http://www.scfbm.org The latest research articles published by Source Code for Biology and Medicine 2013-05-21T00:00:00Z Background: Identifying and quantifying pathological changes in brain electrical activity is important for investigations of brain injury and neurological disease. An example is the development of epilepsy, a secondary consequence of traumatic brain injury. While certain epileptiform events can be identified visually from electroencephalographic (EEG) or electrocorticographic (ECoG) records, quantification of these pathological events has proved to be more difficult. In this study we developed MATLAB-based software that would assist detection of pathological brain electrical activity following traumatic brain injury (TBI) and present our MATLAB code used for the analysis of the ECoG. Methods: Software was developed using MATLAB(TM) and features of the open access EEGLAB. EEGgui is a graphical user interface in the MATLAB programming platform that allows scientists who are not proficient in computer programming to perform a number of elaborate analyses on ECoG signals. The different analyses include Power Spectral Density (PSD), Short Time Fourier analysis and Spectral Entropy (SE). ECoG records used for demonstration of this software were derived from rats that had undergone traumatic brain injury one year earlier. Results: The software provided in this report provides a graphical user interface for displaying ECoG activity and calculating normalized power density using fast fourier transform of the major brain wave frequencies (Delta, Theta, Alpha, Beta1, Beta2 and Gamma). The software further detects events in which power density for these frequency bands exceeds normal ECoG by more than 4 standard deviations. We found that epileptic events could be identified and distinguished from a variety of ECoG phenomena associated with normal changes in behavior. We further found that analysis of spectral entropy was less effective in distinguishing epileptic from normal changes in ECoG activity. Conclusion: The software presented here was a successful modification of EEGLAB in the Matlab environment that allows detection of epileptiform ECoG signals in animals after TBI. The code allows import of large EEG or ECoG data records as standard text files and uses fast fourier transform as a basis for detection of abnormal events. The software can also be used to monitor injury-induced changes in spectral entropy if required. We hope that the software will be useful for other investigators in the field of traumatic brain injury and will stimulate future advances of quantitative analysis of brain electrical activity after neurological injury or disease. http://www.scfbm.org/content/8/1/12 Justin Sick Eric Bray Amade Bregy W Dietrich Helen Bramlett Thomas Sick Source Code for Biology and Medicine 2013, null:12 2013-05-21T00:00:00Z doi:10.1186/1751-0473-8-12 /content/figures/1751-0473-8-12-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 12 2013-05-21T00:00:00Z PDF Background: Processing and pattern recognition of myoelectric signals have been at the core of prosthetic control research in the last decade. Although most studies agree on reporting the accuracy of predicting predefined movements, there is a significant amount of study-dependent variables that make high-resolution inter-study comparison practically impossible. As an effort to provide a common research platform for the development and evaluation of algorithms in prosthetic control, we introduce BioPatRec as open source software. BioPatRec allows a seamless implementation of a variety of algorithms in the fields of 1) Signal processing; 2) Feature selection and extraction; 3) Pattern recognition; and, 4) Real-time control. Furthermore, since the platform is highly modular and customizable, researchers from different fields can seamlessly benchmark their algorithms by applying them in prosthetic control, without necessarily knowing how to obtain and process bioelectric signals, or how to produce and evaluate physically meaningful outputs. Results: BioPatRec is demonstrated in this study by the implementation of a relatively new pattern recognition algorithm, namely Regulatory Feedback Networks (RFN). RFN produced comparable results to those of more sophisticated classifiers such as Linear Discriminant Analysis and Multi-Layer Perceptron. BioPatRec is released with these 3 fundamentally different classifiers, as well as all the necessary routines for the myoelectric control of a virtual hand; from data acquisition to real-time evaluations.All the required instructions for use and development are provided in the online project hosting platform, which includes issue tracking and an extensive "wiki". This transparent