SynView: a GBrowse-compatible approach to visualizing comparative genome data

SUMMARY: We present SynView, a simple and generic approach to dynamically visualize multi-species comparative genome data. It is a light-weight application based on the popular and configurable web-based GBrowse framework. It can be used with a variety of databases and provides the user with a high degree of interactivity. The tool is written in Perl and runs on top of the GBrowse framework. It is in use in the PlasmoDB (http://www.PlasmoDB.org) and the CryptoDB (http://www.CryptoDB.org) projects and can be easily integrated into other cross-species comparative genome projects. AVAILABILITY: The program and instructions are freely available at http://www.ApiDB.org/apps/SynView/ CONTACT: jkissing@uga.edu.     

CLICK and EXPANDER: a system for clustering and visualizing gene expression data

MOTIVATION: Microarrays have become a central tool in biological research. Their applications range from functional annotation to tissue classification and genetic network inference. A key step in the analysis of gene expression data is the identification of groups of genes that manifest similar expression patterns. This translates to the algorithmic problem of clustering genes based on their expression patterns. RESULTS: We present a novel clustering algorithm, called CLICK, and its applications to gene expression analysis. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups (kernels) of highly similar elements, which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clusters. We report on the application of CLICK to a variety of gene expression data sets. In all those applications it outperformed extant algorithms according to several common figures of merit. We also point out that CLICK can be successfully used for the identification of common regulatory motifs in the upstream regions of co-regulated genes. Furthermore, we demonstrate how CLICK can be used to accurately classify tissue samples into disease types, based on their expression profiles. Finally, we present a new java-based graphical tool, called EXPANDER, for gene expression analysis and visualization, which incorporates CLICK and several other popular clustering algorithms. AVAILABILITY: http://www.cs.tau.ac.il/~rshamir/expander/expander.html     

SplitsTree: analyzing and visualizing evolutionary data

MOTIVATION: Real evolutionary data often contain a number of different and sometimes conflicting phylogenetic signals, and thus do not always clearly support a unique tree. To address this problem, Bandelt and Dress (Adv. Math., 92, 47-05, 1992) developed the method of split decomposition. For ideal data, this method gives rise to a tree, whereas less ideal data are represented by a tree-like network that may indicate evidence for different and conflicting phylogenies. RESULTS: SplitsTree is an interactive program, for analyzing and visualizing evolutionary data, that implements this approach. It also supports a number of distances transformations, the computation of parsimony splits, spectral analysis and bootstrapping.      

OmicsViz: Cytoscape plug-in for visualizing omics data across species

MOTIVATION: OmicsViz is a Cytoscape plug-in for mapping and visualizing large-scale omics datasets across species, including those with many-to-many mappings between homologs. This allows users to map their data onto pathways of related model organisms. Mapping schemas across species or different experimental protocols allow users to comparatively analyze the omics data. The data can also be visualized in parallel-coordinate plots. AVAILABILITY: The latest version of OmicsViz with documentation, tutorial and jar files can be downloaded from http://metnet.vrac.iastate.edu/MetNet_fcmodeler.htm     

NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data

A key benefit of long-read nanopore sequencing technology is the ability to detect modified DNA bases, such as 5-methylcytosine. The lack of R/Bioconductor tools for the effective visualization of nanopore methylation profiles between samples from different experimental groups led us to develop the NanoMethViz R package. Our software can handle methylation output generated from a range of different methylation callers and manages large datasets using a compressed data format. To fully explore the methylation patterns in a dataset, NanoMethViz allows plotting of data at various resolutions. At the sample-level, we use dimensionality reduction to look at the relationships between methylation profiles in an unsupervised way. We visualize methylation profiles of classes of features such as genes or CpG islands by scaling them to relative positions and aggregating their profiles. At the finest resolution, we visualize methylation patterns across individual reads along the genome using the spaghetti plot and heatmaps, allowing users to explore particular genes or genomic regions of interest. In summary, our software makes the handling of methylation signal more convenient, expands upon the visualization options for nanopore data and works seamlessly with existing methylation analysis tools available in the Bioconductor project. Our software is available at https://bioconductor.org/packages/NanoMethViz.     

MZmine 2: Modular framework for processing,visualizing, and analyzing mass spectrometry-based molecular profile data

Background  

Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2.     

Targeted projection pursuit for visualizing gene expression data classifications

We present a novel method for finding low-dimensional views of high-dimensional data: Targeted Projection Pursuit. The method proceeds by finding projections of the data that best approximate a target view. Two versions of the method are introduced; one version based on Procrustes analysis and one based on an artificial neural network. These versions are capable of finding orthogonal or non-orthogonal projections, respectively. The method is quantitatively and qualitatively compared with other dimension reduction techniques. It is shown to find 2D views that display the classification of cancers from gene expression data with a visual separation equal to, or better than, existing dimension reduction techniques. AVAILABILITY: source code, additional diagrams, and original data are available from http://computing.unn.ac.uk/staff/CGJF1/tpp/bioinf.html     

VarSifter: visualizing and analyzing exome-scale sequence variation data on a desktop computer

VarSifter is a graphical software tool for desktop computers that allows investigators of varying computational skills to easily and quickly sort, filter, and sift through sequence variation data. A variety of filters and a custom query framework allow filtering based on any combination of sample and annotation information. By simplifying visualization and analyses of exome-scale sequence variation data, this program will help bring the power and promise of massively-parallel DNA sequencing to a broader group of researchers. Availability and Implementation: VarSifter is written in Java, and is freely available in source and binary versions, along with a User Guide, at http://research.nhgri.nih.gov/software/VarSifter/.     

