An interactive tool for visualization of relationships between gene expression profiles

BACKGROUND: Application of phenetic methods to gene expression analysis proved to be a successful approach. Visualizing the results in a 3-dimentional space may further enhance these techniques. RESULTS: We designed and built TreeBuilder3D, an interactive viewer for visualizing the hierarchical relationships between expression profiles such as SAGE libraries or microarrays. The program allows loading expression data as plain text files and visualizing the relative differences of the analyzed datasets in 3-dimensional space using various distance metrics. CONCLUSION: TreeBuilder3D provides a simple interface and has a small size. Written in Java, TreeBuilder3D is a platform-independent, open source application, which may be useful in analysis of large-scale gene expression data

Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies

The dramatic increase in heterogeneous types of biological data—in particular, the abundance of new protein sequences—requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity—GPCRs and kinases from humans, and the crotonase superfamily of enzymes—we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships

chroGPS, a global chromatin positioning system for the functional analysis and visualization of the epigenome

Development of tools to jointly visualize the genome and the epigenome remains a challenge. chroGPS is a computational approach that addresses this question. chroGPS uses multidimensional scaling techniques to represent similarity between epigenetic factors, or between genetic elements on the basis of their epigenetic state, in 2D/3D reference maps. We emphasize biological interpretability, statistical robustness, integration of genetic and epigenetic data from heterogeneous sources, and computational feasibility. Although chroGPS is a general methodology to create reference maps and study the epigenetic state of any class of genetic element or genomic region, we focus on two specific kinds of maps: chroGPSfactors, which visualizes functional similarities between epigenetic factors, and chroGPSgenes, which describes the epigenetic state of genes and integrates gene expression and other functional data. We use data from the modENCODE project on the genomic distribution of a large collection of epigenetic factors in Drosophila, a model system extensively used to study genome organization and function. Our results show that the maps allow straightforward visualization of relationships between factors and elements, capturing relevant information about their functional properties that helps to interpret epigenetic information in a functional context and derive testable hypotheses

INTERACTIVE CLINICAL EVENT PATTERN MINING AND VISUALIZATION USING INSURANCE CLAIMS DATA

With exponential growth on a daily basis, there is potentially valuable information hidden in complex electronic medical records (EMR) systems. In this thesis, several efficient data mining algorithms were explored to discover hidden knowledge in insurance claims data. The first aim was to cluster three levels of information overload(IO) groups among chronic rheumatic disease (CRD) patient groups based on their clinical events extracted from insurance claims data. The second aim was to discover hidden patterns using three renowned pattern mining algorithms: Apriori, frequent pattern growth(FP-Growth), and sequential pattern discovery using equivalence classes(SPADE). The SPADE algorithm was found to be the most efficient method for the dataset used. Finally, a prototype system named myDietPHIL was developed to manage clinical events for CRD patients’ and visualize the relationships of frequent clinical events. The system has been tested and visualization of relationships could facilitate patient education

Aerobic Lineage of the Oxidative Stress Response Protein Rubrerythrin Emerged in an Ancient Microaerobic, (Hyper)Thermophilic Environment

Indexación: Web of Science; Scopus.Rubrerythrins (RBRs) are non-heme di-iron proteins belonging to the ferritin-like superfamily. They are involved in oxidative stress defense as peroxide scavengers in a wide range of organisms. The vast majority of RBRs, including classical forms of this protein, contain a C-terminal rubredoxin-like domain involved in electron transport that is used during catalysis in anaerobic conditions. Rubredoxin is an ancient and large protein family of short length (<100 residues) that contains a Fe-S center involved in electron transfer. However, functional forms of the enzyme lacking the rubredoxin-like domain have been reported (e.g., sulerythrin and ferriperoxin). In this study, phylogenomic evidence is presented that suggests that a complete lineage of rubrerythrins, lacking the rubredoxin-like domain, arose in an ancient microaerobic and (hyper)thermophilic environments in the ancestors of the Archaea Thermoproteales and Sulfolobales. This lineage (termed the "aerobic-type" lineage) subsequently evolved to become adapted to environments with progressively lower temperatures and higher oxygen concentrations via the acquisition of two co-localized genes, termed DUF3501 and RFO, encoding a conserved protein of unknown function and a predicted Fe-S oxidoreductase, respectively. Proposed Horizontal Gene Transfer events from these archaeal ancestors to Bacteria expanded the opportunities for further evolution of this RBR including adaption to lower temperatures. The second lineage (termed the cyanobacterial lineage) is proposed to have evolved in cyanobacterial ancestors, maybe in direct response to the production of oxygen via oxygenic photosynthesis during the Great Oxygen Event (GOE). It is hypothesized that both lineages of RBR emerged in a largely anaerobic world with "whiffs" of oxygen and that their subsequent independent evolutionary trajectories allowed microorganisms to transition from this anaerobic world to an aerobic one.http://journal.frontiersin.org/article/10.3389/fmicb.2016.01822/ful

Protein sequence analysis using the MPI Bioinformatics Toolkit

The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) provides interactive access to a wide range of the best‐performing bioinformatics tools and databases, including the state‐of‐the‐art protein sequence comparison methods HHblits and HHpred. The Toolkit currently includes 35 external and in‐house tools, covering functionalities such as sequence similarity searching, prediction of sequence features, and sequence classification. Due to this breadth of functionality, the tight interconnection of its constituent tools, and its ease of use, the Toolkit has become an important resource for biomedical research and for teaching protein sequence analysis to students in the life sciences. In this article, we provide detailed information on utilizing the three most widely accessed tools within the Toolkit: HHpred for the detection of homologs, HHpred in conjunction with MODELLER for structure prediction and homology modeling, and CLANS for the visualization of relationships in large sequence datasets. Basic Protocol 1: Sequence similarity searching using HHpred Alternate Protocol: Pairwise sequence comparison using HHpred Support Protocol: Building a custom multiple sequence alignment using PSI‐BLAST and forwarding it as input to HHpred Basic Protocol 2: Calculation of homology models using HHpred and MODELLER Basic Protocol 3: Cluster analysis using CLAN

A resource-frugal probabilistic dictionary and applications in (meta)genomics

Genomic and metagenomic fields, generating huge sets of short genomic sequences, brought their own share of high performance problems. To extract relevant pieces of information from the huge data sets generated by current sequencing techniques, one must rely on extremely scalable methods and solutions. Indexing billions of objects is a task considered too expensive while being a fundamental need in this field. In this paper we propose a straightforward indexing structure that scales to billions of element and we propose two direct applications in genomics and metagenomics. We show that our proposal solves problem instances for which no other known solution scales-up. We believe that many tools and applications could benefit from either the fundamental data structure we provide or from the applications developed from this structure.Comment: Submitted to PSC 201