G-InforBIO: integrated system for microbial genomics

Background  

Genome databases contain diverse kinds of information, including gene annotations and nucleotide and amino acid sequences. It is not easy to integrate such information for genomic study. There are few tools for integrated analyses of genomic data, therefore, we developed software that enables users to handle, manipulate, and analyze genome data with a variety of sequence analysis programs.     

Identification of discriminative characteristics for clusters from biologic data with InforBIO software

Background  

There are a number of different methods for generation of trees and algorithms for phylogenetic analysis in the study of bacterial taxonomy. Genotypic information, such as SSU rRNA gene sequences, now plays a more prominent role in microbial systematics than does phenotypic information. However, the integration of genotypic and phenotypic information for polyphasic studies is necessary for the classification and identification of microbes. Thus, we devised an algorithm that objectively identifies discriminative characteristics for focused clusters on generated trees from a dataset composed of coded data, such as phenotypic information. Moreover, this algorithm has been integrated into the polyphasic analysis software, InforBIO.     

Columba: an integrated database of proteins,structures, and annotations

Background  

Structural and functional research often requires the computation of sets of protein structures based on certain properties of the proteins, such as sequence features, fold classification, or functional annotation. Compiling such sets using current web resources is tedious because the necessary data are spread over many different databases. To facilitate this task, we have created COLUMBA, an integrated database of annotations of protein structures.     

Integrating protein structures and precomputed genealogies in the Magnum database: Examples with cellular retinoid binding proteins

Background  

When accurate models for the divergent evolution of protein sequences are integrated with complementary biological information, such as folded protein structures, analyses of the combined data often lead to new hypotheses about molecular physiology. This represents an excellent example of how bioinformatics can be used to guide experimental research. However, progress in this direction has been slowed by the lack of a publicly available resource suitable for general use.     

ProGenExpress: Visualization of quantitative data on prokaryotic genomes

Background  

The integration of genomic information with quantitative experimental data is a key component of systems biology. An increasing number of microbial genomes are being sequenced, leading to an increasing amount of data from post-genomics technologies. The genomes of prokaryotes contain many structures of interest, such as operons, pathogenicity islands and prophage sequences, whose behaviour is of interest during infection and disease. There is a need for simple and novel tools to display and analyse data from these integrated datasets, and we have developed ProGenExpress as a tool for visualising arbitrarily complex numerical data in the context of prokaryotic genomes.     

GDR (<Emphasis Type="Underline">G</Emphasis> enome <Emphasis Type="Underline">D</Emphasis> atabase for <Emphasis Type="Underline">R</Emphasis> osaceae): integrated web resources for Rosaceae genomics and genetics research

Background  

Peach is being developed as a model organism for Rosaceae, an economically important family that includes fruits and ornamental plants such as apple, pear, strawberry, cherry, almond and rose. The genomics and genetics data of peach can play a significant role in the gene discovery and the genetic understanding of related species. The effective utilization of these peach resources, however, requires the development of an integrated and centralized database with associated analysis tools.     

Multiclass discovery in array data

Background  

A routine goal in the analysis of microarray data is to identify genes with expression levels that correlate with known classes of experiments. In a growing number of array data sets, it has been shown that there is an over-abundance of genes that discriminate between known classes as compared to expectations for random classes. Therefore, one can search for novel classes in array data by looking for partitions of experiments for which there are an over-abundance of discriminatory genes. We have previously used such an approach in a breast cancer study.     

Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types

Background  

Much of the public access cancer microarray data is asymmetric, belonging to datasets containing no samples from normal tissue. Asymmetric data cannot be used in standard meta-analysis approaches (such as the inverse variance method) to obtain large sample sizes for statistical power enrichment. Noting that plenty of normal tissue microarray samples exist in studies not involving cancer, we investigated the viability and accuracy of an integrated microarray analysis approach based on significance analysis of microarrays (merged SAM) using a collection of data from separate diseased and normal samples.     

Trace2PS and FSA2PS: two software toolkits for converting trace and fsa files to PostScript format

Background  

Due to the advanced techniques in sequencing and fragment analysis, DNA sequencers and analyzers produce vast amounts of data within short time. To administrate the large data volume conveniently, efficient data management systems are used in order to process and to store sequencers' or analyzers' data outcome. The inclusion of graphical reports in such systems is necessary to achieve a comprehensive view of the integrated data. However, the resulting data of sequencing and fragment analysis runs are stored in a proprietary format, the so-called trace or fsa format, which is only readable by programs provided by the instrument's vendor operating on the machine itself or by commercial tools designed for editing the respective data. To allow for a quick conversion of the proprietary data format into a commonly used one, toolkits are required that reach this aim and can be easily integrated into workflow systems.     

