A visualization and segmentation toolbox for electron microscopy

Three-dimensional image analysis includes image processing, segmentation and visualization operations, which facilitate the interpretation of data. We have developed a toolbox for three-dimensional (3D) electron microscopy (EM) in Amira, which is a commercial software package, used by many laboratories. Our toolbox integrates a number of established procedures specifically tailored for 3D EM. These include input-output, filtering, segmentation, visualization and ray-tracing functions, which can be accessed directly from a user-friendly pop-up menu. They allow performing denoising and segmentation tasks directly in Amira, without the need of other programs, and ultimately allow the visualization of the results at photo-realistic quality with ray-tracing. They also allow a direct interaction with the data, such that, e.g., sub-tomograms can be directly extracted, or segmentation areas can be interactively selected. The implemented functions are fast, reliable and intuitive, yielding a comprehensive package for visualization in EM.     

MarVis: a tool for clustering and visualization of metabolic biomarkers

Background

Gene set analysis based on Gene Ontology (GO) can be a promising method for the analysis of differential expression patterns. However, current studies that focus on individual GO terms have limited analytical power, because the complex structure of GO introduces strong dependencies among the terms, and some genes that are annotated to a GO term cannot be found by statistically significant enrichment.

Results

We proposed a method for enriching clustered GO terms based on semantic similarity, namely cluster enrichment analysis based on GO (CeaGO), to extend the individual term analysis method. Using an Affymetrix HGU95aV2 chip dataset with simulated gene sets, we illustrated that CeaGO was sensitive enough to detect moderate expression changes. When compared to parent-based individual term analysis methods, the results showed that CeaGO may provide more accurate differentiation of gene expression results. When used with two acute leukemia (ALL and ALL/AML) microarray expression datasets, CeaGO correctly identified specifically enriched GO groups that were overlooked by other individual test methods.

Conclusion

By applying CeaGO to both simulated and real microarray data, we showed that this approach could enhance the interpretation of microarray experiments. CeaGO is currently available at http://chgc.sh.cn/en/software/CeaGO/.     

MarVis: a tool for clustering and visualization of metabolic biomarkers

Background  

A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.     

BicAT: a biclustering analysis toolbox

SUMMARY: Besides classical clustering methods such as hierarchical clustering, in recent years biclustering has become a popular approach to analyze biological data sets, e.g. gene expression data. The Biclustering Analysis Toolbox (BicAT) is a software platform for clustering-based data analysis that integrates various biclustering and clustering techniques in terms of a common graphical user interface. Furthermore, BicAT provides different facilities for data preparation, inspection and postprocessing such as discretization, filtering of biclusters according to specific criteria or gene pair analysis for constructing gene interconnection graphs. The possibility to use different biclustering algorithms inside a single graphical tool allows the user to compare clustering results and choose the algorithm that best fits a specific biological scenario. The toolbox is described in the context of gene expression analysis, but is also applicable to other types of data, e.g. data from proteomics or synthetic lethal experiments. AVAILABILITY: The BicAT toolbox is freely available at http://www.tik.ee.ethz.ch/sop/bicat and runs on all operating systems. The Java source code of the program and a developer's guide is provided on the website as well. Therefore, users may modify the program and add further algorithms or extensions.     

Rainbow: a toolbox for phylogenetic supertree construction and analysis

Rainbow is a program that provides a graphic user interface to construct supertrees using different methods. It also provides tools to analyze the quality of the supertrees produced. Rainbow is available for Mac OS X, Windows and Linux. AVAILABILITY: Rainbow is a free open-source software. Its binary files, source code, and manual can be downloaded from the Rainbow web page: http://genome.cs.iastate.edu/Rainbow/     

AFM 4.0: a toolbox for DNA microarray analysis

We have developed a series of programs, collectively packaged as Array File Maker 4.0 (AFM), that manipulate and manage DNA microarray data. AFM 4.0 is simple to use, applicable to any organism or microarray, and operates within the familiar confines of Microsoft Excel. Given a database of expression ratios, AFM 4.0 generates input files for clustering, helps prepare colored figures and Venn diagrams, and can uncover aneuploidy in yeast microarray data. AFM 4.0 should be especially useful to laboratories that do not have access to specialized commercial or in-house software.     

