BiVisu: software tool for bicluster detection and visualization

BiVisu is an open-source software tool for detecting and visualizing biclusters embedded in a gene expression matrix. Through the use of appropriate coherence relations, BiVisu can detect constant, constant-row, constant-column, additive-related as well as multiplicative-related biclusters. The biclustering results are then visualized under a 2D setting for easy inspection. In particular, parallel coordinate (PC) plots for each bicluster are displayed, from which objective and subjective cluster quality evaluation can be performed. Availability: BiVisu has been developed in Matlab and is available at http://www.eie.polyu.edu.hk/~nflaw/Biclustering/.     

GenomeComp: a visualization tool for microbial genome comparison

We have developed a software tool, GenomeComp, for summarizing, parsing and visualizing the genome sequences comparison results derived from voluminous BLAST textual output. With GenomeComp, the variation between genomes can be easily highlighted, such as repeat regions, insertions, deletions and rearrangements of genomic segments. This software provides a new visualizing tool for microbe comparative genomics.     

BLogo: a tool for visualization of bias in biological sequences

Blogo is a web-based tool that detects and displays statistically significant position-specific sequence bias with reduced background noise. The over-represented and under-represented symbols in a particular position are shown above and below the zero line. When the sequences are in open reading frames, the background frequency of nucleotides could be calculated separately for the three positions of a codon, thus greatly reducing the background noise. The chi(2)-test or Fisher's exact test is used to evaluate the statistical significance of every symbol in every position and only those that are significant are highlighted in the resulting logo. The perl source code of the program is freely available and can be run locally. AVAILABILITY: http://acephpx.cropdb.org/blogo/, http://www.bioinformatics.org/blogo/.     

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.     

SNPTools: a software tool for visualization and analysis of microarray data

SUMMARY: We have created a software tool, SNPTools, for analysis and visualization of microarray data, mainly SNP array data. The software can analyse and find differences in intensity levels between groups of arrays and identify segments of SNPs (genes, clones), where the intensity levels differ significantly between the groups. In addition, SNPTools can show jointly loss-of-heterozygosity (LOH) data (derived from genotypes) and intensity data for paired samples of tumour and normal arrays. The output graphs can be manipulated in various ways to modify and adjust the layout. A wizard allows options and parameters to be changed easily and graphs replotted. All output can be saved in various formats, and also re-opened in SNPTools for further analysis. For explorative use, SNPTools allows various genome information to be loaded onto the graphs. AVAILABILITY: The software, example data sets and tutorials are freely available from http://www.birc.au.dk/snptools     

TaxonGap: a visualization tool for intra- and inter-species variation among individual biomarkers

Summary: Selection of optimal biomarkers for the identificationof different operational taxonomic units (OTUs) may be a hardand tedious task, especially when phylogenetic trees for multiplegenes need to be compared. With TaxonGap we present a noveland easy-to-handle software tool that allows visual comparisonof the discriminative power of multiple biomarkers for a setof OTUs. The compact graphical output allows for easy comparisonand selection of individual biomarkers. Availability: Graphical User Interface; Executable JAVA archivefile, source code, supplementary information and sample filescan be downloaded from the website: http://www.kermit.ugent.be/taxongap Contact: Bram.Slabbinck{at}UGent.be Associate Editor: John Quackenbush     

POLYVIEW: a flexible visualization tool for structural and functional annotations of proteins

The POLYVIEW visualization server can be used to generate protein sequence annotations, including secondary structures, relative solvent accessibilities, functional motifs and polymorphic sites. Two-dimensional graphical representations in a customizable format may be generated for both known protein structures and predictions obtained using protein structure prediction servers. POLYVIEW may be used for automated generation of pictures with structural and functional annotations for publications and proteomic on-line resources. AVAILABILITY: http://polyview.cchmc.org.     

