newly developed, downloadable applications housed in Microsoft Excel and Java, which simulates a rigorously validated, dynamic model of weight change. The first two tools available at http://www.pbrc.edu/sswcp, provide a convenient alternative method for providing patients with projected weight loss/gain estimates in response to changes in dietary intake. The second tool, which can be downloaded from the URL http://www.pbrc.edu/mswcp, projects estimated weight loss simultaneously for multiple subjects. This tool was developed to inform weight change experimental design and analysis. While complex dynamic models may not be directly tractable, the newly developed tools offer the opportunity to deliver dynamic model predictions as a convenient and significantly more accurate alternative to the 3500-kcal rule.
Genomic datasets accompanying scientific publications show a surprisingly high rate of gene name corruption. This error is generated when files and tables are imported into Microsoft Excel and certain gene symbols are automatically converted into dates.