http://www.scfbm.org The most accessed research articles published by Source Code for Biology and Medicine 2010-03-01T00:00:00Z 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/ 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 PDF 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 Flow is an open source software application for clinical and experimental researchers to perform exploratory data analysis, clustering and annotation of flow cytometric data. Flow is an extensible system that offers the ease of use commonly found in commercial flow cytometry software packages and the statistical power of academic packages like the R BioConductor project. http://www.scfbm.org/content/3/1/10 Jacob Frelinger Thomas Kepler Cliburn Chan Source Code for Biology and Medicine 2008, 3:10 2008-06-17T00:00:00Z doi:10.1186/1751-0473-3-10 Source Code for Biology and Medicine 1751-0473 3 10 2008-06-17T00:00:00Z 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 Modern life sciences research increasingly relies on computational solutions, from large scale data analyses to theoretical modeling. Within the theoretical models Boolean networks occupy an increasing role as they are eminently suited at mapping biological observations and hypotheses into a mathematical formalism. The conceptual underpinnings of Boolean modeling are very accessible even without a background in quantitative sciences, yet it allows life scientists to describe and explore a wide range of surprisingly complex phenomena. In this paper we provide a clear overview of the concepts used in Boolean simulations, present a software library that can perform these simulations based on simple text inputs and give three case studies. The large scale simulations in these case studies demonstrate the Boolean paradigms and their applicability as well as the advanced features and complex use cases that our software package allows. Our software is distributed via a liberal Open Source license and is freely accessible from http://booleannet.googlecode.com http://www.scfbm.org/content/3/1/16 Istvan Albert Juilee Thakar Song Li Ranran Zhang Reka Albert Source Code for Biology and Medicine 2008, 3:16 2008-11-14T00:00:00Z doi:10.1186/1751-0473-3-16 Source Code for Biology and Medicine 1751-0473 3 16 2008-11-14T00:00:00Z XML Background: Biological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimenting and data acquisition has been developing rapidly in the past years, automated image analysis often introduces a bottleneck in high content screening. Methods: Wndchrm is an open source utility for biological image analysis. The software works by first extracting image content descriptors from the raw image, image transforms, and compound image transforms. Then, the most informative features are selected, and the feature vector of each image is used for classification and similarity measurement. Results: Wndchrm has been tested using several publicly available biological datasets, and provided results which are favorably comparable to the performance of task-specific algorithms developed for these datasets. The simple user interface allows researchers who are not knowledgeable in computer vision methods and have no background in computer programming to apply image analysis to their data. Conclusion: We suggest that wndchrm can be effectively used for a wide range of biological image analysis tasks. Using wndchrm can allow scientists to perform automated biological image analysis while avoiding the costly challenge of implementing computer vision and pattern recognition algorithms. http://www.scfbm.org/content/3/1/13 Lior Shamir Nikita Orlov D. Mark Eckley Tomasz Macura Josiah Johnston Ilya Goldberg Source Code for Biology and Medicine 2008, 3:13 2008-07-08T00:00:00Z doi:10.1186/1751-0473-3-13 Source Code for Biology and Medicine 1751-0473 3 13 2008-07-08T00:00:00Z 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 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: Video cameras sense passively from a distance, offer a rich information stream, and provide intuitively meaningful raw data. Camera-based imaging has thus proven critical for many advances in neuroscience and biology, with applications ranging from cellular imaging of fluorescent dyes to tracking of whole-animal behavior at ecologically relevant spatial scales. Results: Here we present 'Motmot': an open-source software suite for acquiring, displaying, saving, and analyzing digital video in real-time. At the highest level, Motmot is written in the Python computer language. The large amounts of data produced by digital cameras are handled by low-level, optimized functions, usually written in C. This high-level/low-level partitioning and use of select external libraries allow Motmot, with only modest complexity, to perform well as a core technology for many high-performance imaging tasks. In its current form, Motmot allows for: (1) image acquisition from a variety of camera interfaces (package motmot.cam_iface), (2) the display of these images with minimal latency and computer resources using wxPython and OpenGL (package motmot.wxglvideo), (3) saving images with no compression in a single-pass, low-CPU-use format (package motmot.FlyMovieFormat), (4) a pluggable framework for custom analysis of images in realtime and (5) firmware for an inexpensive USB device to synchronize image acquisition across multiple cameras, with analog input, or with other hardware devices (package motmot.fview_ext_trig). These capabilities are brought together in a graphical user interface, called 'FView', allowing an end user to easily view and save digital video without writing any code. One plugin for FView, 'FlyTrax', which tracks the movement of fruit flies in real-time, is included with Motmot, and is described to illustrate the capabilities of FView. Conclusion: Motmot enables realtime image processing and display using the Python computer language. In addition to the provided complete applications, the architecture allows the user to write relatively simple plugins, which can accomplish a variety of computer vision tasks and be integrated within larger software systems. The software is available at http://code.astraw.com/projects/motmot http://www.scfbm.org/content/4/1/5 Andrew Straw Michael Dickinson Source Code for Biology and Medicine 2009, 4:5 2009-07-22T00:00:00Z doi:10.1186/1751-0473-4-5 Source Code for Biology and Medicine 1751-0473 4 5 2009-07-22T00:00:00Z XML Background: Fungi from environmental samples are typically identified to species level through DNA sequencing of the nuclear ribosomal internal transcribed spacer (ITS) region for use in BLAST-based similarity searches in the International Nucleotide Sequence Databases. These searches are time-consuming and regularly require a significant amount of manual intervention and complementary analyses. We here present software – in the form of an identification pipeline for large sets of fungal ITS sequences – developed to automate the BLAST process and several additional analysis steps. The performance of the pipeline was evaluated on a dataset of 350 ITS sequences from fungi growing as epiphytes on building material. Results: The pipeline was written in Perl and uses a local installation of NCBI-BLAST for the similarity searches of the query sequences. The variable subregion ITS2 of the ITS region is extracted from the sequences and used for additional searches of higher sensitivity. Multiple alignments of each query sequence and its closest matches are computed, and query sequences sharing at least 50% of their best matches are clustered to facilitate the evaluation of hypothetically conspecific groups. The pipeline proved to speed up the processing, as well as enhance the resolution, of the evaluation dataset considerably, and the fungi were found to belong chiefly to the Ascomycota, with Penicillium and Aspergillus as the two most common genera. The ITS2 was found to indicate a different taxonomic affiliation than did the complete ITS region for 10% of the query sequences, though this figure is likely to vary with the taxonomic scope of the query sequences. Conclusion: The present software readily assigns large sets of fungal query sequences to their respective best matches in the international sequence databases and places them in a larger biological context. The output is highly structured to be easy to process, although it still needs to be inspected and possibly corrected for the impact of the incomplete and sometimes erroneously annotated fungal entries in these databases. The open source pipeline is available for UNIX-type platforms, and updated releases of the target database are made available biweekly. The pipeline is easily modified to operate on other molecular regions and organism groups. http://www.scfbm.org/content/4/1/1 Henrik Nilsson Gunilla Bok Martin Ryberg Erik Kristiansson Nils Hallenberg Source Code for Biology and Medicine 2009, 4:1 2009-01-15T00:00:00Z doi:10.1186/1751-0473-4-1 Source Code for Biology and Medicine 1751-0473 4 1 2009-01-15T00:00:00Z XML