WebBLAST 2.0: an integrated solution for organizing and analyzing sequence data.

SUMMARY: WebBLAST is a suite of programs intended to assist in organizing sequencing data and to provide first-pass sequence analysis in an automated fashion. Data processing is fully automated, with end-users being presented both graphical and tabular summaries of data that can be viewed using any Web browser. AVAILABILITY: The program is free and available at http://genome.nhgri.nih. gov/webblast.     

Molecular dynamics simulations of RNA: an in silico single molecule approach

RNA molecules are now known to be involved in the processing of genetic information at all levels, taking on a wide variety of central roles in the cell. Understanding how RNA molecules carry out their biological functions will require an understanding of structure and dynamics at the atomistic level, which can be significantly improved by combining computational simulation with experiment. This review provides a critical survey of the state of molecular dynamics (MD) simulations of RNA, including a discussion of important current limitations of the technique and examples of its successful application. Several types of simulations are discussed in detail, including those of structured RNA molecules and their interactions with the surrounding solvent and ions, catalytic RNAs, and RNA-small molecule and RNA-protein complexes. Increased cooperation between theorists and experimentalists will allow expanded judicious use of MD simulations to complement conceptually related single molecule experiments. Such cooperation will open the door to a fundamental understanding of the structure-function relationships in diverse and complex RNA molecules. .     

Simulated data sets for single molecule kinetics: some limitations and complications of data analysis

When the fluorescence intensity of a chromophore attached to or bound in an enzyme relates to a specific reactive step in the enzymatic reaction, a single molecule fluorescence study of the process reveals a time sequence in the fluorescence emission that can be analyzed to derive kinetic and mechanistic information. Reports of various experimental results and corresponding theoretical studies have provided a basis for interpreting these data and understanding the methodology. We have found it useful to parallel experiments with Monte Carlo simulations of potential models hypothesized to describe the reaction kinetics. The simulations can be adapted to include experimental limitations, such as limited data sets, and complexities such as dynamic disorder, where reaction rates appear to change over time. By using models that are known a priori, the simulations reveal some of the challenges of interpreting finite single-molecule data sets by employing various statistical signatures that have been identified.     

Sequence-specific amplification polymorphisms (SSAPs): a multi-locus approach for analyzing transposon insertions

Multiple copies of transposable elements, inserted at random around the host genome, can be used as molecular markers. Sequence-specific amplification polymorphisms (SSAPs) amplify the region between a PCR primer site near the end of an element and an adjacent restriction site in the flanking genomic DNA. Each amplified insertion is revealed as a band on a sequencing gel or capillary electrophoresis, and a genomic DNA sample produces a characteristic fingerprint of bands. Here, we explain the SSAP marker development method, which can be performed for any species, with recommendations for experimental parameters in several plant species. It takes about 1-2 weeks to complete the whole SSAP marker procedure.     

Nanopore Analysis of Wild-Type and Mutant Prion Protein (PrPC): Single Molecule Discrimination and PrPC Kinetics

Prion diseases are fatal neurodegenerative diseases associated with the conversion of cellular prion protein (PrP^C) in the central nervous system into the infectious isoform (PrP^Sc). The mechanics of conversion are almost entirely unknown, with understanding stymied by the lack of an atomic-level structure for PrP^Sc. A number of pathogenic PrP^C mutants exist that are characterized by an increased propensity for conversion into PrP^Sc and that differ from wild-type by only a single amino-acid point mutation in their primary structure. These mutations are known to perturb the stability and conformational dynamics of the protein. Understanding of how this occurs may provide insight into the mechanism of PrP^C conversion. In this work we sought to explore wild-type and pathogenic mutant prion protein structure and dynamics by analysis of the current fluctuations through an organic α-hemolysin nanometer-scale pore (nanopore) in which a single prion protein has been captured electrophoretically. In doing this, we find that wild-type and D178N mutant PrP^C, (a PrP^C mutant associated with both Fatal Familial Insomnia and Creutzfeldt-Jakob disease), exhibit easily distinguishable current signatures and kinetics inside the pore and we further demonstrate, with the use of Hidden Markov Model signal processing, accurate discrimination between these two proteins at the single molecule level based on the kinetics of a single PrP^C capture event. Moreover, we present a four-state model to describe wild-type PrP^C kinetics in the pore as a first step in our investigation on characterizing the differences in kinetics and conformational dynamics between wild-type and D178N mutant PrP^C. These results demonstrate the potential of nanopore analysis for highly sensitive, real-time protein and small molecule detection based on single molecule kinetics inside a nanopore, and show the utility of this technique as an assay to probe differences in stability between wild-type and mutant prion proteins at the single molecule level.     