Quantitative flow visualization: toward a comprehensive flow diagnostic tool

Quantitative flow visualization has many roots and has takenseveral approaches. The advent of digital image processing hasmade it possible to practically extract useful information fromevery kind of flow image. In a direct approach, the image intensityor color (wavelength or frequency) can be used as an indicationof concentration, density and temperature fields or gradientsof these scalar fields in the flow (Merzkirch, 1987). For whole-fieldvelocity measurement, the method of choice by experimental fluidmechanicians has been the technique of Particle Image Velocimetry(DPIV). This paper presents a novel approach to extend the DPIVtechnique from a planar method to a full three-dimensional volumemapping technique useful in both engineering and biologicalapplications.     

BisoGenet: a new tool for gene network building,visualization and analysis

Background  

The increasing availability and diversity of omics data in the post-genomic era offers new perspectives in most areas of biomedical research. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing omics-data. The development of software tools capable of integrating data from different sources and to provide flexible methods to reconstruct, represent and analyze topological networks is an active field of research in bioinformatics.     

SNP-VISTA: An interactive SNP visualization tool

Background  

Recent advances in sequencing technologies promise to provide a better understanding of the genetics of human disease as well as the evolution of microbial populations. Single Nucleotide Polymorphisms (SNPs) are established genetic markers that aid in the identification of loci affecting quantitative traits and/or disease in a wide variety of eukaryotic species. With today's technological capabilities, it has become possible to re-sequence a large set of appropriate candidate genes in individuals with a given disease in an attempt to identify causative mutations. In addition, SNPs have been used extensively in efforts to study the evolution of microbial populations, and the recent application of random shotgun sequencing to environmental samples enables more extensive SNP analysis of co-occurring and co-evolving microbial populations. The program is available at [1].     

methGraph: A genome visualization tool for PCR-based methylation assays

Abnormalities in DNA methylation of CpG islands that play a role in gene regulation affect gene expression and hence play a role in disease, including cancer. Bisulfite-based DNA methylation analysis methods such as methylation-specific PCR (MSP) and bisulfite sequencing (BiSeq) are most commonly used to study gene-specific DNA methylation. Assessing specificity and visualizing the position of PCR primers in their genomic context is a laborious and tedious task, primarily due to the sequence changes induced during the bisulfite conversion. For this purpose, we developed methGraph, a web application for easy, fast and flexible visualization and accurate in silico quality evaluation of PCR-based methylation assays. The visualization process starts by submitting PCR primer sequences for specificity assessment and mapping on the genome using the BiSearch ePCR primer-search algorithm. The next step comprises the selection of relevant UCSC genome annotation tracks for display in the final graph. A custom track showing all individual CpG dinucleotides, representing their distribution in the CpG island is also provided. Finally, methGraph creates a BED file that is automatically uploaded to the UCSC genome browser, after which the resulting image files are extracted and made available for visualization and download. The generated high-quality figures can easily be customized and exported for use in publications or presentations. methGraph is available at http://mellfire.ugent.be/methgraph/.     

Kiwi: a tool for integration and visualization of network topology and gene-set analysis

Background

The analysis of high-throughput data in biology is aided by integrative approaches such as gene-set analysis. Gene-sets can represent well-defined biological entities (e.g. metabolites) that interact in networks (e.g. metabolic networks), to exert their function within the cell. Data interpretation can benefit from incorporating the underlying network, but there are currently no optimal methods that link gene-set analysis and network structures.

Results

Here we present Kiwi, a new tool that processes output data from gene-set analysis and integrates them with a network structure such that the inherent connectivity between gene-sets, i.e. not simply the gene overlap, becomes apparent. In two case studies, we demonstrate that standard gene-set analysis points at metabolites regulated in the interrogated condition. Nevertheless, only the integration of the interactions between these metabolites provides an extra layer of information that highlights how they are tightly connected in the metabolic network.

Conclusions

Kiwi is a tool that enhances interpretability of high-throughput data. It allows the users not only to discover a list of significant entities or processes as in gene-set analysis, but also to visualize whether these entities or processes are isolated or connected by means of their biological interaction. Kiwi is available as a Python package at http://www.sysbio.se/kiwi and an online tool in the BioMet Toolbox at http://www.biomet-toolbox.org.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0408-9) contains supplementary material, which is available to authorized users.