Cluster Viz:集成于Cytoscape的聚类可视化插件

生物网络中的聚类分析是功能模块识别及蛋白质功能预测的重要方法,聚类结果的可视化对于快速有效地分析生物网络结构也具有重要作用。通过分析生物网络显示和分析平台Cytoscape的架构,设计了一个使用方便的聚类分析和显示插件ClusterViz。这是一个可扩展的聚类算法的集成平台,可以不断增加其中的聚类算法,并对不同算法的结果进行比较分析,目前已实现了三种典型的算法实例。该插件能够成为蛋白质相互作用网络机理研究的一个有效工具。     

基于Cytoscape的蛋白质网络可视化聚类分析插件

蛋白质网络聚类是识别功能模块的重要手段,不仅有利于理解生物系统的组织结构,对预测蛋白质功能也具有重要的意义.聚类结果的可视化分析是实现蛋白质网络聚类的有效途径.本论文基于开源的Cytoscape平台,设计并实现了一个蛋白质网络聚类分析及可视化插件CytoCluster.该插件集成了MCODE,FAG-EC,HC-PIN,OH-PIN,IPCA,EAGLE等六种典型的聚类算法;实现了聚类结果的可视化,将分析所得的clusters以缩略图列表的形式直观地显示出来,对于单个cluster,可显示在原网络中的位置,并能生成相应的子图单独显示;可对聚类结果进行导出,记录了算法名称、参数、聚类结果等信息.该插件具有良好的扩展性,提供了统一的算法接口,可不断添加新的聚类算法.     

SeqHunter：序列搜索与分析的生物信息学软件包

利用Microsoft Visual Basic 6.0脚本,开发了一个在Windows平台下使用的生物信息学软件包SeqHunter;该软件包以简便的图形界面操作方式,可以实现本地化Blast,序列提取、比对与分析,序列数据库建立和管理等多种常用功能,为基因功能分析与大规模基因组数据挖掘提供了一个实用工具。     

CGH-Plotter: MATLAB toolbox for CGH-data analysis

CGH-Plotter is a MATLAB toolbox with a graphical user interface for the analysis of comparative genomic hybridization (CGH) microarray data. CGH-Plotter provides a tool for rapid visualization of CGH-data according to the locations of the genes along the genome. In addition, the CGH-Plotter identifies regions of amplifications and deletions, using k-means clustering and dynamic programming. The application offers a convenient way to analyze CGH-data and can also be applied for the analysis of cDNA microarray expression data. CGH-Plotter toolbox is platform independent and requires MATLAB 6.1 or higher to operate.     

MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data

SUMMARY: New additional methods are presented for processing and visualizing mass spectrometry based molecular profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large number of files, support for parallel processing, new methods for calculating peak areas using post-alignment peak picking algorithm and implementation of Sammon's mapping and curvilinear distance analysis for data visualization and exploratory analysis. AVAILABILITY: MZmine is available under GNU Public license from http://mzmine.sourceforge.net/.     

NeoAnalysis: a Python-based toolbox for quick electrophysiological data processing and analysis

Background

In a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simultaneously with the experimental settings. However, most open-source toolboxes currently available for data analysis were developed to handle only a portion of the data and did not take into account the sorting of experimental conditions. Additionally, these toolboxes require that the input data be in a specific format, which can be inconvenient to users. Therefore, the development of a highly integrated toolbox that can process multiple types of data regardless of input data format and perform basic analysis for general electrophysiological experiments is incredibly useful.

Results

Here, we report the development of a Python based open-source toolbox, referred to as NeoAnalysis, to be used for quick electrophysiological data processing and analysis. The toolbox can import data from different data acquisition systems regardless of their formats and automatically combine different types of data into a single file with a standardized format. In cases where additional spike sorting is needed, NeoAnalysis provides a module to perform efficient offline sorting with a user-friendly interface. Then, NeoAnalysis can perform regular analog signal processing, spike train, and local field potentials analysis, behavioral response (e.g. saccade) detection and extraction, with several options available for data plotting and statistics. Particularly, it can automatically generate sorted results without requiring users to manually sort data beforehand. In addition, NeoAnalysis can organize all of the relevant data into an informative table on a trial-by-trial basis for data visualization. Finally, NeoAnalysis supports analysis at the population level.

Conclusions

With the multitude of general-purpose functions provided by NeoAnalysis, users can easily obtain publication-quality figures without writing complex codes. NeoAnalysis is a powerful and valuable toolbox for users doing electrophysiological experiments.

