http://www.scfbm.org The most accessed research articles published by Source Code for Biology and Medicine 2011-09-08T00:00:00Z Background: The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods: In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results: We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. Conclusion: If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool. http://www.scfbm.org/content/3/1/17 Zoran Bursac Clinton Gauss David Williams David Hosmer Source Code for Biology and Medicine 2008, null:17 2008-12-16T00:00:00Z doi:10.1186/1751-0473-3-17 /content/figures/1751-0473-3-17-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 17 2008-12-16T00:00:00Z XML Background: Genomics and proteomics analyses regularly involve the simultaneous test of hundreds of hypotheses, either on numerical or categorical data. To correct for the occurrence of false positives, validation tests based on multiple testing correction, such as Bonferroni and Benjamini and Hochberg, and re-sampling, such as permutation tests, are frequently used. Despite the known power of permutation-based tests, most available tools offer such tests for either t-test or ANOVA only. Less attention has been given to tests for categorical data, such as the Chi-square. This project takes a first step by developing an open-source software tool, Ptest, that addresses the need to offer public software tools incorporating these and other statistical tests with options for correcting for multiple hypotheses. Results: This study developed a public-domain, user-friendly software whose purpose was twofold: first, to estimate test statistics for categorical and numerical data; and second, to validate the significance of the test statistics via Bonferroni, Benjamini and Hochberg, and a permutation test of numerical and categorical data. The tool allows the calculation of Chi-square test for categorical data, and ANOVA test, Bartlett's test and t-test for paired and unpaired data. Once a test statistic is calculated, Bonferroni, Benjamini and Hochberg, and a permutation tests are implemented, independently, to control for Type I errors. An evaluation of the software using different public data sets is reported, which illustrates the power of permutation tests for multiple hypotheses assessment and for controlling the rate of Type I errors. Conclusion: The analytical options offered by the software can be applied to support a significant spectrum of hypothesis testing tasks in functional genomics, using both numerical and categorical data. http://www.scfbm.org/content/3/1/15 Anyela Camargo Francisco Azuaje Haiying Wang Huiru Zheng Source Code for Biology and Medicine 2008, null:15 2008-10-21T00:00:00Z doi:10.1186/1751-0473-3-15 /content/figures/1751-0473-3-15-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 15 2008-10-21T00:00:00Z 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, null:13 2008-07-08T00:00:00Z doi:10.1186/1751-0473-3-13 /content/figures/1751-0473-3-13-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 13 2008-07-08T00:00:00Z 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, null:16 2008-11-14T00:00:00Z doi:10.1186/1751-0473-3-16 /content/figures/1751-0473-3-16-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 16 2008-11-14T00:00:00Z XML Background: Next Generation Sequencing (NGS) technology generates tens of millions of short reads for each DNA/RNA sample. A key step in NGS data analysis is the short read alignment of the generated sequences to a reference genome. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing this information. Results: We have developed a Graphical User Interface (GUI) software tool named SAMMate. SAMMate allows biomedical researchers to quickly process SAM/BAM files and is compatible with both single-end and paired-end sequencing technologies. SAMMate also automates some standard procedures in DNA-seq and RNA-seq data analysis. Using either standard or customized annotation files, SAMMate allows users to accurately calculate the short read coverage of genomic intervals. In particular, for RNA-seq data SAMMate can accurately calculate the gene expression abundance scores for customized genomic intervals using short reads originating from both exons and exon-exon junctions. Furthermore, SAMMate can quickly calculate a whole-genome signal map at base-wise resolution allowing researchers to solve an array of bioinformatics problems. Finally, SAMMate can export both a wiggle file for alignment visualization in the UCSC genome browser and an alignment statistics report. The biological impact of these features is demonstrated via several case studies that predict miRNA targets using short read alignment information files. Conclusions: With just a few mouse clicks, SAMMate will provide biomedical researchers easy access to important alignment information stored in SAM/BAM files. Our software is constantly updated and will greatly facilitate the downstream analysis of NGS data. Both the source code and the GUI executable are freely available under