scholarly journals FastTrack: An open-source software for tracking varying numbers of deformable objects

2021 ◽  
Vol 17 (2) ◽  
pp. e1008697
Author(s):  
Benjamin Gallois ◽  
Raphaël Candelier
Keyword(s):  
Open Source ◽  
Cell Tracking ◽  
Ad Hoc ◽  
Ground Truth ◽  
General Purpose ◽  
Two Dimensions ◽  
Deformable Objects ◽  
Tracking Accuracy ◽  
Link Type ◽  
Wide Range

Analyzing the dynamical properties of mobile objects requires to extract trajectories from recordings, which is often done by tracking movies. We compiled a database of two-dimensional movies for very different biological and physical systems spanning a wide range of length scales and developed a general-purpose, optimized, open-source, cross-platform, easy to install and use, self-updating software called FastTrack. It can handle a changing number of deformable objects in a region of interest, and is particularly suitable for animal and cell tracking in two-dimensions. Furthermore, we introduce the probability of incursions as a new measure of a movie’s trackability that doesn’t require the knowledge of ground truth trajectories, since it is resilient to small amounts of errors and can be computed on the basis of an ad hoc tracking. We also leveraged the versatility and speed of FastTrack to implement an iterative algorithm determining a set of nearly-optimized tracking parameters—yet further reducing the amount of human intervention—and demonstrate that FastTrack can be used to explore the space of tracking parameters to optimize the number of swaps for a batch of similar movies. A benchmark shows that FastTrack is orders of magnitude faster than state-of-the-art tracking algorithms, with a comparable tracking accuracy. The source code is available under the GNU GPLv3 at https://github.com/FastTrackOrg/FastTrack and pre-compiled binaries for Windows, Mac and Linux are available at http://www.fasttrack.sh.

scholarly journals Dashing: fast and accurate genomic distances with HyperLogLog

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Daniel N. Baker ◽  
Ben Langmead
Keyword(s):  
Open Source ◽  
Software Tool ◽  
Estimation Methods ◽  
Cardinality Estimation ◽  
Link Type ◽  
Wide Range

AbstractDashing is a fast and accurate software tool for estimating similarities of genomes or sequencing datasets. It uses the HyperLogLog sketch together with cardinality estimation methods that are specialized for set unions and intersections. Dashing summarizes genomes more rapidly than previous MinHash-based methods while providing greater accuracy across a wide range of input sizes and sketch sizes. It can sketch and calculate pairwise distances for over 87K genomes in 6 minutes. Dashing is open source and available at https://github.com/dnbaker/dashing.

scholarly journals NeuroPycon: An open-source Python toolbox for fast multi-modal and reproducible brain connectivity pipelines

2019 ◽  
Author(s):  
David Meunier ◽  
Annalisa Pascarella ◽  
Dmitrii Altukhov ◽  
Mainak Jas ◽  
Etienne Combrisson ◽  
...  
Keyword(s):  
Open Source ◽  
Brain Connectivity ◽  
Reproducible Research ◽  
Link Type ◽  
Large Sets ◽  
Current Implementation ◽  
Wide Range ◽  
Neuroimaging Software ◽  
Brain Data ◽  
Automatic Removal

AbstractRecent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html. and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.

EFFECTIVE GPU ACCELERATION OF LARGE SCALE, ASYNCHRONOUS SIMULATIONS ON GRAPHS

2012 ◽  
Vol 15 (08) ◽  
pp. 1250035
Author(s):  
DUSTIN ARENDT ◽  
YANG CAO
Keyword(s):  
Stochastic Modeling ◽  
Large Scale ◽  
Ad Hoc ◽  
General Purpose ◽  
Gpu Acceleration ◽  
Simulation Framework ◽  
Good Balance ◽  
Recent Emergence ◽  
Wide Range ◽  
Gpu Implementation

The recent emergence of GPGPU programming has resulted in a number of very efficient, but ultimately ad-hoc implementations of GPU accelerated simulations of complex systems. Because developing applications for the GPU is still a difficult and time consuming task, efficient GPU parallelizations of general purpose modeling frameworks are very useful. The dimer automaton is a stochastic modeling and simulation framework with a good balance of robustness, generality, and simplicity with capacity to model a wide range of phenomena. A major advantage of dimer automata is the ease in which they can be applied to any space that can be represented as a graph. Therefore, we have developed an efficient GPU implementation of dimer automata that runs up to 80 times faster than the serial implementation.

