Source Code for Biology and Medicine最新文献

{"title":"Superplot3d: an open source GUI tool for 3d trajectory visualisation and elementary processing.","authors":"Luke J Whitehorn,&nbsp;Frances M Hawkes,&nbsp;Ian An Dublon","doi":"10.1186/1751-0473-8-19","DOIUrl":"https://doi.org/10.1186/1751-0473-8-19","url":null,"abstract":"<p><p>When acquiring simple three-dimensional (3d) trajectory data it is common to accumulate large coordinate data sets. In order to examine integrity and consistency of object tracking, it is often necessary to rapidly visualise these data. Ordinarily, to achieve this the user must either execute 3d plotting functions in a numerical computing environment or manually inspect data in two dimensions, plotting each individual axis.Superplot3d is an open source MATLAB script which takes tab delineated Cartesian data points in the form x, y, z and time and generates an instant visualization of the object's trajectory in free-rotational three dimensions. Whole trajectories may be instantly presented, allowing for rapid inspection. Executable from the MATLAB command line (or deployable as a compiled standalone application) superplot3d also provides simple GUI controls to obtain rudimentary trajectory information, allow specific visualization of trajectory sections and perform elementary processing.Superplot3d thus provides a framework for non-programmers and programmers alike, to recreate recently acquired 3d object trajectories in rotatable 3d space. It is intended, via the use of a preference driven menu to be flexible and work with output from multiple tracking software systems. Source code and accompanying GUIDE .fig files are provided for deployment and further development. </p>","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":"8 1","pages":"19"},"PeriodicalIF":0.0,"publicationDate":"2013-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1751-0473-8-19","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31769279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments.","authors":"Yuri Matsuzaki,&nbsp;Nobuyuki Uchikoga,&nbsp;Masahito Ohue,&nbsp;Takehiro Shimoda,&nbsp;Toshiyuki Sato,&nbsp;Takashi Ishida,&nbsp;Yutaka Akiyama","doi":"10.1186/1751-0473-8-18","DOIUrl":"https://doi.org/10.1186/1751-0473-8-18","url":null,"abstract":"<p><strong>Background: </strong>Protein-protein interaction (PPI) plays a core role in cellular functions. Massively parallel supercomputing systems have been actively developed over the past few years, which enable large-scale biological problems to be solved, such as PPI network prediction based on tertiary structures.</p><p><strong>Results: </strong>We have developed a high throughput and ultra-fast PPI prediction system based on rigid docking, \"MEGADOCK\", by employing a hybrid parallelization (MPI/OpenMP) technique assuming usages on massively parallel supercomputing systems. MEGADOCK displays significantly faster processing speed in the rigid-body docking process that leads to full utilization of protein tertiary structural data for large-scale and network-level problems in systems biology. Moreover, the system was scalable as shown by measurements carried out on two supercomputing environments. We then conducted prediction of biological PPI networks using the post-docking analysis.</p><p><strong>Conclusions: </strong>We present a new protein-protein docking engine aimed at exhaustive docking of mega-order numbers of protein pairs. The system was shown to be scalable by running on thousands of nodes. The software package is available at: http://www.bi.cs.titech.ac.jp/megadock/k/.</p>","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":"8 1","pages":"18"},"PeriodicalIF":0.0,"publicationDate":"2013-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1751-0473-8-18","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31707647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GEMBASSY: an EMBOSS associated software package for comprehensive genome analyses.","authors":"Hidetoshi Itaya,&nbsp;Kazuki Oshita,&nbsp;Kazuharu Arakawa,&nbsp;Masaru Tomita","doi":"10.1186/1751-0473-8-17","DOIUrl":"https://doi.org/10.1186/1751-0473-8-17","url":null,"abstract":"<p><p>The popular European Molecular Biology Open Software Suite (EMBOSS) currently contains over 400 tools used in various bioinformatics researches, equipped with sophisticated development frameworks for interoperability and tool discoverability as well as rich documentations and various user interfaces. In order to further strengthen EMBOSS in the fields of genomics, we here present a novel EMBOSS associated software (EMBASSY) package named GEMBASSY, which adds more than 50 analysis tools from the G-language Genome Analysis Environment and its Representational State Transfer (REST) and SOAP web services. GEMBASSY basically contains wrapper programs of G-language REST/SOAP web services to provide intuitive and easy access to various annotations within complete genome flatfiles, as well as tools for analyzing nucleic composition, calculating codon usage, and visualizing genomic information. For example, analysis methods such as for calculating distance between sequences by genomic signatures and for predicting gene expression levels from codon usage bias are effective in the interpretation of meta-genomic and meta-transcriptomic data. GEMBASSY tools can be used seamlessly with other EMBOSS tools and UNIX command line tools. The source code written in C is available from GitHub (https://github.com/celery-kotone/GEMBASSY/) and the distribution package is freely available from the GEMBASSY web site (http://www.g-language.org/gembassy/). </p>","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":"8 1","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2013-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1751-0473-8-17","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31691917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation.","authors":"Erlend