the GNU General Public License at http://sammate.sourceforge.net. http://www.scfbm.org/content/6/1/2 Guorong Xu Nan Deng Zhiyu Zhao Thair Judeh Erik Flemington Dongxiao Zhu Source Code for Biology and Medicine 2011, null:2 2011-01-13T00:00:00Z doi:10.1186/1751-0473-6-2 /content/figures/1751-0473-6-2-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 2 2011-01-13T00:00:00Z XML With ever-increasing numbers of microbial genomes being sequenced, efficient tools are needed to perform strain-level identification of any newly sequenced genome. Here, we present the SNP identification for strain typing (SNIT) pipeline, a fast and accurate software system that compares a newly sequenced bacterial genome with other genomes of the same species to identify single nucleotide polymorphisms (SNPs) and small insertions/deletions (indels). Based on this information, the pipeline analyzes the polymorphic loci present in all input genomes to identify the genome that has the fewest differences with the newly sequenced genome. Similarly, for each of the other genomes, SNIT identifies the input genome with the fewest differences. Results from five bacterial species show that the SNIT pipeline identifies the correct closest neighbor with 75% to 100% accuracy. The SNIT pipeline is available for download at http://www.bhsai.org/snit.html http://www.scfbm.org/content/6/1/14 Ravi Vijaya Satya Nela Zavaljevski Jaques Reifman Source Code for Biology and Medicine 2011, null:14 2011-09-08T00:00:00Z doi:10.1186/1751-0473-6-14 /content/figures/1751-0473-6-14-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 14 2011-09-08T00:00:00Z XML Background: Computer simulations are of increasing importance in modeling biological phenomena. Their purpose is to predict behavior and guide future experiments. The aim of this project is to model the early immune response to vaccination by an agent based immune response simulation that incorporates realistic biophysics and intracellular dynamics, and which is sufficiently flexible to accurately model the multi-scale nature and complexity of the immune system, while maintaining the high performance critical to scientific computing. Results: The Multiscale Systems Immunology (MSI) simulation framework is an object-oriented, modular simulation framework written in C++ and Python. The software implements a modular design that allows for flexible configuration of components and initialization of parameters, thus allowing simulations to be run that model processes occurring over different temporal and spatial scales. Conclusion: MSI addresses the need for a flexible and high-performing agent based model of the immune system. http://www.scfbm.org/content/3/1/6 Faheem Mitha Timothy Lucas Feng Feng Thomas Kepler Cliburn Chan Source Code for Biology and Medicine 2008, null:6 2008-04-28T00:00:00Z doi:10.1186/1751-0473-3-6 /content/figures/1751-0473-3-6-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 6 2008-04-28T00:00:00Z XML Recent developments in sequencing technologies have given the opportunity to sequence many bacterial genomes with limited cost and labor, compared to previous techniques. However, a limiting step of genome sequencing is the finishing process, needed to infer the relative position of each contig and close sequencing gaps. An additional degree of complexity is given by bacterial species harboring more than one replicon, which are not contemplated by the currently available programs. The availability of a large number of bacterial genomes allows geneticists to use complete genomes (possibly from the same species) as templates for contigs mapping.Here we present CONTIGuator, a software tool for contigs mapping over a reference genome which allows the visualization of a map of contigs, underlining loss and/or gain of genetic elements and permitting to finish multipartite genomes. The functionality of CONTIGuator was tested using four genomes, demonstrating its improved performances compared to currently available programs.Our approach appears efficient, with a clear visualization, allowing the user to perform comparative structural genomics analysis on draft genomes. CONTIGuator is a Python script for Linux environments and can be used on normal desktop machines and can be downloaded from http://contiguator.sourceforge.net. http://www.scfbm.org/content/6/1/11 Marco Galardini Emanuele Biondi Marco Bazzicalupo Alessio Mengoni Source Code for Biology and Medicine 2011, null:11 2011-06-21T00:00:00Z doi:10.1186/1751-0473-6-11 /content/figures/1751-0473-6-11-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 11 2011-06-21T00:00:00Z 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, null:10 2008-06-17T00:00:00Z doi:10.1186/1751-0473-3-10 /content/figures/1751-0473-3-10-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 10 2008-06-17T00:00:00Z 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, null:5 2009-07-22T00:00:00Z doi:10.1186/1751-0473-4-5 /content/figures/1751-0473-4-5-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 5 2009-07-22T00:00:00Z XML