Phylo-mLogo: an interactive and hierarchical multiple-logo visualization tool for alignment of many sequences.

BACKGROUND: When aligning several hundreds or thousands of sequences, such as epidemic virus sequences or homologous/orthologous sequences of some big gene families, to reconstruct the epidemiological history or their phylogenies, how to analyze and visualize the alignment results of many sequences has become a new challenge for computational biologists. Although there are several tools available for visualization of very long sequence alignments, few of them are applicable to the alignments of many sequences. RESULTS: A multiple-logo alignment visualization tool, called Phylo-mLogo, is presented in this paper. Phylo-mLogo calculates the variabilities and homogeneities of alignment sequences by base frequencies or entropies. Different from the traditional representations of sequence logos, Phylo-mLogo not only displays the global logo patterns of the whole alignment of multiple sequences, but also demonstrates their local homologous logos for each clade hierarchically. In addition, Phylo-mLogo also allows the user to focus only on the analysis of some important, structurally or functionally constrained sites in the alignment selected by the user or by built-in automatic calculation. CONCLUSION: With Phylo-mLogo, the user can symbolically and hierarchically visualize hundreds of aligned sequences simultaneously and easily check the changes of their amino acid sites when analyzing many homologous/orthologous or influenza virus sequences. More information of Phylo-mLogo can be found at URL http://biocomp.iis.sinica.edu.tw/phylomlogo.

The effectiveness of hospital-based diabetes case management: an example from a northern Taiwan regional hospital.

The aim of this study was to design, implement and evaluate disease outcomes at a regional hospital- based case management program of care for patients with type 2 diabetes. A medical team and practice guidelines were established in line with the health insurance strategy of Taiwan's Bureau of National Health Insurance (BNHI) and American Diabetes Association (ADA) Standards of Care for Diabetes (2003 edition). Also, a set of self-care booklets was designed suitable for use by the subject group. The study was prospective and followed the patients from enrollment to one year. Patient outcomes were determined based on laboratory examinations and recorded self-care behavior. Data were collected at enrollment and over 4 follow-up times within a one year period. Generalized Estimating Equation (GEE) multiple linear regression and logistic regression were used for repeated measurements and adjustments of the effects of specific prognostic factors. Sixty subjects diagnosed with type 2 diabetes (mean duration 3.25 years) were recruited. All participants were married with a mean age of 52.5 years. A majority (58.3%) was male and 65% were ethnic Hakka. Self-care knowledge and behavior accomplishment rates were: taking medications by oneself, 91.3% (knowing medicines, 25.4%); hypoglycemia management, 23.3%; monitoring blood sugar, 46.7%; exercise, 35.8%; diet management, 51.7% and foot care, 92.8%. Significantly improved ADA diabetes care standard items included HbA1C (p< .0001), fasting glucose (p< .01) and triglycerides (p< .05). The study incorporated evidence-based guidelines, public health insurance strategies and self-care booklets into a protocol to provide comprehensive care. The implemented diabetes program achieved diabetes care goals and improved patient self-care.

SinicView: a visualization environment for comparisons of multiple nucleotide sequence alignment tools.

BACKGROUND: Deluged by the rate and complexity of completed genomic sequences, the need to align longer sequences becomes more urgent, and many more tools have thus been developed. In the initial stage of genomic sequence analysis, a biologist is usually faced with the questions of how to choose the best tool to align sequences of interest and how to analyze and visualize the alignment results, and then with the question of whether poorly aligned regions produced by the tool are indeed not homologous or are just results due to inappropriate alignment tools or scoring systems used. Although several systematic evaluations of multiple sequence alignment (MSA) programs have been proposed, they may not provide a standard-bearer for most biologists because those poorly aligned regions in these evaluations are never discussed. Thus, a tool that allows cross comparison of the alignment results obtained by different tools simultaneously could help a biologist evaluate their correctness and accuracy. RESULTS: In this paper, we present a versatile alignment visualization system, called SinicView, (for Sequence-aligning INnovative and Interactive Comparison VIEWer), which allows the user to efficiently compare and evaluate assorted nucleotide alignment results obtained by different tools. SinicView calculates similarity of the alignment outputs under a fixed window using the sum-of-pairs method and provides scoring profiles of each set of aligned sequences. The user can visually compare alignment results either in graphic scoring profiles or in plain text format of the aligned nucleotides along with the annotations information. We illustrate the capabilities of our visualization system by comparing alignment results obtained by MLAGAN, MAVID, and MULTIZ, respectively. CONCLUSION: With SinicView, users can use their own data sequences to compare various alignment tools or scoring systems and select the most suitable one to perform alignment in the initial stage of sequence analysis.

POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome.

One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.