Geographical information system parallelization for spatial big data processing: a review

With the increasing interest in large-scale, high-resolution and real-time geographic information system (GIS) applications and spatial big data processing, traditional GIS is not efficient enough to handle the required loads due to limited computational capabilities.Various attempts have been made to adopt high performance computation techniques from different applications, such as designs of advanced architectures, strategies of data partition and direct parallelization method of spatial analysis algorithm, to address such challenges. This paper surveys the current state of parallel GIS with respect to parallel GIS architectures, parallel processing strategies, and relevant topics. We present the general evolution of the GIS architecture which includes main two parallel GIS architectures based on high performance computing cluster and Hadoop cluster. Then we summarize the current spatial data partition strategies, key methods to realize parallel GIS in the view of data decomposition and progress of the special parallel GIS algorithms. We use the parallel processing of GRASS as a case study. We also identify key problems and future potential research directions of parallel GIS.     

Condition-dependent cell volume and concentration of Escherichia coli to facilitate data conversion for systems biology modeling

Systems biology modeling typically requires quantitative experimental data such as intracellular concentrations or copy numbers per cell. In order to convert population-averaging omics measurement data to intracellular concentrations or cellular copy numbers, the total cell volume and number of cells in a sample need to be known. Unfortunately, even for the often studied model bacterium Escherichia coli this information is hardly available and furthermore, certain measures (e.g. cell volume) are also dependent on the growth condition. In this work, we have determined these basic data for E. coli cells when grown in 22 different conditions so that respective data conversions can be done correctly. First, we determine growth-rate dependent cell volumes. Second, we show that in a 1 ml E. coli sample at an optical density (600 nm) of 1 the total cell volume is around 3.6 μl for all conditions tested. Third, we demonstrate that the cell number in a sample can be determined on the basis of the sample's optical density and the cells' growth rate. The data presented will allow for conversion of E. coli measurement data normalized to optical density into volumetric cellular concentrations and copy numbers per cell--two important parameters for systems biology model development.     

NetMatch: a Cytoscape plugin for searching biological networks

NetMatch is a Cytoscape plugin which allows searching biological networks for subcomponents matching a given query. Queries may be approximate in the sense that certain parts of the subgraph-query may be left unspecified. To make the query creation process easy, a drawing tool is provided. Cytoscape is a bioinformatics software platform for the visualization and analysis of biological networks. AVAILABILITY: The full package, a tutorial and associated examples are available at the following web sites: http://alpha.dmi.unict.it/~ctnyu/netmatch.html, http://baderlab.org/Software/NetMatch.     

CytoITMprobe: a network information flow plugin for Cytoscape

ABSTRACT: BACKGROUND: Cytoscape is a well-developed flexible platform for visualization, integration and analysis of network data. Apart from the sophisticated graph layout and visualization routines, it hosts numerous user-developed plugins that significantly extend its core functionality. Earlier, we developed a network information flow framework and implemented it as a web application, called ITM Probe. Given a context consisting of one or more user-selected nodes, ITM Probe retrieves other network nodes most related to that context. It requires neither user restriction to subnetwork of interest nor additional and possibly noisy information. However, plugins for Cytoscape with these features do not yet exist. To provide the Cytoscape users the possibility of integrating ITM Probe into their workflows, we developed CytoITMprobe, a new Cytoscape plugin. FINDINGS: CytoITMprobe maintains all the desirable features of ITM Probe and adds additional flexibility not achievable through its web service version. It provides access to ITM Probe either through a web server or locally. The input, consisting of a Cytoscape network, together with the desired origins and/or destinations of information and a dissipation coefficient, is specified through a query form. The results are shown as a subnetwork of significant nodes and several summary tables. Users can control the composition and appearance of the subnetwork and interchange their ITM Probe results with other software tools through tab-delimited files. CONCLUSIONS: The main strength of CytoITMprobe is its flexibility. It allows the user to specify as input any Cytoscape network, rather than being restricted to the pre-compiled protein-protein interaction networks available through the ITM Probe web service. Users may supply their own edge weights and directionalities. Consequently, as opposed to ITM Probe web service, CytoITMprobe can be applied to many other domains of network-based research beyond protein-networks. It also enables seamless integration of ITM Probe results with other Cytoscape plugins having complementary functionality for data analysis.     

Towards unbiased parentage assignment: combining genetic, behavioural and spatial data in a Bayesian framework

Inferring the parentage of a sample of individuals is often a prerequisite for many types of analysis in molecular ecology, evolutionary biology and quantitative genetics. In all but a few cases, the method of parentage assignment is divorced from the methods used to estimate the parameters of primary interest, such as mate choice or heritability. Here we present a Bayesian approach that simultaneously estimates the parentage of a sample of individuals and a wide range of population-level parameters in which we are interested. We show that joint estimation of parentage and population-level parameters increases the power of parentage assignment, reduces bias in parameter estimation, and accurately evaluates uncertainty in both. We illustrate the method by analysing a number of simulated test data sets, and through a re-analysis of parentage in the Seychelles warbler, Acrocephalus sechellensis. A combination of behavioural, spatial and genetic data are used in the analyses and, importantly, the method does not require strong prior information about the relationship between nongenetic data and parentage.     

Bayesian meta-analysis models for microarray data: a comparative study

Background

With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Results

Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.

Conclusion

The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.     

