GeneNotes – A novel information management software for biologists

BACKGROUND: Collecting and managing information is a challenging task in a genome-wide profiling research project. Most databases and online computational tools require a direct human involvement. Information and computational results are presented in various multimedia formats (e.g., text, image, PDF, word files, etc.), many of which cannot be automatically processed by computers in biologically meaningful ways. In addition, the quality of computational results is far from perfect and requires nontrivial manual examination. The timely selection, integration and interpretation of heterogeneous biological information still heavily rely on the sensibility of biologists. Biologists often feel overwhelmed by the huge amount of and the great diversity of distributed heterogeneous biological information. DESCRIPTION: We developed an information management application called GeneNotes. GeneNotes is the first application that allows users to collect and manage multimedia biological information about genes/ESTs. GeneNotes provides an integrated environment for users to surf the Internet, collect notes for genes/ESTs, and retrieve notes. GeneNotes is supported by a server that integrates gene annotations from many major databases (e.g., HGNC, MGI, etc.). GeneNotes uses the integrated gene annotations to (a) identify genes given various types of gene IDs (e.g., RefSeq ID, GenBank ID, etc.), and (b) provide quick views of genes. GeneNotes is free for academic usage. The program and the tutorials are available at: . CONCLUSIONS: GeneNotes provides a novel human-computer interface to assist researchers to collect and manage biological information. It also provides a platform for studying how users behave when they manipulate biological information. The results of such study can lead to innovation of more intelligent human-computer interfaces that greatly shorten the cycle of biology research

MACSIMS : multiple alignment of complete sequences information management system

BACKGROUND: In the post-genomic era, systems-level studies are being performed that seek to explain complex biological systems by integrating diverse resources from fields such as genomics, proteomics or transcriptomics. New information management systems are now needed for the collection, validation and analysis of the vast amount of heterogeneous data available. Multiple alignments of complete sequences provide an ideal environment for the integration of this information in the context of the protein family. RESULTS: MACSIMS is a multiple alignment-based information management program that combines the advantages of both knowledge-based and ab initio sequence analysis methods. Structural and functional information is retrieved automatically from the public databases. In the multiple alignment, homologous regions are identified and the retrieved data is evaluated and propagated from known to unknown sequences with these reliable regions. In a large-scale evaluation, the specificity of the propagated sequence features is estimated to be >99%, i.e. very few false positive predictions are made. MACSIMS is then used to characterise mutations in a test set of 100 proteins that are known to be involved in human genetic diseases. The number of sequence features associated with these proteins was increased by 60%, compared to the features available in the public databases. An XML format output file allows automatic parsing of the MACSIM results, while a graphical display using the JalView program allows manual analysis. CONCLUSION: MACSIMS is a new information management system that incorporates detailed analyses of protein families at the structural, functional and evolutionary levels. MACSIMS thus provides a unique environment that facilitates knowledge extraction and the presentation of the most pertinent information to the biologist. A web server and the source code are available at

xGDB: open-source computational infrastructure for the integrated evaluation and analysis of genome features

The eXtensible Genome Data Broker (xGDB) provides a software infrastructure consisting of integrated tools for the storage, display, and analysis of genome features in their genomic context. Common features include gene structure annotations, spliced alignments, mapping of repetitive sequence, and microarray probes, but the software supports inclusion of any property that can be associated with a genomic location. The xGDB distribution and user support utilities are available online at the xGDB project website, http://xgdb.sourceforge.net/

Prospective Study of Metal Fume-Induced Responses of Global Gene Expression Profiling in Whole Blood

Metal particulate inhalation causes pulmonary and cardiovascular diseases. Our previous results showed that systemic responses to short-term occupational welding-fume exposure could be assessed by microarray analyses in whole-blood total RNA sampled before and after exposure. To expand our understanding of the duration of particulate-induced gene expression changes, we conducted a study using a similar population 1 yr after the original study and extended our observations in the postexposure period. We recruited 15 individuals with welding fume exposure and 7 nonexposed individuals. Thirteen of the 22 individuals (9 in exposed group and 4 in nonexposed group) had been monitored in the previous study. Whole-blood total RNA was analyzed at 3 time points, including baseline, immediately following exposure (approximately 5 h after baseline), and 24 h after baseline, using cDNA microarray technology. We replicated the patterns of Gene Ontology (GO) terms associated with response to stimulus, cell death, phosphorus metabolism, localization, and regulation of biological processes significantly enriched with altered genes in the nonsmoking exposed group. Most of the identified genes had opposite expression changes between the exposure and postexposure periods in nonsmoking welders. In addition, we found dose-dependent patterns that were affected by smoking status. In conclusion, short-term occupational exposure to metal particulates causes systemic responses in the peripheral blood. Furthermore, the acute particulate-induced effects on gene expression profiling were transient in nonsmoking welders, with most effects diminishing within 19 h following exposure

CELO: A System for Efficiently Building Informatics Solutions to Manage Biomedical Research Data

Traditional data management methods are unable to sufficiently support growing trends in biomedical research such as collection of larger data sets, use of diverse data types, and sharing of data among multiple laboratories. Although many technologies are readily available to help laboratories build data management solutions, many laboratories are not taking advantage of them. This may be due to hardware and software costs, the need for an informaticist to build customized solutions, and long development times. Several systems already exist which attempt to address the informatics needs of biomedical researchers. A review of these systems has revealed the benefits and drawbacks of various system design approaches, and has helped us to identify a set of core requirements for a system that will successfully serve the biomedical research community. In consideration of these requirements, we developed the Customizable Electronic Laboratory Online (CELO) system to help laboratories efficiently build cost-effective informatics solutions. CELO automatically creates a generic database and web interface for laboratories that submit a simple web registration form. Researchers can then build their own customized data management systems using web-based features such as configurable user permissions, customizable user interfaces, support for multimedia files, and templates for defining research data representations. An evaluation of the CELO system has demonstrated its ability to efficiently create customized solutions for research laboratories with basic data management needs. The evaluation has also highlighted areas in which CELO can be improved and has elucidated potential research problems that may be of interest to the biomedical informatics field