Network-based pipeline for analyzing MS data: an application toward liver cancer

Current limitations in proteome analysis by high-throughput mass spectrometry (MS) approaches have sometimes led to incomplete (or inconclusive) data sets being published or unpublished. In this work, we used an iTRAQ reference data on hepatocellular carcinoma (HCC) to design a two-stage functional analysis pipeline to widen and improve the proteome coverage and, subsequently, to unveil the molecular changes that occur during HCC progression in human tumorous tissue. The first involved functional cluster analysis by incorporating an expansion step on a cleaned integrated network. The second used an in-house developed pathway database where recovery of shared neighbors was followed by pathway enrichment analysis. In the original MS data set, over 500 proteins were detected from the tumors of 12 male patients, but in this paper we reported an additional 1000 proteins after application of our bioinformatics pipeline. Through an integrative effort of network cleaning, community finding methods, and network analysis, we also uncovered several biologically interesting clusters implicated in HCC. We established that HCC transition from a moderate to poor stage involved densely connected clusters that comprised of PCNA, XRCC5, XRCC6, PARP1, PRKDC, and WRN. From our pathway enrichment analyses, it appeared that the HCC moderate stage, unlike the poor stage, is enriched in proteins involved in immune responses, thus suggesting the acquisition of immuno-evasion. Our strategy illustrates how an original oncoproteome could be expanded to one of a larger dynamic range where current technology limitations prevent/limit comprehensive proteome characterization.     

ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data

MOTIVATION: Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets. RESULTS: We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors.     

PredCSF: an integrated feature-based approach for predicting conotoxin superfamily

Conotoxins are small disulfide-rich peptides that are invaluable channel-targeted peptides and target neuronal receptors. They show prospects for being potent pharmaceuticals in the treatment of Alzheimer's disease, Parkinson's disease, and epilepsy. Accurate and fast prediction of conotoxin superfamily is very helpful towards the understanding of its biological and pharmacological functions especially in the post-genomic era. In the present study, we have developed a novel approach called PredCSF for predicting the conotoxin superfamily from the amino acid sequence directly based on fusing different kinds of sequential features by using modified one-versus-rest SVMs. The input features to the PredCSF classifiers are composed of physicochemical properties, evolutionary information, predicted second structure and amino acid composition, where the most important features are further screened by random forest feature selection to improve the prediction performance. The prediction results show that PredCSF can obtain an overall accuracy of 90.65% based on a benchmark dataset constructed from the most recent database, which consists of 4 main conotoxin superfamilies and 1 class of non-conotoxin class. Systematic experiments also show that combing different features is helpful for enhancing the prediction power when dealing with complex biological problems. PredCSF is expected to be a powerful tool for in silico identification of novel conotonxins and is freely available for academic use at http://www.csbio.sjtu.edu.cn/bioinf/PredCSF.     

NetAffx Gene Ontology Mining Tool: a visual approach for microarray data analysis

SUMMARY: The NetAffx Gene Ontology (GO) Mining Tool is a web-based, interactive tool that permits traversal of the GO graph in the context of microarray data. It accepts a list of Affymetrix probe sets and renders a GO graph as a heat map colored according to significance measurements. The rendered graph is interactive, with nodes linked to public web sites and to lists of the relevant probe sets. The GO Mining Tool provides visualization combining biological annotation with expression data, encompassing thousands of genes in one interactive view. AVAILABILITY: GO Mining Tool is freely available at http://www.affymetrix.com/analysis/query/go_analysis.affx     

ProteoMix: an integrated and flexible system for interactively analyzing large numbers of protein sequences