CRCView: a web server for analyzing and visualizing microarray gene expression data using model-based clustering

CRCView is a user-friendly point-and-click web server for analyzing and visualizing microarray gene expression data using a Dirichlet process mixture model-based clustering algorithm. CRCView is designed to clustering genes based on their expression profiles. It allows flexible input data format, rich graphical illustration as well as integrated GO term based annotation/interpretation of clustering results. Availability: http://helab.bioinformatics.med.umich.edu/crcview/.     

gff2ps: visualizing genomic annotations

gff2psis a program for visualizing annotations of genomic sequences. The program takes the annotated features on a genomic sequence in GFF format as input, and produces a visual output in PostScript. While it can be used in a very simple way, it also allows for a great degree of customization through a number of options and/or customization files.     

BactMAP: An R package for integrating,analyzing and visualizing bacterial microscopy data

High-throughput analyses of single-cell microscopy data are a critical tool within the field of bacterial cell biology. Several programs have been developed to specifically segment bacterial cells from phase-contrast images. Together with spot and object detection algorithms, these programs offer powerful approaches to quantify observations from microscopy data, ranging from cell-to-cell genealogy to localization and movement of proteins. Most segmentation programs contain specific post-processing and plotting options, but these options vary between programs and possibilities to optimize or alter the outputs are often limited. Therefore, we developed BactMAP (Bacterial toolbox for Microscopy Analysis & Plotting), a command-line based R package that allows researchers to transform cell segmentation and spot detection data generated by different programs into various plots. Furthermore, BactMAP makes it possible to perform custom analyses and change the layout of the output. Because BactMAP works independently of segmentation and detection programs, inputs from different sources can be compared within the same analysis pipeline. BactMAP complies with standard practice in R which enables the use of advanced statistical analysis tools, and its graphic output is compatible with ggplot2, enabling adjustable plot graphics in every operating system. User feedback will be used to create a fully automated Graphical User Interface version of BactMAP in the future. Using BactMAP, we visualize key cell cycle parameters in Bacillus subtilis and Staphylococcus aureus, and demonstrate that the DNA replication forks in Streptococcus pneumoniae dissociate and associate before splitting of the cell, after the Z-ring is formed at the new quarter positions. BactMAP is available from https://veeninglab.com/bactmap .     

MEXPRESS: visualizing expression,DNA methylation and clinical TCGA data

Background

In recent years, increasing amounts of genomic and clinical cancer data have become publically available through large-scale collaborative projects such as The Cancer Genome Atlas (TCGA). However, as long as these datasets are difficult to access and interpret, they are essentially useless for a major part of the research community and their scientific potential will not be fully realized. To address these issues we developed MEXPRESS, a straightforward and easy-to-use web tool for the integration and visualization of the expression, DNA methylation and clinical TCGA data on a single-gene level (http://mexpress.be).

Results

In comparison to existing tools, MEXPRESS allows researchers to quickly visualize and interpret the different TCGA datasets and their relationships for a single gene, as demonstrated for GSTP1 in prostate adenocarcinoma. We also used MEXPRESS to reveal the differences in the DNA methylation status of the PAM50 marker gene MLPH between the breast cancer subtypes and how these differences were linked to the expression of MPLH.

Conclusions

We have created a user-friendly tool for the visualization and interpretation of TCGA data, offering clinical researchers a simple way to evaluate the TCGA data for their genes or candidate biomarkers of interest.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1847-z) contains supplementary material, which is available to authorized users.     

Matrix metalloproteinases: they're not just for matrix anymore!

The matrix metalloproteinases (MMPs) have been viewed as bulldozers, destroying the extracellular matrix to permit normal remodeling and contribute to pathological tissue destruction and tumor cell invasion. More recently, the identification of specific matrix and non-matrix substrates for MMPs and the elucidation of the biological consequence of cleavage indicates that perhaps MMPs should be viewed more as pruning shears, playing sophisticated roles in modulating normal cellular behavior, cell-cell communication and tumor progression.     

MQ2: visualizing multi-trait mapped QTL results

Bacterial blight (BB) of rice (Oryza sativa L.) caused by Xanthomonas oryzae pv. oryzae (Xoo) is a destructive disease in rice worldwide. Xa3, a gene conferring resistance to BB at the booting stage of the rice plant, has been characterized previously using map-based cloning. We cloned and sequenced the Xa3/xa3 gene in the Korean cultivars Hwayeong, Ilmi, and Goun and conferred resistance or susceptibility to BB. We detected polymorphisms, and polymerase chain reaction-based functional markers were developed based on the single nucleotide polymorphism from the Xa3 and xa3 nucleotide sequence. Susceptible or resistant individuals from an F2 population developed from a cross between Milyang 244 and Ilmi, near-isogenic lines carrying BB resistance genes, were screened with functional markers. The BB3-RF and BB3-RR primers consistently amplified a resistance-specific fragment of 255 bp only in resistant plants, whereas the BB3-SF and BB3-SR primers were specific to susceptible plants. Genotyping results were co-segregated with phenotype by conducting the BB resistance test with the K₃ race. These markers could be effective for marker-assisted selection of the Xa3 gene in rice breeding programs.     

PubNet: a flexible system for visualizing literature derived networks

We have developed PubNet, a web-based tool that extracts several types of relationships returned by PubMed queries and maps them into networks, allowing for graphical visualization, textual navigation, and topological analysis. PubNet supports the creation of complex networks derived from the contents of individual citations, such as genes, proteins, Protein Data Bank (PDB) IDs, Medical Subject Headings (MeSH) terms, and authors. This feature allows one to, for example, examine a literature derived network of genes based on functional similarity.