scholarly journals Dashing: Fast and Accurate Genomic Distances with HyperLogLog

2018 ◽  
Author(s):  
Daniel N Baker ◽  
Ben Langmead
Keyword(s):  
Open Source ◽  
Software Tool ◽  
Estimation Methods ◽  
Cardinality Estimation ◽  
Link Type ◽  
Wide Range

AbstractDashing is a fast and accurate software tool for estimating similarities of genomes or sequencing datasets. It uses the HyperLogLog sketch together with cardinality estimation methods that are specialized for set unions and intersections. Dashing summarizes genomes more rapidly than previous MinHash-based methods while providing greater accuracy across a wide range of input sizes and sketch sizes. It can sketch and calculate pairwise distances for over 87K genomes in 6 minutes. Dashing is open source and available at https://github.com/dnbaker/dashing.

scholarly journals SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research

2017 ◽  
Author(s):  
Honghan Wu ◽  
Giulia Toti ◽  
Katherine I. Morley ◽  
Zina M. Ibrahim ◽  
Amos Folarin ◽  
...  
Keyword(s):  
Information Extraction ◽  
Open Source ◽  
Language Processing ◽  
Semantic Search ◽  
Trial Recruitment ◽  
College Hospital ◽  
Link Type ◽  
Semantic Data ◽  
Wide Range ◽  
The Uk

ABSTRACTObjectiveUnlocking the data contained within both structured and unstructured components of Electronic Health Records (EHRs) has the potential to provide a step change in data available forsecondary research use, generation of actionable medical insights, hospital management and trial recruitment. To achieve this, we implemented SemEHR - a semantic search and analytics, open source tool for EHRs.MethodsSemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualised mentions of a wide range of biomedical concepts within EHRs. Natural Language Processing (NLP) annotations are further assembled at patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data is serviced via ontology-based search and analytics interfaces.ResultsSemEHR has been deployed to a number of UK hospitals including the Clinical Record Interactive Search (CRIS), an anonymised replica of the EHR of the UK South London and Maudsley (SLaM) NHS Foundation Trust, one of Europes largest providers of mental health services. In two CRIS-based studies, SemEHR achieved 93% (Hepatitis C case) and 99% (HIV case) F-Measure results in identifying true positive patients. At King’s College Hospital in London, as part of the CogStack programme (github.com/cogstack), SemEHR is being used to recruit patients into the UK Dept of Health 100k Genome Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast in searching phenotypes - time for recruitment criteria checking reduced from days to minutes. Validated on an open intensive care EHR data - MIMICIII, the vital signs extracted by SemEHR can achieve around 97% accuracy.ConclusionResults from the multiple case studies demonstrate SemEHR’s efficiency - weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of a patient, bringing in more and unexpected insight compared to study-oriented bespoke information extraction systems.SemEHR is open source available at https://github.com/CogStack/SemEHR.

scholarly journals Human mitochondrial variant annotation with HmtNote

2019 ◽  
Author(s):  
R. Preste ◽  
R. Clima ◽  
M. Attimonelli
Keyword(s):  
Open Source ◽  
Online Resources ◽  
Annotation Database ◽  
Variant Annotation ◽  
Internet Connection ◽  
Link Type ◽  
Wide Range ◽  
Using Data ◽  
Cross Reference ◽  
Python Package

AbstractHmtNote is a Python package to annotate human mitochondrial variants from VCF files.Variants are annotated using a wide range of information, which are grouped into basic, cross-reference, variability and prediction subsets so that users can either select specific annotations of interest or use them altogether.Annotations are performed using data from HmtVar, a recently published database of human mitochondrial variations, which collects information from several online resources as well as offering in-house pathogenicity predictions.HmtNote also allows users to download a local annotation database, that can be used to annotate variants offline, without having to rely on an internet connection.HmtNote is a free and open source package, and can be downloaded and installed from PyPI (https://pypi.org/project/hmtnote) or GitHub (https://github.com/robertopreste/HmtNote).