The bovine QTL viewer: a web accessible database of bovine Quantitative Trait Loci

Background  

Many important agricultural traits such as weight gain, milk fat content and intramuscular fat (marbling) in cattle are quantitative traits. Most of the information on these traits has not previously been integrated into a genomic context. Without such integration application of these data to agricultural enterprises will remain slow and inefficient. Our goal was to populate a genomic database with data mined from the bovine quantitative trait literature and to make these data available in a genomic context to researchers via a user friendly query interface.     

Integrated analysis of metabolic phenotypes in Saccharomyces cerevisiae

Background  

The yeast Saccharomyces cerevisiae is an important microorganism for both industrial processes and scientific research. Consequently, there have been extensive efforts to characterize its cellular processes. In order to fully understand the relationship between yeast's genome and its physiology, the stockpiles of diverse biological data sets that describe its cellular components and phenotypic behavior must be integrated at the genome-scale. Genome-scale metabolic networks have been reconstructed for several microorganisms, including S. cerevisiae, and the properties of these networks have been successfully analyzed using a variety of constraint-based methods. Phenotypic phase plane analysis is a constraint-based method which provides a global view of how optimal growth rates are affected by changes in two environmental variables such as a carbon and an oxygen uptake rate. Some applications of phenotypic phase plane analysis include the study of optimal growth rates and of network capaCity and function.     

Endothelial cell markers reflecting endothelial cell dysfunction in patients with mixed connective tissue disease

Introduction  

The aim of the present study was to investigate the association between cardiovascular risk factors and endothelial dysfunction in patients with mixed connective tissue disease (MCTD) and to determine which biomarkers are associated with atherosclerotic complications, such as cardiovascular disease.     

Virus variation resources at the National Center for Biotechnology Information: dengue virus

Background  

There is an increasing number of complete and incomplete virus genome sequences available in public databases. This large body of sequence data harbors information about epidemiology, phylogeny, and virulence. Several specialized databases, such as the NCBI Influenza Virus Resource or the Los Alamos HIV database, offer sophisticated query interfaces along with integrated exploratory data analysis tools for individual virus species to facilitate extracting this information. Thus far, there has not been a comprehensive database for dengue virus, a significant public health threat.     

Compressing DNA sequence databases with coil

Background  

Publicly available DNA sequence databases such as GenBank are large, and are growing at an exponential rate. The sheer volume of data being dealt with presents serious storage and data communications problems. Currently, sequence data is usually kept in large "flat files," which are then compressed using standard Lempel-Ziv (gzip) compression – an approach which rarely achieves good compression ratios. While much research has been done on compressing individual DNA sequences, surprisingly little has focused on the compression of entire databases of such sequences. In this study we introduce the sequence database compression software coil.     

Eutrophication of aquatic ecosystems a new method for calculating the potential contributions of nitrogen and phosphorus

Aim, Scope and Background  

Aquatic eutrophication is a widespread problem in inland and coastal waters around the world and it should therefore be one of the impact categories to be considered in LCA studies of products and services. In LCAs there are several impact assessment methods to determine characterisation factors for eutrophying nutrients, but few methods have been developed to model fate and spatial aspects. One such method was developed as part of an LCA application of the Finnish forest industry. The aim of this study was to present this characterisation method in which the potential contributions of nitrogen and phosphorus to eutrophication of aquatic ecosystems are calculated. The use of the method was demonstrated by producing site/sector-specific characterisation factors and by constructing a reference value of aquatic eutrophication for Finland. A discussion of sensitivity and uncertainty aspects related to input data is also presented.     

Energy decision making in a pulp and paper mill: selection of LCA system boundary

Background, aim, and scope  

North American pulp and paper mills are facing tremendous challenges, which may necessitate major mill modernizations. An example is process modification to reduce dependency on purchased power, which is an expensive resource. Such modifications may have environmental implications at the mills’ sites, on their product life cycle, and on other interconnected systems, and therefore, systematic tools such as life cycle assessment (LCA) need to be applied. Different LCA system boundary approaches can be used for such process design applications, and these approaches need to be compared to determine their respective benefits and limitations in this context. This study compares setting the system boundary according to a cradle-to-gate approach [attributional LCA (ALCA)] and a system expansion [consequential LCA (CLCA)] approach using a case study, which deals with implementing cogeneration and increased de-inked pulp production at an integrated newsprint mill.