http://www.scfbm.org The latest research articles published by Source Code for Biology and Medicine 2010-07-13T00:00:00Z This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ Gaggle Tool Creator (GTC) is a web application which provides access to public annotation, interaction, orthology, and genomic data for hundreds of organisms, and enables instant analysis of the data using many popular web-based and desktop applications. http://www.scfbm.org/content/5/1/7 Dan Tenenbaum J Bare Nitin Baliga Source Code for Biology and Medicine 2010, 5:7 2010-07-13T00:00:00Z doi:10.1186/1751-0473-5-7 Source Code for Biology and Medicine 1751-0473 5 7 2010-07-13T00:00:00Z PDF Web applications for biology and medicine often need to integrate data from Entrez services provided by the National Center for Biotechnology Information. However, direct access to Entrez from a web browser is not possible due to 'same-origin' security restrictions. The use of "Asynchronous JavaScript and XML" (AJAX) to create rich, interactive web applications is now commonplace. The ability to access Entrez via AJAX would be advantageous in the creation of integrated biomedical web resources. We describe EntrezAJAX, which provides access to Entrez eUtils and is able to circumvent same-origin browser restrictions. EntrezAJAX is easily implemented by JavaScript developers and provides identical functionality as Entrez eUtils as well as enhanced functionality to ease development. We provide easy-to-understand developer examples written in JavaScript to illustrate potential uses of this service. For the purposes of speed, reliability and scalability, EntrezAJAX has been deployed on Google App Engine, a freely available cloud service. The EntrezAJAX webpage is located at http://entrezajax.appspot.com/ http://www.scfbm.org/content/5/1/6 Nicholas Loman Mark Pallen Source Code for Biology and Medicine 2010, 5:6 2010-06-21T00:00:00Z doi:10.1186/1751-0473-5-6 Source Code for Biology and Medicine 1751-0473 5 6 2010-06-21T00:00:00Z XML Background: The use of haplotype-based association tests can improve the power of genome-wide association studies. Since the observed genotypes are unordered pairs of alleles, haplotype phase must be inferred. However, estimating haplotype phase is time consuming. When millions of single-nucleotide polymorphisms (SNPs) are analyzed in genome-wide association study, faster methods for haplotype estimation are required. Methods: We developed a program package for parallel computation of haplotype estimation. Our program package, ParaHaplo 2.0, is intended for use in workstation clusters using the Intel Message Passing Interface (MPI). We compared the performance of our algorithm to that of the regular permutation test on both Japanese in Tokyo, Japan and Han Chinese in Beijing, China of the HapMap dataset. Results: Parallel version of ParaHaplo 2.0 can estimate haplotypes 100 times faster than a non-parallel version of the ParaHaplo. Conclusion: ParaHaplo 2.0 is an invaluable tool for conducting haplotype-based genome-wide association studies (GWAS). The need for fast haplotype estimation using parallel computing will become increasingly important as the data sizes of such projects continue to increase. The executable binaries and program sources of ParaHaplo are available at the following address: http://en.sourceforge.jp/projects/parallelgwas/releases/ http://www.scfbm.org/content/5/1/5 Kazuharu Misawa Naoyuki Kamatani Source Code for Biology and Medicine 2010, 5:5 2010-06-04T00:00:00Z doi:10.1186/1751-0473-5-5 Source Code for Biology and Medicine 1751-0473 5 5 2010-06-04T00:00:00Z XML Michigan's Department of Community Health (MDCH) is responsible for managing hospitals through the utilization of a Certificate of Need (CON) Commission. Regulation is achieved by limiting the number of beds a hospital can use for inpatient services. MDCH assigns hospitals to service areas and sub areas by use patterns. Hospital beds are then assigned within these Hospital Service Areas and Facility Sub Areas. The determination of the number of hospital beds a facility subarea is authorized to hold, called bed need, is defined in the Michigan Hospital Standards and published by the CON Commission and MDCH. These standards vaguely define a methodology for calculating hospital bed need for a projection year, five years ahead of the base year (defined as the most recent year for which patient data have been published by the Michigan Hospital Association). MDCH approached the authors and requested a reformulation of the process. Here we present a comprehensive guide and associated code as interpreted from the hospital standards with results from the 2011 projection year. Additionally, we discuss methodologies for other states and compare them to Michigan's Bed Need methodology. http://www.scfbm.org/content/5/1/4 