Nameeta Shah

Phylo-VISTA: interactive visualization of multiple DNA sequence alignments

MOTIVATION The power of multi-sequence comparison for biological discovery is well established. The need for new capabilities to visualize and compare cross-species alignment data is intensified by the growing number of genomic sequence datasets being generated for an ever-increasing number of organisms. To be efficient these visualization algorithms must support the ability to accommodate consistently a wide range of evolutionary distances in a comparison framework based upon phylogenetic relationships. RESULTS We have developed Phylo-VISTA, an interactive tool for analyzing multiple alignments by visualizing a similarity measure for multiple DNA sequences. The complexity of visual presentation is effectively organized using a framework based upon interspecies phylogenetic relationships. The phylogenetic organization supports rapid, user-guided interspecies comparison. To aid in navigation through large sequence datasets, Phylo-VISTA leverages concepts from VISTA that provide a user with the ability to select and view data at varying resolutions. The combination of multiresolution data visualization and analysis, combined with the phylogenetic framework for interspecies comparison, produces a highly flexible and powerful tool for visual data analysis of multiple sequence alignments. AVAILABILITY Phylo-VISTA is available at http://www-gsd.lbl.gov/phylovista. It requires an Internet browser with Java Plug-in 1.4.2 and it is integrated into the global alignment program LAGAN at http://lagan.stanford.edu

SNP-VISTA: An interactive SNP visualization tool

BackgroundRecent 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 todays 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 http://genome.lbl.gov/vista/snpvista[1].ResultsWe have developed and present two modifications of an interactive visualization tool, SNP-VISTA, to aid in the analyses of the following types of data: A. Large-scale re-sequence data of disease-related genes for discovery of associated and/or causative alleles (GeneSNP-VISTA). B. Massive amounts of ecogenomics data for studying homologous recombination in microbial populations (EcoSNP-VISTA). The main features and capabilities of SNP-VISTA are: 1) mapping of SNPs to gene structure; 2) classification of SNPs, based on their location in the gene, frequency of occurrence in samples and allele composition; 3) clustering, based on user-defined subsets of SNPs, highlighting haplotypes as well as recombinant sequences; 4) integration of protein evolutionary conservation visualization; and 5) display of automatically calculated recombination points that are user-editable.ConclusionThe main strength of SNP-VISTA is its graphical interface and use of visual representations, which support interactive exploration and hence better understanding of large-scale SNP data by the user.

Phylo-VISTA: An Interactive Visualization Tool for Multiple DNA Sequence Alignments

We have developed Phylo-VISTA (Shah et al., 2003), an interactive software tool for analyzing multiple alignments by visualizing a similarity measure for DNA sequences of multiple species. The complexity of visual presentation is effectively organized using a framework based upon inter-species phylogenetic relationships. The phylogenetic organization supports rapid, user-guided inter-species comparison. To aid in navigation through large sequence datasets, Phylo-VISTA provides a user with the ability to select and view data at varying resolutions. The combination of multi-resolution data visualization and analysis, combined with the phylogenetic framework for inter-species comparison, produces a highly flexible and powerful tool for visual data analysis of multiple sequence alignments.

A simple model of the modular structure of transcriptional regulation in yeast.

Resolving the general organizational principles that govern the interactions during transcriptional gene regulation has great relevance for understanding disease progression, biofabrication, and biological systems in general. The available genome-level monitoring technologies and the best understood biological work on gene regulation are together providing us with unprecedented amounts of data and universal modeling frameworks in which to reason about regulatory systems on a computational level. Gene regulatory systems exhibit modularity in their regulatory sequences as well as in the corresponding gene expression. This modularity has a nontrivial, general combinatorial structure that can be studied and generalized to model classes of regulatory systems. Here, we study computationally the combinatorial nature of transcriptional regulation by assuming a one-to-one relationship between shared patterns in genome-wide gene-expression and cis-region modules. In our combinatorial framework, the DNA binding events are complementary to their expression counterparts, and together let us approximate the underlying regulation structure. Our model maps regulatory systems onto hierarchical structures which can be approximated by conflating existing large scale gene expression and ChIP-chip data. We have developed methods for building regulatory hierarchies and identifying the basic functional units, or modules, of transcriptional regulation. We validate our model using yeast data by showing agreement of our predictions with experimental data, and using the hierarchies to resolve a finer structure of co-regulation.

PointCloudXplore: a visualization tool for 3D gene expressiondata

PointCloudXplore: A Visualization Tool for 3D Gene Expression Data Oliver R¨ bel ∗,1,2 , Gunther H. Weber 2,3 , Soile V.E. Ker¨ nen 3 , Charless C. Fowlkes 4 , u a Cris L. Luengo Hendriks 3 , Lisa Simirenko 3 , Nameeta Y. Shah 2 , Michael B. Eisen 3 , Mark D. Biggin 3 , Hans Hagen 1 , Damir Sudar 3 , Jitendra Malik 4 , David W. Knowles 3 , and Bernd Hamann 1,2 International Research Training Group “Visualization of Large and Unstructured Data Sets,” University of Kaiserslautern, Germany Institute for Data Analysis and Visualization, University of California, Davis, CA, USA Life Sciences and Genomics Divisions, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Computer Science Division, University of California, Berkeley, CA, USA Abstract: The Berkeley Drosophila Transcription Network Project (BDTNP) has de- veloped a suite of methods that support quantitative, computational analysis of three- dimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos, aiming at a more in-depth understanding of gene regulatory networks. We describe a new tool, called PointCloudXplore (PCX), that supports effective 3D gene expression data exploration. PCX is a visualization tool that uses the established visualization techniques of multiple views, brushing, and linking to support the analysis of high-dimensional datasets that describe many genes’ expression. Each of the views in PointCloudXplore shows a different gene expression data property. Brushing is used to select and em- phasize data associated with defined subsets of embryo cells within a view. Linking is used to show in additional views the expression data for a group of cells that have first been highlighted as a brush in a single view, allowing further data subset properties to be determined. In PCX, physical views of the data are linked to abstract data displays such as parallel coordinates. Physical views show the spatial relationships between different genes’ expression patterns within an embryo. Abstract gene expression data displays on the other hand allow for an analysis of relationships between different genes directly in the gene expression space. We discuss on parallel coordinates as one example abstract data view currently available in PCX. We have developed several ex- tensions to standard parallel coordinates to facilitate brushing and the visualization of 3D gene expression data. ∗ oliverruebel@web.de

Inferring Cis-region Hierarchies from Patterns in Time-Course Gene Expression Data

Resolving the co-regulation relationships between genes is a major step toward understanding the underlying topology and dynamics of gene networks. Although co-expression of genes does not directly imply their co-regulation, model-based approaches coupled with the availability of large-scale gene expression data can help associate expression patterns with features in their cis-regions. Inspired by studies of transcriptional regulation in sea-urchin, here we report on preliminary validation of the following simple model for transcriptional regulation in yeast: the same Cis-Regulatory Modules (CRMs) in the cis-regions of different genes give rise to very similar functional events in the time-course expression profiles of those genes. We use a modified version of a prior algorithm for decomposing time-course gene expression patterns into functional events. To capture and reason about shared CRMs we introduce an order relationship, or a Regulation Hierarchy on the genes. When tested on actual time-course gene expression data of yeast preliminary results indicate 50% – 71% matches, of high confidence, between our derived and known cis-region regulation hierarchies. This hierarchy structure yields practical predictions when used with other type of genomic data, e.g. location of TF-DNA interactions.