http://www.scfbm.org The latest research articles published by Source Code for Biology and Medicine 2011-10-13T00:00:00Z Background: Word-clouds recently emerged on the web as a solution for quickly summarizing text by maximizing the display of most relevant terms about a specific topic in the minimum amount of space. As biologists are faced with the daunting amount of new research data commonly presented in textual formats, word-clouds can be used to summarize and represent biological and/or biomedical content for various applications. Results: Genes2WordCloud is a web application that enables users to quickly identify biological themes from gene lists and research relevant text by constructing and displaying word-clouds. It provides users with several different options and ideas for the sources that can be used to generate a word-cloud. Different options for rendering and coloring the word-clouds give users the flexibility to quickly generate customized word-clouds of their choice. Methods: Genes2WordCloud is a word-cloud generator and a word-cloud viewer that is based on WordCram implemented using Java, Processing, AJAX, mySQL, and PHP. Text is fetched from several sources and then processed to extract the most relevant terms with their computed weights based on word frequencies. Genes2WordCloud is freely available for use online; it is open source software and is available for installation on any web-site along with supporting documentation at http://www.maayanlab.net/G2W. Conclusions: Genes2WordCloud provides a useful way to summarize and visualize large amounts of textual biological data or to find biological themes from several different sources. The open source availability of the software enables users to implement customized word-clouds on their own web-sites and desktop applications. http://www.scfbm.org/content/6/1/15 Caroline Baroukh Sherry Jenkins Ruth Danenfelser Avi Ma'ayan Source Code for Biology and Medicine 2011, null:15 2011-10-13T00:00:00Z doi:10.1186/1751-0473-6-15 /content/figures/1751-0473-6-15-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 15 2011-10-13T00:00:00Z 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: Current sequencing technology makes it practical to sequence many samples of a given organism, raising new challenges for the processing and interpretation of large genomics data sets with associated metadata. Traditional computational phylogenetic methods are ideal for studying the evolution of gene/protein families and using those to infer the evolution of an organism, but are less than ideal for the study of the whole organism mainly due to the presence of insertions/deletions/rearrangements. These methods provide the researcher with the ability to group a set of samples into distinct genotypic groups based on sequence similarity, which can then be associated with metadata, such as host information, pathogenicity, and time or location of occurrence. Genotyping is critical to understanding, at a genomic level, the origin and spread of infectious diseases. Increasingly, genotyping is coming into use for disease surveillance activities, as well as for microbial forensics. The classic genotyping approach has been based on phylogenetic analysis, starting with a multiple sequence alignment. Genotypes are then established by expert examination of phylogenetic trees. However, these traditional single-processor methods are suboptimal for rapidly growing sequence datasets being generated by next-generation DNA sequencing machines, because they increase in computational complexity quickly with the number of sequences. Results: Nephele is a suite of tools that uses the complete composition vector algorithm to represent each sequence in the dataset as a vector derived from its constituent k-mers by passing the need for multiple sequence alignment, and affinity propagation clustering to group the sequences into genotypes based on a distance measure over the vectors. Our methods produce results that correlate well with expert-defined clades or genotypes, at a fraction of the computational cost of traditional phylogenetic methods run on traditional hardware. Nephele can use the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. We were able to generate a neighbour-joined tree of over 10,000 16S samples in less than 2 hours. Conclusions: We conclude that using Nephele can substantially decrease the processing time required for generating genotype trees of tens to hundreds of organisms at genome scale sequence coverage. http://www.scfbm.org/content/6/1/13 Marc Colosimo Matthew Peterson Scott Mardis Lynette Hirschman Source Code for Biology and Medicine 2011, null:13 2011-08-18T00:00:00Z doi:10.1186/1751-0473-6-13 /content/figures/1751-0473-6-13-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 13 2011-08-18T00:00:00Z XML Background: We previously developed the DBRF-MEGN (difference-based regulation finding-minimum equivalent gene network) method, which deduces the most parsimonious signed directed graphs (SDGs) consistent with expression profiles of single-gene deletion mutants. However, until the present study, we have not presented the details of the method's algorithm or a proof of the algorithm. Results: We describe in detail the algorithm of the DBRF-MEGN method and prove that the algorithm deduces all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants. Conclusions: The DBRF-MEGN method provides all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants. http://www.scfbm.org/content/6/1/12 Koji Kyoda Kotaro Baba Hiroaki Kitano Shuichi Onami Source Code for Biology and Medicine 2011, null:12 2011-06-24T00:00:00Z doi:10.1186/1751-0473-6-12 /content/figures/1751-0473-6-12-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 12 2011-06-24T00:00:00Z 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 Background: Use of missing genotype imputations and haplotype reconstructions are valuable in genome-wide association studies (GWASs). By modeling the patterns of linkage disequilibrium in a reference panel, genotypes not directly measured in the study samples can be imputed and used for GWASs. Since millions of single nucleotide polymorphisms need to be imputed in a GWAS, faster methods for genotype imputation and haplotype reconstruction are required. Results: We developed a program package for parallel computation of genotype imputation and haplotype reconstruction. Our program package, ParaHaplo 3.0, is intended for use in workstation clusters using the Intel Message Passing Interface. We compared the performance of ParaHaplo 3.0 on the Japanese in Tokyo, Japan and Han Chinese in Beijing, and Chinese in the HapMap dataset. A parallel version of ParaHaplo 3.0 can conduct genotype imputation 20 times faster than a non-parallel version of ParaHaplo. Conclusions: ParaHaplo 3.0 is an invaluable tool for conducting haplotype-based GWASs. The need for faster genotype imputation and haplotype reconstruction using parallel computing will become increasingly important as the data sizes of such projects continue to increase. ParaHaplo executable binaries and program sources are available at http://en.sourceforge.jp/projects/parallelgwas/releases/. http://www.scfbm.org/content/6/1/10 Kazuharu Misawa Naoyuki Kamatani Source Code for Biology and Medicine 2011, null:10 2011-05-24T00:00:00Z doi:10.1186/1751-0473-6-10 /content/figures/1751-0473-6-10-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 10 2011-05-24T00:00:00Z XML Background: Laboratory Information Management Systems (LIMS) are an increasingly important part of modern laboratory infrastructure. As typically very sophisticated software products, LIMS often require considerable resources to select, deploy and maintain. Larger organisations may have access to specialist IT support to assist with requirements elicitation and software customisation, however smaller groups will often have limited IT support to perform the kind of iterative development that can resolve the difficulties that biologists often have when specifying requirements. Translational medicine aims to accelerate the process of treatment discovery by bringing together multiple disciplines to discover new approaches to treating disease, or novel applications of existing treatments. The diverse set of disciplines and complexity of processing procedures involved, especially with the use of high throughput technologies, bring difficulties in customizing a generic LIMS to provide a single system for managing sample related data within a translational medicine research setting, especially where limited IT support is available. Results: We have designed and developed a LIMS, BonsaiLIMS, around a very simple data model that can be easily implemented using a variety of technologies, and can be easily extended as specific requirements dictate. A reference implementation using Oracle 11 g database and the Python framework, Django is presented. Conclusions: By focusing on a minimal feature set and a modular design we have been able to deploy the BonsaiLIMS system very quickly. The benefits to our institute have been the avoidance of the prolonged implementation timescales, budget overruns, scope creep, off-specifications and user fatigue issues that typify many enterprise software implementations. The transition away from using local, uncontrolled records in spreadsheet and paper formats to a centrally held, secured and backed-up database brings the immediate