Shaun Langley Steven Fuller Joseph Messina Ashton Shortridge Sue Grady Source Code for Biology and Medicine 2010, 5:4 2010-03-25T00:00:00Z doi:10.1186/1751-0473-5-4 Source Code for Biology and Medicine 1751-0473 5 4 2010-03-25T00:00:00Z XML Background: Meta-analysis is a major theme in biomedical research. In the present paper we introduce a package for R and Bioconductor that provides useful tools for performing this type of work. One idea behind the development of MADAM was that many meta-analysis methods, which are available in R, are not able to use the capacities of parallel computing yet. In this first version, we implemented one meta-analysis method in such a parallel manner. Additionally, we provide tools for combining the results from a set of methods in an ensemble approach. Functionality for visualization of results is also provided. Results: The presented package enables the carrying out of meta-analysis either by providing functions directly or by wrapping them to existing implementations. Overall, five different meta-analysis methods are now usable through MADAM, along with another three methods for combining the corresponding results. Visualizing the results is eased by three included functions. For developing and testing meta-analysis methods, a mock up data generator is integrated. Conclusions: The use of MADAM enables a user to focus on one package, in turn enabling them to work with the same data types across a set of methods. By making use of the snow package, MADAM can be made compatible with an existing parallel computing infrastructure. MADAM is open source and freely available within CRAN http://cran.r-project.org. http://www.scfbm.org/content/5/1/3 Karl Kugler Laurin Mueller Armin Graber Source Code for Biology and Medicine 2010, 5:3 2010-03-01T00:00:00Z doi:10.1186/1751-0473-5-3 Source Code for Biology and Medicine 1751-0473 5 3 2010-03-01T00:00:00Z XML Background: This paper describes a computer program named Dockres that is designed to analyze and summarize results of virtual screening of small molecules. The program is supplemented with utilities that support the screening process. Foremost among these utilities are scripts that run the virtual screening of a chemical library on a large number of processors in parallel. Methods: Dockres and some of its supporting utilities are written Fortran-77; other utilities are written as C-shell scripts. They support the parallel execution of the screening. The current implementation of the program handles virtual screening with Autodock-3 and Autodock-4, but can be extended to work with the output of other programs. Results: Analysis of virtual screening by Dockres led to both active and selective lead compounds. Conclusions: Analysis of virtual screening was facilitated and enhanced by Dockres in both the authors' laboratories as well as laboratories elsewhere. http://www.scfbm.org/content/5/1/2 Mihaly Mezei Ming-Ming Zhou Source Code for Biology and Medicine 2010, 5:2 2010-01-14T00:00:00Z doi:10.1186/1751-0473-5-2 Source Code for Biology and Medicine 1751-0473 5 2 2010-01-14T00:00:00Z XML Background: The manual counting of cells by microscopy is a commonly used technique across biological disciplines. Traditionally, hand tally counters have been used to track event counts. Although this method is adequate, there are a number of inefficiencies which arise when managing large numbers of samples or large sample sizes. Results: We describe software that mimics a traditional multi-register tally counter. Full customizability allows operation on any computer with minimal hardware requirements. The efficiency of counting large numbers of samples and/or large sample sizes is improved through the use of a "multi-count" register that allows single keystrokes to correspond to multiple events. Automatically updated multi-parameter values are implemented as user-specified equations, reducing errors and time required for manual calculations. The user interface was optimized for use with a touch screen and numeric keypad, eliminating the need for a full keyboard and mouse. Conclusions: Our software provides an inexpensive, flexible, and productivity-enhancing alternative to manual hand tally counters. http://www.scfbm.org/content/5/1/1 Charles Kim Joseph DeRisi Source Code for Biology and Medicine 2010, 5:1 2010-01-13T00:00:00Z doi:10.1186/1751-0473-5-1 Source Code for Biology and Medicine 1751-0473 5 1 2010-01-13T00:00:00Z XML Background: Visualization tools allow researchers to obtain a global view of the interrelationships between the probes or experiments of a gene expression (e.g. microarray) data set. Some existing methods include hierarchical clustering and k-means. In recent years, others have proposed applying minimum spanning trees (MST) for microarray clustering. Although MST-based