The Gaggle: An open-source software system for integrating bioinformatics software and data sources

Background  

Systems biologists work with many kinds of data, from many different sources, using a variety of software tools. Each of these tools typically excels at one type of analysis, such as of microarrays, of metabolic networks and of predicted protein structure. A crucial challenge is to combine the capabilities of these (and other forthcoming) data resources and tools to create a data exploration and analysis environment that does justice to the variety and complexity of systems biology data sets. A solution to this problem should recognize that data types, formats and software in this high throughput age of biology are constantly changing.     

IOCBIO Kinetics: An open-source software solution for analysis of data traces

Biological measurements frequently involve measuring parameters as a function of time, space, or frequency. Later, during the analysis phase of the study, the researcher splits the recorded data trace into smaller sections, analyzes each section separately by finding a mean or fitting against a specified function, and uses the analysis results in the study. Here, we present the software that allows to analyze these data traces in a manner that ensures repeatability of the analysis and simplifies the application of FAIR (findability, accessibility, interoperability, and reusability) principles in such studies. At the same time, it simplifies the routine data analysis pipeline and gives access to a fast overview of the analysis results. For that, the software supports reading the raw data, processing the data as specified in the protocol, and storing all intermediate results in the laboratory database. The software can be extended by study- or hardware-specific modules to provide the required data import and analysis facilities. To simplify the development of the data entry web interfaces, that can be used to enter data describing the experiments, we released a web framework with an example implementation of such a site. The software is covered by open-source license and is available through several online channels.     

CRIMSON: An open-source software framework for cardiovascular integrated modelling and simulation

In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization.     

MapQuant: open-source software for large-scale protein quantification

Whole-cell protein quantification using MS has proven to be a challenging task. Detection efficiency varies significantly from peptide to peptide, molecular identities are not evident a priori, and peptides are dispersed unevenly throughout the multidimensional data space. To overcome these challenges we developed an open-source software package, MapQuant, to quantify comprehensively organic species detected in large MS datasets. MapQuant treats an LC/MS experiment as an image and utilizes standard image processing techniques to perform noise filtering, watershed segmentation, peak finding, peak fitting, peak clustering, charge-state determination and carbon-content estimation. MapQuant reports abundance values that respond linearly with the amount of sample analyzed on both low- and high-resolution instruments (over a 1000-fold dynamic range). Background noise added to a sample, either as a medium-complexity peptide mixture or as a high-complexity trypsinized proteome, exerts negligible effects on the abundance values reported by MapQuant and with coefficients of variance comparable to other methods. Finally, MapQuant's ability to define accurate mass and retention time features of isotopic clusters on a high-resolution mass spectrometer can increase protein sequence coverage by assigning sequence identities to observed isotopic clusters without corresponding MS/MS data.     

Automated sperm head morphology analyzer for open-source software

Sperm head morphology has been identified as a characteristic that can be used to predict a male's semen quality. In the present study, we have developed an automated sperm head morphology analysis (ASMA) plug-in for open-source ImageJ software (http://rsbweb.nih.gov/ij/). We describe the plug-in's functionality, and confirm its validity for sperm head morphology analysis using fish sperm. Sperm head morphological measurements (length and width) made with the ASMA plug-in did not differ from manual measurements. Using the plug-in to measure sperm head-shaped objects of known size, the associated plug-in error rate was < 0.5%. Brightness and contrast ratios influenced sperm head measurements, suggesting the need for standardized protocols. This plug-in was effective at measuring elliptical (i.e., Atlantic cod) as well as slightly irregular (i.e., Chinook salmon) shaped sperm heads. In conclusion, our ASMA plug-in represents a versatile alternative to costly sperm morphology software.     

Automated sperm head morphology analyzer for open-source software

Sperm head morphology has been identified as a characteristic that can be used to predict a male's semen quality. In the present study, we have developed an automated sperm head morphology analysis (ASMA) plug-in for open-source ImageJ software (http://rsbweb.nih.gov/ij/). We describe the plug-in's functionality, and confirm its validity for sperm head morphology analysis using fish sperm. Sperm head morphological measurements (length and width) made with the ASMA plug-in did not differ from manual measurements. Using the plug-in to measure sperm head-shaped objects of known size, the associated plug-in error rate was < 0.5%. Brightness and contrast ratios influenced sperm head measurements, suggesting the need for standardized protocols. This plug-in was effective at measuring elliptical (i.e., Atlantic cod) as well as slightly irregular (i.e., Chinook salmon) shaped sperm heads. In conclusion, our ASMA plug-in represents a versatile alternative to costly sperm morphology software.     

Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

Background

The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease.

Results

In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM.

Conclusion

Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.     

Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

Background  

Data generated from liquid chromatography coupled to high-resolution mass spectrometry (LC-MS)-based studies of a biological sample can contain large amounts of biologically significant information in the form of proteins, peptides, and metabolites. Interpreting this data involves inferring the masses and abundances of biomolecules injected into the instrument. Because of the inherent complexity of mass spectral patterns produced by these biomolecules, the analysis is significantly enhanced by using visualization capabilities to inspect and confirm results. In this paper we describe Decon2LS, an open-source software package for automated processing and visualization of high-resolution MS data. Drawing extensively on algorithms developed over the last ten years for ICR2LS, Decon2LS packages the algorithms as a rich set of modular, reusable processing classes for performing diverse functions such as reading raw data, routine peak finding, theoretical isotope distribution modelling, and deisotoping. Because the source code is openly available, these functionalities can now be used to build derivative applications in relatively fast manner. In addition, Decon2LS provides an extensive set of visualization tools, such as high performance chart controls.     