benefits of improved data visibility, audit and overall data quality. The open-source availability of this software allows others to rapidly implement a LIMS which in itself might sufficiently address user requirements. In situations where this software does not meet requirements, it can serve to elicit more accurate specifications from end-users for a more heavyweight LIMS by acting as a demonstrable prototype. http://www.scfbm.org/content/6/1/9 Timothy Bath Selcuk Bozdag Vackar Afzal Daniel Crowther Source Code for Biology and Medicine 2011, null:9 2011-05-13T00:00:00Z doi:10.1186/1751-0473-6-9 /content/figures/1751-0473-6-9-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 9 2011-05-13T00:00:00Z XML The availability of bioinformatics web-based services is rapidly proliferating, for their interoperability and ease of use. The next challenge is in the integration of these services in the form of workflows, and several projects are already underway, standardizing the syntax, semantics, and user interfaces. In order to deploy the advantages of web services with locally installed tools, here we describe a collection of proxy client tools for 42 major bioinformatics web services in the form of European Molecular Biology Open Software Suite (EMBOSS) UNIX command-line tools. EMBOSS provides sophisticated means for discoverability and interoperability for hundreds of tools, and our package, named the Keio Bioinformatics Web Service (KBWS), adds functionalities of local and multiple alignment of sequences, phylogenetic analyses, and prediction of cellular localization of proteins and RNA secondary structures. This software implemented in C is available under GPL from http://www.g-language.org/kbws/ and GitHub repository http://github.com/cory-ko/KBWS. Users can utilize the SOAP services implemented in Perl directly via WSDL file at http://soap.g-language.org/kbws.wsdl (RPC Encoded) and http://soap.g-language.org/kbws_dl.wsdl (Document/literal). http://www.scfbm.org/content/6/1/8 Kazuki Oshita Kazuharu Arakawa Masaru Tomita Source Code for Biology and Medicine 2011, null:8 2011-04-29T00:00:00Z doi:10.1186/1751-0473-6-8 /content/figures/1751-0473-6-8-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 8 2011-04-29T00:00:00Z XML Background: When biological networks are studied, it is common to look for clusters, i.e. sets of nodes that are highly inter-connected. To understand the biological meaning of a cluster, the user usually has to sift through many textual annotations that are associated with biological entities.FindingsThe WordCloud Cytoscape plugin generates a visual summary of these annotations by displaying them as a tag cloud, where more frequent words are displayed using a larger font size. Word co-occurrence in a phrase can be visualized by arranging words in clusters or as a network. Conclusions: WordCloud provides a concise visual summary of annotations which is helpful for network analysis and interpretation. WordCloud is freely available at http://baderlab.org/Software/WordCloudPlugin http://www.scfbm.org/content/6/1/7 Layla Oesper Daniele Merico Ruth Isserlin Gary Bader Source Code for Biology and Medicine 2011, null:7 2011-04-07T00:00:00Z doi:10.1186/1751-0473-6-7 /content/figures/1751-0473-6-7-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 7 2011-04-07T00:00:00Z XML Background: Creating a user friendly web based application which executes an R script allows physicians, epidemiologists, and others unfamiliar with the statistical language to perform powerful statistical analyses easily. The geographic mapping of data is an important tool in spatial epidemiological analysis, and the R project includes many tools for such analyses, but few for visualization. Hence, web applications that run R for epidemiological analysis need to be able to present the results in a geographic format. Results: Rwui is a web application for creating web based applications for running R scripts. We describe updates to Rwui that enable it to create web applications for R scripts which return the results of the analysis to the web page as geographic maps. Conclusions: Rwui enables statisticians to create web applications for R scripts without the need to learn web programming. Creating a web application provides users access to an R based analysis without the need to learn R. Recent updates to Rwui have increased its applicability in the field of spatial epidemiological analysis. http://www.scfbm.org/content/6/1/6 Richard Newton Andrew Deonarine Lorenz Wernisch Source Code for Biology and Medicine 2011, null:6 2011-04-01T00:00:00Z doi:10.1186/1751-0473-6-6 /content/figures/1751-0473-6-6-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 6 2011-04-01T00:00:00Z XML