     

Direct visualization and quantitative analysis of epidermal growth factor-induced receptor clustering

Several observations have indicated that clustering of growth factor receptors plays an important role in the action of growth factors. In this investigation, we have used the label fracture method to study the effects of epidermal growth factor (EGF) on the lateral distribution of its receptors in A431 epidermoid carcinoma cells. This method allows a direct visualization of immunogold-labeled plasma membrane receptors on ultrastructural level and in addition permits an quantitative analysis of their lateral distribution. EGF receptors were immunogold-labeled according to standard procedures with the monoclonal anti-EGF receptor antibody 2E9 (IgG1), which binds to the EGF receptor in a 1:1 ratio. In the absence of EGF, EGF receptors located on the surface of A431 cells were found to be clustered, as deduced from Poisson variance analysis (p less than 0.001). Following treatment of A431 cells with EGF, receptor clustering increased rapidly, reaching the maximum within 10 min. Maximal clustering was maintained for 1 h, after which the lateral distribution of receptors returned to the control situation within another hour.     

TOM software toolbox: acquisition and analysis for electron tomography

Automated data acquisition procedures have changed the perspectives of electron tomography (ET) in a profound manner. Elaborate data acquisition schemes with autotuning functions minimize exposure of the specimen to the electron beam and sophisticated image analysis routines retrieve a maximum of information from noisy data sets. "TOM software toolbox" integrates established algorithms and new concepts tailored to the special needs of low dose ET. It provides a user-friendly unified platform for all processing steps: acquisition, alignment, reconstruction, and analysis. Designed as a collection of computational procedures it is a complete software solution within a highly flexible framework. TOM represents a new way of working with the electron microscope and can serve as the basis for future high-throughput applications.     

GEDI: a user-friendly toolbox for analysis of large-scale gene expression data

Background  

Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills.     

MBEToolbox: a Matlab toolbox for sequence data analysis in molecular biology and evolution

Background  

MATLAB is a high-performance language for technical computing, integrating computation, visualization, and programming in an easy-to-use environment. It has been widely used in many areas, such as mathematics and computation, algorithm development, data acquisition, modeling, simulation, and scientific and engineering graphics. However, few functions are freely available in MATLAB to perform the sequence data analyses specifically required for molecular biology and evolution.     

Development of a sensitivity analysis framework for aquatic biogeochemical models using machine learning

Our evolving understanding of ecosystem functioning along with the advent of computational power have paved the way for the development of complex mathematical models that explicitly represent the functional diversity of biotic communities and multiple biogeochemical cycles. The ever-growing demand for more complex models underscores the importance of robust sensitivity analysis (SA) to elucidate the impact of the uncertainty on model inputs and to untangle the parameter covariance patterns that ultimately lead to the emergence of equifinality problems. In this study, we propose a novel multi-pronged SA framework that integrates advanced statistical and machine learning (ML) techniques. Principal component analysis (PCA) is first applied to dissect the wide array of predictive outputs and identify modes of variability in time and/or space. Classification and Regression Tree (CART) analysis is then used to identify a set of splitting decisions connecting threshold values of key state variables and model parameters with different ranges of predictive outputs with management interest. Self-Organizing Maps (SOM) are implemented as a final step to unravel any non-linear associations between model parameters and responses. As a proof-of-concept, we used a complex aquatic biogeochemical model developed for the Bay of Quinte, a eutrophic embayment in Lake Ontario, to examine competition patterns and structural shifts among multiple functional phytoplankton (diatoms, N-fixing cyanobacteria, and Microcystis) and zooplankton (herbivores and omnivores) groups. Our sensitivity analysis framework showed that the parameters representing the dependence of growth and metabolic processes on temperature are particularly influential to recreate plankton community dynamics during the cold period of the year, whereas the interplay among the interspecific resource competition, strength of the prey-predator interactions, and phosphorus availability mainly regulate their dynamics during the growing season. The growth strategies of diatoms, their nutritional quality that determines the assimilation efficiency by zooplankton, along with the ambient nutrient availability determine our capacity to reproduce patterns of cyanobacteria dominance and faithfully depict the severity of harmful algal blooms. Finally, our study discusses the benefits of a broader use of the ML-based SA framework to unravel influential parametric interactions in modulating the behaviors of complex mathematical models.