scholarly journals FRETboard: semi-supervised classification of fret traces

2020 ◽  
Author(s):  
Carlos de Lannoy ◽  
Mike Filius ◽  
Sung Hyun Kim ◽  
Chirlmin Joo ◽  
Dick de Ridder
Keyword(s):  
Single Molecule ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Ground Truth ◽  
Optical Signals ◽  
Link Type ◽  
Wide Range ◽  
Classification Tool

AbstractFörster resonance energy transfer (FRET) is a useful phenomenon in biomolecular investigations, as it can be leveraged for nano-scale measurements. The optical signals produced by such experiments can be analyzed by fitting a statistical model. Several software tools exist to fit such models in an unsupervised manner, but their operating system-dependent installation requirements and lack of flexibility impede wide-spread adoption. Here we propose to fit such models more efficiently and intuitively by adopting a semi-supervised approach, in which the user interactively guides the model to fit a given dataset, and introduce FRETboard, a web tool that allows users to provide such guidance. We show that our approach is able to closely reproduce ground truth FRET statistics in a wide range of simulated single-molecule scenarios, and correctly estimate parameters for up to eleven states. On in vitro data we retrieve parameters identical to those obtained by laborious manual classification in a fraction of the required time. Moreover, we designed FRETboard to be easily extendable to other models, allowing it to adapt to future developments in FRET measurement and analysis.Availabilitysource code is available at https://github.com/cvdelannoy/FRETboard. The FRETboard classification tool is also available as a browser application at https://www.bioinformatics.nl/FRETboard.

scholarly journals A FREE AND OPEN SOURCE TOOL TO ASSESS THE ACCURACY OF LAND COVER MAPS: IMPLEMENTATION AND APPLICATION TO LOMBARDY REGION (ITALY)

2018 ◽  
Vol XLII-3 ◽  
pp. 87-92 ◽  
Author(s):  
G. Bratic ◽  
M. A. Brovelli ◽  
M. E. Molinari
Keyword(s):  
Open Source ◽  
Ad Hoc ◽  
Confusion Matrix ◽  
Ground Truth ◽  
Lombardy Region ◽  
Promising Tool ◽  
The Matrix ◽  
Key Factor ◽  
Gis Environment ◽  
Land Cover Maps

The availability of thematic maps has significantly increased over the last few years. Validation of these maps is a key factor in assessing their suitability for different applications. The evaluation of the accuracy of classified data is carried out through a comparison with a reference dataset and the generation of a confusion matrix from which many quality indexes can be derived. In this work, an ad hoc free and open source Python tool was implemented to automatically compute all the matrix confusion-derived accuracy indexes proposed by literature. The tool was integrated into GRASS GIS environment and successfully applied to evaluate the quality of three high-resolution global datasets (GlobeLand30, Global Urban Footprint, Global Human Settlement Layer Built-Up Grid) in the Lombardy Region area (Italy). In addition to the most commonly used accuracy measures, e.g. overall accuracy and Kappa, the tool allowed to compute and investigate less known indexes such as the Ground Truth and the Classification Success Index. The promising tool will be further extended with spatial autocorrelation analysis functions and made available to researcher and user community.

scholarly journals simuG: a general-purpose genome simulator

2018 ◽  
Author(s):  
Jia-Xing Yue ◽  
Gianni Liti
Keyword(s):  
General Purpose ◽  
Bioinformatics Analyses ◽  
Full Spectrum ◽  
Genomic Variants ◽  
Link Type ◽  
Wide Range ◽  
Simulation Based ◽  
Testing Data

AbstractSummarySimulated genomes with pre-defined and random genomic variants can be very useful for benchmarking genomic and bioinformatics analyses. Here we introduce simuG, a light-weighted tool for simulating the full-spectrum of genomic variants. The simplicity and versatility of simuG makes it a unique general purpose genome simulator for a wide-range of simulation-based applications.Availability and implementationCode in Perl along with user manual and testing data is available at https://github.com/yjx1217/simuG. This software is free for use under the MIT license.

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