ProteoMix is a suite of JAVA programs for identifying, annotating and predicting regions of interest in large sets of amino acid sequences, according to systematic and consistent criteria. It is based on two concepts (1) the integration of results from different sequence analysis tools increases the prediction reliability; and (2) the integration protocol is critical and needs to be easily adaptable in a case-by-case manner. ProteoMix was designed to analyze simultaneously multiple protein sequences using several bioinformatics tools, merge the results of the analyses using logical functions and display them on an integrated viewer. In addition, new sequences can be added seamlessly to an analysis performed on an initial set of sequences. ProteoMix has a modular design, and bioinformatics tools are run on remote servers accessed using the Internet Simple Object Access Protocol (SOAP), ensuring the swift implementation of additional tools. ProteoMix has a user-friendly interactive graphical user interface environment and runs on PCs with Microsoft OS. AVAILABILITY: ProteoMix is freely available for academic users at http://bio.gsc.riken.jp/ProteoMix/     

SCOPExplorer: a tool for browsing and analyzing structural classification of proteins (SCOP) data

We have developed a tool, named "SCOPExplorer", for browsing and analyzing SCOP information. SCOPExplorer 1) contains a tree-style viewer to display an overview of protein structure data, 2) is able to employ a variety of options to analyze SCOP data statistically, and 3) provides a function to link protein domains to protein data bank (PDB) resources. SCOPExplorer uses an XML-based structural document format, named "SCOPML", derived from the SCOP data. To evaluate SCOPExplorer, proteins containing more than 20 domains were analyzed. The Skp1-Skp2 protein complex and the Fab fragment of IgG2 contain the largest numbers of domains in the current eukaryotic SCOP database. These proteins are known to either bind to various proteins or generate diversity. This suggests that the more domains a protein has, the more interactions or more variability it will be capable of. (SCOPExplorer is available for download at http://scopexplorer.ulsan.ac.kr).     

Managing riparian zones for river health improvement: an integrated approach

Riparian zones are among the most valuable ecosystems on the earth. They act as the ecological engineers that improve river health through delivering a range of ecosystem functions. Stream bank stabilization, pollutant and sediment buffering, temperature regulation, provision of energy to river food webs and communities, groundwater recharge and provision of ecological corridors and habitat for wildlife, are among major ecosystem functions of riparian zones that play a great role in river health. Besides these ecosystem functions, riparian zones also provide various ecosystem goods and services for human well-being. But in the current scenario, riparian zones are under severe threat due to agricultural activities, urbanization, river flow alteration, overexploitation, climate change, pollution, and biological invasion. In the present and probable future scenarios of declining river health and global environmental changes, there is a pressing need of an integrated approach for managing riparian zones. This review article aims to advocate an integrated approach for riparian zone management based on various components such as riparian condition assessment, policy framework, stakeholder’s participation, management practices, legislation, and awareness. Authors also discussed riparian zones in context of their concepts, features, functions, and threats.

     

DivStat: A User-Friendly Tool for Single Nucleotide Polymorphism Analysis of Genomic Diversity

Recent developments have led to an enormous increase of publicly available large genomic data, including complete genomes. The 1000 Genomes Project was a major contributor, releasing the results of sequencing a large number of individual genomes, and allowing for a myriad of large scale studies on human genetic variation. However, the tools currently available are insufficient when the goal concerns some analyses of data sets encompassing more than hundreds of base pairs and when considering haplotype sequences of single nucleotide polymorphisms (SNPs). Here, we present a new and potent tool to deal with large data sets allowing the computation of a variety of summary statistics of population genetic data, increasing the speed of data analysis.     

A tool for analyzing information from data storage tags: the continuous wavelet transform (CWT)

Electronic Data Storage Tags (DSTs) have the potential of providing long term, high-resolution observation data of individual fish in the ecosystem. However, traditional time series analysis methods are limited in extracting the required information from DSTs. The continuous wavelet transform (CWT) is a tool for decomposing a time series into instantaneous frequency versus time characteristics. This allows data signals, which are restricted to specific frequencies (localized frequencies), or frequency components, which are restricted to specific time intervals (time localized) to be extracted and displayed in a highly intuitive way. The aim of this short communication is to present the CWT as a tool for extracting information from DST data. In the first case, synthetically generated data is used to demonstrate how the methodology captures different time–frequency dependent patterns in the time series data. The relevance of the methodology to real field data is demonstrated by an analysis of DST depth data from tagged Barents Sea coastal cod. This work has also enjoyed partial funding from the EU-FP5-Q5RS200200813 CODYSSEY project.