Hodneland, Tanja Kögel, Dominik Michael Frei, Hans-Hermann Gerdes, Arvid Lundervold","doi":"10.1186/1751-0473-8-16","DOIUrl":"10.1186/1751-0473-8-16","url":null,"abstract":"<p><p>: The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening. </p>","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":"8 1","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3850890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31651590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing a new EPR lineshape parameter for organic radicals in carbonaceous matter.","authors":"Mathilde Bourbin,&nbsp;Yann Le Du,&nbsp;Laurent Binet,&nbsp;Didier Gourier","doi":"10.1186/1751-0473-8-15","DOIUrl":"https://doi.org/10.1186/1751-0473-8-15","url":null,"abstract":"<p><strong>Background: </strong>Electron Paramagnetic Resonance (EPR) is a non-destructive, non-invasive technique useful for the characterization of organic moieties in primitive carbonaceous matter related to the origin of life. The classical EPR parameters are the peak-to-peak amplitude, the linewidth and the g factor; however, such parameters turn out not to suffice to fully determine a single EPR line.</p><p><strong>Results: </strong>In this paper, we give the definition and practical implementation of a new EPR parameter based on the signal shape that we call the R10 factor. This parameter was originally defined in the case of a single symmetric EPR line and used as a new datation method for organic matter in the field of exobiology.</p><p><strong>Conclusion: </strong>Combined to classical EPR parameters, the proposed shape parameter provides a full description of an EPR spectrum and opens the way to novel applications like datation. Such a parameter is a powerful tool for future EPR studies, not only of carbonaceous matter, but also of any substance which spectrum exhibits a single symmetric line.</p><p><strong>Reproducibility: </strong>The paper is a literate program-written using Noweb within the Org-mode as provided by the Emacs editor- and it also describes the full data analysis pipeline that computes the R10 on a real EPR spectrum.</p>","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":"8 1","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2013-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1751-0473-8-15","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31590740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GenePattern flow cytometry suite.","authors":"Josef Spidlen,&nbsp;Aaron Barsky,&nbsp;Karin Breuer,&nbsp;Peter Carr,&nbsp;Marc-Danie Nazaire,&nbsp;Barbara Allen Hill,&nbsp;Yu Qian,&nbsp;Ted Liefeld,&nbsp;Michael Reich,&nbsp;Jill P Mesirov,&nbsp;Peter Wilkinson,&nbsp;Richard H Scheuermann,&nbsp;Rafick-Pierre Sekaly,&nbsp;Ryan R Brinkman","doi":"10.1186/1751-0473-8-14","DOIUrl":"10.1186/1751-0473-8-14","url":null,"abstract":"<p><strong>Background: </strong>Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research.</p><p><strong>Results: </strong>In order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines.</p><p><strong>Conclusions: </strong>GenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser.</p>","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":"8 1","pages":"14"},"PeriodicalIF":0.0,"publicationDate":"2013-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1751-0473-8-14","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31553127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crowdsourcing the corpasome.","authors":"Manuel Corpas","doi":"10.1186/1751-0473-8-13","DOIUrl":"https://doi.org/10.1186/1751-0473-8-13","url":null,"abstract":"<p><p>The suffix -ome conveys \"comprehensiveness\" in some way. The idea of the Corpasome started half-jokingly, acknowledging the efforts to sequence five members of my family. After the unexpected response from many scientists from around the world, it has become clear how useful this approach could be for understanding the genomic information contained in our personal genomics tests. </p>","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":"8 1","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"2013-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1751-0473-8-13","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31532787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"biobambam: tools for read pair collation based algorithms on BAM files","authors":"German Tischler, Steven Leonard","doi":"10.1186/1751-0473-9-13","DOIUrl":"https://doi.org/10.1186/1751-0473-9-13","url":null,"abstract":"","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":"9 1","pages":"13 - 13"},"PeriodicalIF":0.0,"publicationDate":"2013-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1751-0473-9-13","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65725210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEGgui: a program used to detect electroencephalogram anomalies after traumatic brain injury.","authors":"Justin Sick,&nbsp;Eric Bray,&nbsp;Amade Bregy,&nbsp;W Dalton Dietrich,&nbsp;Helen M Bramlett,&nbsp;Thomas Sick","doi":"10.1186/1751-0473-8-12","DOIUrl":"https://doi.org/10.1186/1751-0473-8-12","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>Software was developed using MATLAB(™) 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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":"8 1","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1751-0473-8-12","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31446781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms.","authors":"Max Ortiz-Catalan,&nbsp;Rickard Brånemark,&nbsp;Bo Håkansson","doi":"10.1186/1751-0473-8-11","DOIUrl":"https://doi.org/10.1186/1751-0473-8-11","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":"8 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2013-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1751-0473-8-11","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31367433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}