cMap: the comparative genetic map viewer

cMap, a www comparative genetic map graphical utility, has a search capability and provides comparison of two genetic maps within or between species with dynamic links to data resources and text lists of the shared loci, running in a relational database environment. Currently, maps from three species (maize 'Zea mays L.', rice 'Oryza sativa L.', and sorghum 'Sorghum bicolor L.'), representing over 13,800 distinct loci, are available for comparison at http://www.agron.missouri.edu/cMapDB/cMap.html. AVAILABILITY: cMap source code is available without cost on request for non-commercial use.     

A genomic data viewer for iPad

The Integrative Genomics Viewer (IGV) for iPad, based on the popular IGV application for desktop and laptop computers, supports researchers who wish to take advantage of the mobility of today’s tablet computers to view genomic data and present findings to colleagues.     

GenoMap,a circular genome data viewer

A Tcl/Tk-based application called GenoMap is described, a viewer for genome-wide map of microarray expression data within a circular bacterial genome. An interactive interface facilitates easy identification of the expressed region. This software is also used for drawing genome-wide quantitative data.     

primetv: a viewer for reconciled trees

Background  

Evolutionary processes, such as gene family evolution or parasite-host co-speciation, can often be viewed as a tree evolving inside another tree. Relating two given trees under such a constraint is known as reconciling them. Adequate software tools for generating illustrations of tree reconciliations are instrumental for presenting and communicating results and ideas regarding these phenomena. Available visualization tools have been limited to illustrations of the most parsimonious reconciliation. However, there exists a plethora of biologically relevant non-parsimonious reconciliations. Illustrations of these general reconciliations may not be achieved without manual editing.     

An eScience-Bayes strategy for analyzing omics data

Background  

The omics fields promise to revolutionize our understanding of biology and biomedicine. However, their potential is compromised by the challenge to analyze the huge datasets produced. Analysis of omics data is plagued by the curse of dimensionality, resulting in imprecise estimates of model parameters and performance. Moreover, the integration of omics data with other data sources is difficult to shoehorn into classical statistical models. This has resulted in ad hoc approaches to address specific problems.     

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     

GT-Miner: a graph-theoretic data miner, viewer, and model processor

Inexpensive computational power combined with high-throughput experimental platforms has created a wealth of biological information requiring analytical tools and techniques for interpretation. Graph-theoretic concepts and tools have provided an important foundation for information visualization, integration, and analysis of datasets, but they have often been relegated to background analysis tasks. GT-Miner is designed for visual data analysis and mining operations, interacts with other software, including databases, and works with diverse data types. It facilitates a discovery-oriented approach to data mining wherein exploration of alterations of the data and variations of the visualization is encouraged. The user is presented with a basic iterative process, consisting of loading, visualizing, transforming, and then storing the resultant information. Complex analyses are built-up through repeated iterations and user interactions. The iterative process is optimized by automatic layout following transformations and by maintaining a current selection set of interest for elements modified by the transformations. Multiple visualizations are supported including hierarchical, spring, and force-directed self-organizing layouts. Graphs can be transformed with an extensible set of algorithms or manually with an integral visual editor. GT-Miner is intended to allow easier access to visual data mining for the non-expert.     

Techniques for integrating ‐omics data

The challenge for -omics research is to tackle the problem of fragmentation of knowledge by integrating several sources of heterogeneous information into a coherent entity. It is widely recognized that successful data integration is one of the keys to improve productivity for stored data. Through proper data integration tools and algorithms, researchers may correlate relationships that enable them to make better and faster decisions. The need for data integration is essential for present ‐omics community, because ‐omics data is currently spread world wide in wide variety of formats. These formats can be integrated and migrated across platforms through different techniques and one of the important techniques often used is XML. XML is used to provide a document markup language that is easier to learn, retrieve, store and transmit. It is semantically richer than HTML. Here, we describe bio warehousing, database federation, controlled vocabularies and highlighting the XML application to store, migrate and validate -omics data.     

supraHex: An R/Bioconductor package for tabular omics data analysis using a supra-hexagonal map

Biologists are increasingly confronted with the challenge of quickly understanding genome-wide biological data, which usually involve a large number of genomic coordinates (e.g. genes) but a much smaller number of samples. To meet the need for data of this shape, we present an open-source package called ‘supraHex’ for training, analysing and visualising omics data. This package devises a supra-hexagonal map to self-organise the input data, offers scalable functionalities for post-analysing the map, and more importantly, allows for overlaying additional data for multilayer omics data comparisons. Via applying to DNA replication timing data of mouse embryogenesis, we demonstrate that supraHex is capable of simultaneously carrying out gene clustering and sample correlation, providing intuitive visualisation at each step of the analysis. By overlaying CpG and expression data onto the trained replication-timing map, we also show that supraHex is able to intuitively capture an inherent relationship between late replication, low CpG density promoters and low expression levels. As part of the Bioconductor project, supraHex makes accessible to a wide community in a simple way, what would otherwise be a complex framework for the ultrafast understanding of any tabular omics data, both scientifically and artistically. This package can run on Windows, Mac and Linux, and is freely available together with many tutorials on featuring real examples at http://supfam.org/supraHex.     

Lightweight genome viewer: portable software for browsing genomics data in its chromosomal context

Background  

Lightweight genome viewer (lwgv) is a web-based tool for visualization of sequence annotations in their chromosomal context. It performs most of the functions of larger genome browsers, while relying on standard flat-file formats and bypassing the database needs of most visualization tools. Visualization as an aide to discovery requires display of novel data in conjunction with static annotations in their chromosomal context. With database-based systems, displaying dynamic results requires temporary tables that need to be tracked for removal.     

Mapplet: a CORBA-based genome map viewer

MOTIVATION : There are a large number of genetic and physical maps, distributed at many sites. Each site offers different kinds of access methods and viewers. CORBA, the de facto standard for distributed object-oriented computing, offers new opportunities to unify the view on these maps through standard interfaces. A collaboration of Infobiogen and the EBI proposes a common IDL for maps. RESULTS: A CORBA map viewer is presented which serves as a proof of concept for the proposed IDL. It demonstrates its usefulness in the context of map viewing and its ability to handle large maps with <1000 markers. The viewer gives access to the maps of the Radiation Hybrid Database at EBI. It gives a quick overview of several large maps side by side. The marker density at each map position is displayed and different marker types can be highlighted. AVAILABILITY: Demonstration and source code at: http://sunny.ebi.ac.uk/ approximately jungfer/Mapplet CONTACT: jungfer@ebi.ac.uk      

Molecular signatures from omics data: From chaos to consensus

In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.     

Bambino: a variant detector and alignment viewer for next-generation sequencing data in the SAM/BAM format

SUMMARY: Bambino is a variant detector and graphical alignment viewer for next-generation sequencing data in the SAM/BAM format, which is capable of pooling data from multiple source files. The variant detector takes advantage of SAM-specific annotations, and produces detailed output suitable for genotyping and identification of somatic mutations. The assembly viewer can display reads in the context of either a user-provided or automatically generated reference sequence, retrieve genome annotation features from a UCSC genome annotation database, display histograms of non-reference allele frequencies, and predict protein-coding changes caused by SNPs. AVAILABILITY: Bambino is written in platform-independent Java and available from https://cgwb.nci.nih.gov/goldenPath/bamview/documentation/index.html, along with documentation and example data. Bambino may be launched online via Java Web Start or downloaded and run locally.     

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.