PathVisio-Validator: a rule-based validation plugin for graphical pathway notations

PURPOSE: The PathVisio-Validator plugin aims to simplify the task of producing biological pathway diagrams that follow graphical standardized notations, such as Molecular Interaction Maps or the Systems Biology Graphical Notation. This plugin assists in the creation of pathway diagrams by ensuring correct usage of a notation, and thereby reducing ambiguity when diagrams are shared among biologists. Rulesets, needed in the validation process, can be generated for any graphical notation that a developer desires, using either Schematron or Groovy. The plugin also provides support for filtering validation results, validating on a subset of rules, and distinguishing errors and warnings.     

Bacterivorous grazers facilitate organic matter decomposition: a stoichiometric modeling approach

There is widespread empirical evidence that protist grazing on bacteria reduces bacterial abundances but increases bacteria-mediated decomposition of organic matter. This paradox has been noted repeatedly in the microbiology literature but lacks a generally accepted mechanistic explanation. To explain this paradox quantitatively, we develop a bacteria-grazer model of organic matter decomposition that incorporates protozoa-driven nutrient recycling and stoichiometry. Unlike previous efforts, the current model includes explicit limitation, via Liebig's law of minimum, by two possible factors, nutrient and carbon densities, as well as their relative ratios in bacteria and grazers. Our model shows two principal results: (1) when the environment is carbon limiting, organic matter can always be decomposed completely, regardless of the presence/absence of grazers; (2) when the environment is nutrient (such as nitrogen) limiting, it is possible for organic matter to be completely decomposed in the presence, but not absence, of grazers. Grazers facilitate decomposition by releasing nutrients back into the environment, which would otherwise be limiting, while preying upon bacteria. Model analysis reveals that facilitation of organic matter decomposition by grazers is positively related to the stoichiometric difference between bacteria and grazers. In addition, we predict the existence of an optimal density range of introduced grazers, which maximally facilitate the decomposition of organic matter in a fixed time period. This optimal range reflects a trade-off between grazer-induced nutrient recycling and grazer-induced mortality of bacteria.     

Chlorophyll a fluorescence transients: a fast data acquisition system to facilitate in vivo measurements

Characteristics of primary phases in chlorophyll a fluorescence transients based on room temperature in vivo measurement with a Plant Productivity Fluorometer (Brancker Model SF-10) can be greatly facilitated by coupling the instrument to a fast data acquisition system. The SF-10 was linked to a Multitech Industrial Corporation Microprofessor Microcomputer and further modified to ensure simultaneous onset of light activation and signal capture. Circuit diagrams and program listings are given in detail. This microprocessor system is capable of capturing signal changes over a minimum period of 200 milliseconds to a maximum of 6 seconds. Accuracy of recorded data is dependent on rate of change of the input signal and the recording time period. Acquisition and storage of 5000 points from zero to 300 milliseconds ensured clear resolution of F_o, I and D when played back over 120 seconds on a chart recorder. For routine use, the primary transient can be captured over 0–2 seconds and then replayed as an accompaniment to standard slower presentation of primary plus secondary transients. Coincidence of signal amplitude for F_p on both systems can then be ascertained while retaining adequate resolution of F_o and I.     

WordCloud: a Cytoscape plugin to create a visual semantic summary of networks

Background  

When biological networks are studied, it is common to look for clusters, i.e. sets of nodes that are highly inter-connected. To understand the biological meaning of a cluster, the user usually has to sift through many textual annotations that are associated with biological entities.     

Chlorophyll a fluorescence transients: a fast data acquisition system to facilitate in vivo measurements

Characteristics of primary phases in chlorophyll a fluorescence transients based on room temperature in vivo measurement with a Plant Productivity Fluorometer (Brancker Model SF-10) can be greatly facilitated by coupling the instrument to a fast data acquisition system. The SF-10 was linked to a Multitech Industrial Corporation Microprofessor Microcomputer and further modified to ensure simultaneous onset of light activation and signal capture. Circuit diagrams and program listings are given in detail. This microprocessor system is capable of capturing signal changes over a minimum period of 200 milliseconds to a maximum of 6 seconds. Accuracy of recorded data is dependent on rate of change of the input signal and the recording time period. Acquisition and storage of 5000 points from zero to 300 milliseconds ensured clear resolution of F_o, I and D when played back over 120 seconds on a chart recorder. For routine use, the primary transient can be captured over 0–2 seconds and then replayed as an accompaniment to standard slower presentation of primary plus secondary transients. Coincidence of signal amplitude for F_p on both systems can then be ascertained while retaining adequate resolution of F_o and I.     

BiNoM: a Cytoscape plugin for manipulating and analyzing biological networks

BiNoM (Biological Network Manager) is a new bioinformatics software that significantly facilitates the usage and the analysis of biological networks in standard systems biology formats (SBML, SBGN, BioPAX). BiNoM implements a full-featured BioPAX editor and a method of 'interfaces' for accessing BioPAX content. BiNoM is able to work with huge BioPAX files such as whole pathway databases. In addition, BiNoM allows the analysis of networks created with CellDesigner software and their conversion into BioPAX format. BiNoM comes as a library and as a Cytoscape plugin which adds a rich set of operations to Cytoscape such as path and cycle analysis, clustering sub-networks, decomposition of network into modules, clipboard operations and others. AVAILABILITY: Last version of BiNoM distributed under the LGPL licence together with documentation, source code and API are available at http://bioinfo.curie.fr/projects/binom