implementation aims to facilitate collaboration and speed up utilization. Moreover, BioPatRec provides a publicly available repository of myoelectric signals that allow algorithms benchmarking on common data sets. This is particularly useful for researchers lacking of data acquisition hardware, or with limited access to patients. Conclusions: BioPatRec has been made openly and freely available with the hope to accelerate, through the community contributions, the development of better algorithms that can potentially improve the patient's quality of life. It is currently used in 3 different continents and by researchers of different disciplines, thus proving to be a useful tool for development and collaboration. http://www.scfbm.org/content/8/1/11 Max Ortiz-Catalan Rickard Brånemark Bo Håkansson Source Code for Biology and Medicine 2013, null:11 2013-04-18T00:00:00Z doi:10.1186/1751-0473-8-11 /content/figures/1751-0473-8-11-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 11 2013-04-18T00:00:00Z PDF Background Non-negative matrix factorization (NMF) has been introduced as an important method for mining biological data. Though there currently exists packages implemented in R and other programming languages, they either provide only a few optimization algorithms or focus on a specific application field. There does not exist a complete NMF package for the bioinformatics community, and in order to perform various data mining tasks on biological data.Results We provide a convenient MATLAB toolbox containing both the implementations of various NMF techniques and a variety of NMF-based data mining approaches for analyzing biological data. Data mining approaches implemented within the toolbox include data clustering and bi-clustering, feature extraction and selection, sample classification, missing values imputation, data visualization, and statistical comparison.Conclusions A series of analysis such as molecular pattern discovery, biological process identification, dimension reduction, disease prediction, visualization, and statistical comparison can be performed using this toolbox. http://www.scfbm.org/content/8/1/10 Yifeng Li Alioune Ngom Source Code for Biology and Medicine 2013, null:10 2013-04-16T00:00:00Z doi:10.1186/1751-0473-8-10 /content/figures/1751-0473-8-10-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 10 2013-04-16T00:00:00Z PDF Background: The annotation of surface exposed bacterial membrane proteins is an important step in interpretation and validation of proteomic experiments. In particular, proteins detected by cell surface protease shaving experiments can indicate exposed regions of membrane proteins that may contain antigenic determinants or constitute vaccine targets in pathogenic bacteria. Results: inmembrane is a tool to predict the membrane proteins with surface-exposed regions of polypeptide in sets of bacterial protein sequences. We have re-implemented a protocol for Gram-positive bacterial proteomes, and developed a new protocol for Gram-negative bacteria, which interface with multiple predictors of subcellular localization and membrane protein topology. Through the use of a modern scripting language, inmembrane provides an accessible code-base and extensible architecture that is amenable to modification for related sequence annotation tasks. Conclusions: inmembrane easily integrates predictions from both local binaries and web-based queries to help gain an overview of likely surface exposed protein in a bacterial proteome. The program is hosted on the Github repository http://github.com/boscoh/inmembrane. http://www.scfbm.org/content/8/1/9 Andrew Perry Bosco Ho Source Code for Biology and Medicine 2013, null:9 2013-03-19T00:00:00Z doi:10.1186/1751-0473-8-9 /content/figures/1751-0473-8-9-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 9 2013-03-19T00:00:00Z PDF Background: Surrogate variable analysis (SVA) is a powerful method to identify, estimate, and utilize the components of gene expression heterogeneity due to unknown and/or unmeasured technical, genetic, environmental, or demographic factors. These sources of heterogeneity are common in gene expression studies, and failing to incorporate them into the analysis can obscure results. Using SVA increases the biological accuracy and reproducibility of gene expression studies by identifying these sources of heterogeneity and correctly accounting for them in the analysis. Results: Here we have developed a web application called SVAw (Surrogate variable analysis Web app) that provides a user friendly interface for SVA analyses of genome-wide expression studies. The software has been developed based on open source bioconductor SVA package. In our software, we have extended the SVA program functionality