clustering is formally equivalent to the dendrograms produced by hierarchical clustering under certain conditions; visually they can be quite different. Methods: HAMSTER (Helpful Abstraction using Minimum Spanning Trees for Expression Relations) is an open source system for generating a set of MSTs from the experiments of a microarray data set. While previous works have generated a single MST from a data set for data clustering, we recursively merge experiments and repeat this process to obtain a set of MSTs for data visualization. Depending on the parameters chosen, each tree is analogous to a snapshot of one step of the hierarchical clustering process. We scored and ranked these trees using one of three proposed schemes. HAMSTER is implemented in C++ and makes use of Graphviz for laying out each MST. Results: We report on the running time of HAMSTER and demonstrate using data sets from the NCBI Gene Expression Omnibus (GEO) that the images created by HAMSTER offer insights that differ from the dendrograms of hierarchical clustering. In addition to the C++ program which is available as open source, we also provided a web-based version (HAMSTER+) which allows users to apply our system through a web browser without any computer programming knowledge. Conclusion: Researchers may find it helpful to include HAMSTER in their microarray analysis workflow as it can offer insights that differ from hierarchical clustering. We believe that HAMSTER would be useful for certain types of gradient data sets (e.g time-series data) and data that indicate relationships between cells/tissues. Both the source and the web server variant of HAMSTER are available from http://hamster.cbrc.jp/. http://www.scfbm.org/content/4/1/8 Raymond Wan Larisa Kiseleva Hajime Harada Hiroshi Mamitsuka Paul Horton Source Code for Biology and Medicine 2009, 4:8 2009-11-20T00:00:00Z doi:10.1186/1751-0473-4-8 Source Code for Biology and Medicine 1751-0473 4 8 2009-11-20T00:00:00Z XML Background: Since more than a million single-nucleotide polymorphisms (SNPs) are analyzed in any given genome-wide association study (GWAS), performing multiple comparisons can be problematic. To cope with multiple-comparison problems in GWAS, haplotype-based algorithms were developed to correct for multiple comparisons at multiple SNP loci in linkage disequilibrium. A permutation test can also control problems inherent in multiple testing; however, both the calculation of exact probability and the execution of permutation tests are time-consuming. Faster methods for calculating exact probabilities and executing permutation tests are required. Methods: We developed a set of computer programs for the parallel computation of accurate P-values in haplotype-based GWAS. Our program, ParaHaplo, is intended for workstation clusters using the Intel Message Passing Interface (MPI). We compared the performance of our algorithm to that of the regular permutation test on JPT and CHB of HapMap. Results: ParaHaplo can detect smaller differences between 2 populations than SNP-based GWAS. We also found that parallel-computing techniques made ParaHaplo 100-fold faster than a non-parallel version of the program. Conclusion: ParaHaplo is a useful tool in conducting haplotype-based GWAS. Since the data sizes of such projects continue to increase, the use of fast computations with parallel computing--such as that used in ParaHaplo--will become increasingly important. The executable binaries and program sources of ParaHaplo are available at the following address: http://sourceforge.jp/projects/parallelgwas/?_sl=1 http://www.scfbm.org/content/4/1/7 Kazuharu Misawa Naoyuki Kamatani Source Code for Biology and Medicine 2009, 4:7 2009-10-21T00:00:00Z doi:10.1186/1751-0473-4-7 Source Code for Biology and Medicine 1751-0473 4 7 2009-10-21T00:00:00Z XML Chaos Game Representation (CGR) is a generalized scale-independent Markov transition table, which is useful for the visualization and comparative study of genomic signature, or for the study of characteristic sequence motifs. However, in order to fully utilize the scale-independent properties of CGR, it should be accessible through scale-independent user interface instead of static images. Here we describe a web server and Perl library for generating zoomable CGR images utilizing Google Maps API, which is also easily searchable for specific motifs. The web server is freely accessible at http://www.g-language.org/wiki/cgr/, and the Perl library as well as the source code is distributed with the G-language Genome Analysis Environment under GNU General Public License. http://www.scfbm.org/content/4/1/6 Kazuharu Arakawa Kazuki Oshita Masaru Tomita Source Code for Biology and Medicine 2009, 4:6 2009-09-17T00:00:00Z doi:10.1186/1751-0473-4-6 Source Code for Biology and Medicine 1751-0473 4 6 2009-09-17T00:00:00Z XML