Affordable biocomputing for everyone: using the Internet,freeware and open-source software

How to build your own complete working biocomputing platform with nothing more than a desktop computer and an Internet connection.     

Llamanade: An open-source computational pipeline for robust nanobody humanization

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DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation

We present DeepMIB, a new software package that is capable of training convolutional neural networks for segmentation of multidimensional microscopy datasets on any workstation. We demonstrate its successful application for segmentation of 2D and 3D electron and multicolor light microscopy datasets with isotropic and anisotropic voxels. We distribute DeepMIB as both an open-source multi-platform Matlab code and as compiled standalone application for Windows, MacOS and Linux. It comes in a single package that is simple to install and use as it does not require knowledge of programming. DeepMIB is suitable for everyone interested of bringing a power of deep learning into own image segmentation workflows.     

IJ_Rhizo: an open-source software to measure scanned images of root samples

Background and aims

This paper provides an overview of the measuring capabilities of IJ_Rhizo, an ImageJ macro that measures scanned images of washed root samples. IJ_Rhizo is open-source, platform-independent and offers a simple graphic user interface (GUI) for a main audience of non-programmer scientists. Being open-source based, it is also fully modifiable to accommodate the specific needs of the more computer-literate users. A comparison of IJ_Rhizo’s performance with that of the widely used commercial package WinRHIZO? is discussed.

Methods

We compared IJ_Rhizo’s performance with that of the commercial package WinRHIZO? using two sets of images, one comprising test-line images, the second consisting of images of root samples collected in the field. IJ_Rhizo and WinRHIZO? estimates were compared by means of correlation and regression analysis.

Results

IJ_Rhizo “Kimura” and WinRHIZO? “Tennant” were the length estimates that were best linearly correlated with each other. Correlation between average root diameter estimates was weaker, due to the sensitivity of this parameter to thresholding and filtering of image background noise.

Conclusions

Overall, IJ_Rhizo offers new opportunities for researchers who cannot afford the cost of commercial software packages to carry out automated measurement of scanned images of root samples, without sacrificing accuracy.     

Automatic preparation, calibration, and simulation of deformable objects

Many simulation environments - particularly those intended for medical simulation - require solid objects to deform at interactive rates, with deformation properties that correspond to real materials. Furthermore, new objects may be created frequently (for example, each time a new patient's data is processed), prohibiting manual intervention in the model preparation process. This paper provides a pipeline for rapid preparation of deformable objects with no manual intervention, specifically focusing on mesh generation (preparing solid meshes from surface models), automated calibration of models to finite element reference analyses (including a novel approach to reducing the complexity of calibrating nonhomogeneous objects), and automated skinning of meshes for interactive simulation.     

Automatic preparation,calibration, and simulation of deformable objects

Many simulation environments – particularly those intended for medical simulation – require solid objects to deform at interactive rates, with deformation properties that correspond to real materials. Furthermore, new objects may be created frequently (for example, each time a new patient's data is processed), prohibiting manual intervention in the model preparation process. This paper provides a pipeline for rapid preparation of deformable objects with no manual intervention, specifically focusing on mesh generation (preparing solid meshes from surface models), automated calibration of models to finite element reference analyses (including a novel approach to reducing the complexity of calibrating nonhomogeneous objects), and automated skinning of meshes for interactive simulation.     

Commissioning and validation of commercial deformable image registration software for adaptive contouring

PurposeTo report the commissioning and validation of deformable image registration(DIR) software for adaptive contouring.MethodsDIR (SmartAdapt®v13.6) was validated using two methods namely contour propagation accuracy and landmark tracking, using physical phantoms and clinical images of various disease sites. Five in-house made phantoms with various known deformations and a set of 10 virtual phantoms were used. Displacement in lateral, anterio-posterior (AP) and superior-inferior (SI) direction were evaluated for various organs and compared with the ground truth. Four clinical sites namely, brain (n = 5), HN (n = 9), cervix (n = 18) and prostate (n = 23) were used. Organs were manually delineated by a radiation oncologist, compared with the deformable image registration (DIR) generated contours. 3D slicer v4.5.0.1 was used to analyze Dice Similarity Co-efficient (DSC), shift in centre of mass (COM) and Hausdorff distances Hf_95%/avg.ResultsMean (SD) DSC, Hf_95% (mm), Hf_avg (mm) and COM of all the phantoms 1–5 were 0.84 (0.2) mm, 5.1 (7.4) mm, 1.6 (2.2) mm, and 1.6 (0.2) mm respectively. Phantom-5 had the largest deformation as compared to phantoms 1–4, and hence had suboptimal indices. The virtual phantom resulted in consistent results for all the ROIs investigated. Contours propagated for brain patients were better with a high DSC score (0.91 (0.04)) as compared to other sites (HN: 0.84, prostate: 0.81 and cervix 0.77). A similar trend was seen in other indices too. The accuracy of propagated contours is limited for complex deformations that include large volume and shape change of bladder and rectum respectively. Visual validation of the propagated contours is recommended for clinical implementation.ConclusionThe DIR algorithm was commissioned and validated for adaptive contouring.