in three aspects: (i) the SVAw performs a fully automated and user friendly analysis workflow; (ii) It calculates probe/gene Statistics for both pre and post SVA analysis and provides a table of results for the regression of gene expression on the primary variable of interest before and after correcting for surrogate variables; and (iii) it generates a comprehensive report file, including graphical comparison of the outcome for the user. Conclusions: SVAw is a web server freely accessible solution for the surrogate variant analysis of high-throughput datasets and facilitates removing all unwanted and unknown sources of variation. It is freely available for use at http://psychiatry.igm.jhmi.edu/sva. The executable packages for both web and standalone application and the instruction for installation can be downloaded from our web site. http://www.scfbm.org/content/8/1/8 Mehdi Pirooznia Fayaz Seifuddin Fernando Goes Jeffrey Leek Peter Zandi Source Code for Biology and Medicine 2013, null:8 2013-03-11T00:00:00Z doi:10.1186/1751-0473-8-8 /content/figures/1751-0473-8-8-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 8 2013-03-11T00:00:00Z XML Background: Reproducibility is the hallmark of good science. Maintaining a high degree of transparency in scientific reporting is essential not just for gaining trust and credibility within the scientific community but also for facilitating the development of new ideas. Sharing data and computer code associated with publications is becoming increasingly common, motivated partly in response to data deposition requirements from journals and mandates from funders. Despite this increase in transparency, it is still difficult to reproduce or build upon the findings of most scientific publications without access to a more complete workflow.FindingsVersion control systems (VCS), which have long been used to maintain code repositories in the software industry, are now finding new applications in science. One such open source VCS, Git, provides a lightweight yet robust framework that is ideal for managing the full suite of research outputs such as datasets, statistical code, figures, lab notes, and manuscripts. For individual researchers, Git provides a powerful way to track and compare versions, retrace errors, explore new approaches in a structured manner, while maintaining a full audit trail. For larger collaborative efforts, Git and Git hosting services make it possible for everyone to work asynchronously and merge their contributions at any time, all the while maintaining a complete authorship trail. In this paper I provide an overview of Git along with use-cases that highlight how this tool can be leveraged to make science more reproducible and transparent, foster new collaborations, and support novel uses. http://www.scfbm.org/content/8/1/7 Karthik Ram Source Code for Biology and Medicine 2013, null:7 2013-02-28T00:00:00Z doi:10.1186/1751-0473-8-7 /content/figures/1751-0473-8-7-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 7 2013-02-28T00:00:00Z XML Background: The whole-genome sequences of many non-model organisms have recently been determined. Using these genome sequences, next-generation sequencing based experiments such as RNA-seq and ChIP-seq have been performed and comparisons of the experiments between related species have provided new knowledge about evolution and biological processes. Although these comparisons require transformation of the genome coordinates of the reads between the species, current software tools are not suitable to convert the massive numbers of reads to the corresponding coordinates of other species’ genomes. Results: Here, we introduce a set of programs, called REad COordinate Transformer (RECOT), created to transform the coordinates of short reads obtained from the genome of a query species being studied to that of a comparison target species after aligning the query and target gene/genome sequences. RECOT generates output in SAM format that can be viewed using recent genome browsers capable of displaying next-generation sequencing data. Conclusions: We demonstrate the usefulness of RECOT in comparing ChIP-seq results between two closely-related fruit flies. The results indicate position changes of a transcription factor binding site caused sequence polymorphisms at the binding site. http://www.scfbm.org/content/8/1/6 Akiko Izawa Jun Sese Source Code for Biology and Medicine 2013, null:6 2013-02-26T00:00:00Z doi:10.1186/1751-0473-8-6 /content/figures/1751-0473-8-6-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 6 2013-02-26T00:00:00Z XML Background: In population association studies, standard methods of statistical inference assume that study subjects are independent samples. In genetic association studies, it is therefore of interest to diagnose undocumented close relationships in nominally unrelated study samples. Results: We describe the R package CrypticIBDcheck to identify pairs of closely-related subjects based on genetic marker data from single-nucleotide polymorphisms (SNPs). The package is able to accommodate SNPs in linkage disequibrium (LD), without the need to thin the markers so that they are approximately independent in the population. Sample pairs are identified by superposing their estimated identity-by-descent (IBD) coefficients on plots of IBD coefficients for pairs of simulated subjects from one of several common close relationships. Conclusions: The methods implemented in CrypticIBDcheck are particularly relevant to candidate-gene association studies, in which dependent SNPs cluster in a relatively small number of genes spread throughout the genome. The accommodation of LD allows the use of all available genetic data, a desirable property when working with a modest number of dependent SNPs within candidate genes. CrypticIBDcheck is available from the Comprehensive R Archive Network (CRAN). http://www.scfbm.org/content/8/1/5 Annick Nembot-Simo Jinko Graham Brad McNeney Source Code for Biology and Medicine 2013, null:5 2013-02-06T00:00:00Z doi:10.1186/1751-0473-8-5 /content/figures/1751-0473-8-5-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 5 2013-02-06T00:00:00Z XML Background: Large-scale sequence studies requiring BLAST-based analysis produce huge amounts of data to be parsed. BLAST parsers are available, but they are often missing some important features, such as keeping all information from the raw BLAST output, allowing direct access to single results, and performing logical operations over them.FindingsWe implemented BlaSTorage, a Python package that parses multi BLAST results and returns them in a purpose-built object-database format. Unlike other BLAST parsers, BlaSTorage retains and stores all parts of BLAST results, including alignments, without loss of information; a complete API allows access to all the data components. Conclusions: BlaSTorage shows comparable speed of more basic parser written in compiled languages as C++ and can be easily integrated into web applications or software pipelines. http://www.scfbm.org/content/8/1/4 Massimiliano Orsini Simone Carcangiu Source Code for Biology and Medicine 2013, null:4 2013-01-30T00:00:00Z doi:10.1186/1751-0473-8-4 /content/figures/1751-0473-8-4-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 4 2013-01-30T00:00:00Z XML Background: Data processing in the bioinformatics field often involves the handling of diverse software programs in one workflow. The field is lacking a set of standards for file formats so that files have to be processed in different ways in order to make them compatible to different analysis programs. The problem is that mass spectrometry vendors at most provide only closed-source Windows libraries to programmatically access their proprietary binary formats. This prohibits the creation of an efficient and unified tool that fits all processing needs of the users. Therefore, researchers are spending a significant amount of time using GUI-based conversion and processing programs. Besides the time needed for manual usage, such programs also can show long running times for processing, because most of them make use of only a single CPU. In particular, algorithms to enhance data quality, e.g. peak picking or deconvolution of spectra, add waiting time for the users. Results: To automate these processing tasks and let them run continuously without user interaction, we developed the FGCZ Converter Control (FCC) at the Functional Genomics Center Zurich (FGCZ) core facility. The FCC is a rule-based system for automated file processing that reduces the operation of diverse programs to a single configuration task. Using filtering rules for raw data files, the parameters for all tasks can be custom-tailored to the needs of every single researcher and processing can run automatically and efficiently on any number of servers in parallel using all available CPU resources. Conclusions: FCC has been used intensively at FGCZ for processing more than hundred thousand mass spectrometry raw files so far. Since we know that many other research facilities have similar problems, we would like to report on our tool and the accompanying ideas for an efficient set-up for potential reuse. http://www.scfbm.org/content/8/1/3 Simon Barkow-Oesterreicher Can Türker Christian Panse Source Code for Biology and Medicine 2013, null:3 2013-01-11T00:00:00Z doi:10.1186/1751-0473-8-3 /content/figures/1751-0473-8-3-toc.gif Source Code for Biology and Medicine 1751-0473 ${item.volume} 3 2013-01-11T00:00:00Z XML