scholarly journals MLDSP-GUI: an alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis

2019 ◽  
Vol 36 (7) ◽  
pp. 2258-2259 ◽  
Author(s):  
Gurjit S Randhawa ◽  
Kathleen A Hill ◽  
Lila Kari
Keyword(s):  
User Interface ◽  
Open Source ◽  
Graphical User Interface ◽  
Dna Sequences ◽  
Software Tool ◽  
Digital Signal ◽  
Disease Classification ◽  
Supplementary Information ◽  
Alignment Free ◽  
Comparison And Analysis

Abstract Summary Machine Learning with Digital Signal Processing and Graphical User Interface (MLDSP-GUI) is an open-source, alignment-free, ultrafast, computationally lightweight, and standalone software tool with an interactive GUI for comparison and analysis of DNA sequences. MLDSP-GUI is a general-purpose tool that can be used for a variety of applications such as taxonomic classification, disease classification, virus subtype classification, evolutionary analyses, among others. Availability and implementation MLDSP-GUI is open-source, cross-platform compatible, and is available under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/). The executable and dataset files are available at https://sourceforge.net/projects/mldsp-gui/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MLDSP-GUI: An alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis

2019 ◽  
Author(s):  
Gurjit S. Randhawa ◽  
Kathleen A. Hill ◽  
Lila Kari
Keyword(s):  
User Interface ◽  
Open Source ◽  
Graphical User Interface ◽  
Dna Sequences ◽  
Disease Classification ◽  
Supplementary Information ◽  
Link Type ◽  
Alignment Free ◽  
Comparison And Analysis ◽  
Interactive Graphical User Interface

AbstractSummaryMLDSP-GUI (Machine Learning with Digital Signal Processing) is an open-source, alignment-free, ultrafast, computationally lightweight, standalone software tool with an interactive Graphical User Interface (GUI) for comparison and analysis of DNA sequences. MLDSP-GUI is a general-purpose tool that can be used for a variety of applications such as taxonomic classification, disease classification, virus subtype classification, evolutionary analyses, among others.AvailabilityMLDSP-GUI is open-source, cross-platform compatible, and is available under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/). The executable and dataset files are available at https://sourceforge.net/projects/mldsp-gui/[email protected] informationSupplementary data are available online.

scholarly journals The DNA walk and its demonstration of deterministic chaos—relevance to genomic alterations in lung cancer

2019 ◽  
Vol 35 (16) ◽  
pp. 2738-2748 ◽  
Author(s):  
Blake Hewelt ◽  
Haiqing Li ◽  
Mohit Kumar Jolly ◽  
Prakash Kulkarni ◽  
Isa Mambetsariev ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Open Source ◽  
Fractal Analysis ◽  
Dna Sequences ◽  
Chaotic Behavior ◽  
Supplementary Information ◽  
Wild Type ◽  
Genomic Alterations ◽  
Turtle Graphics ◽  
Dna Walk

AbstractMotivationAdvancements in cancer genetics have facilitated the development of therapies with actionable mutations. Although mutated genes have been studied extensively, their chaotic behavior has not been appreciated. Thus, in contrast to naïve DNA, mutated DNA sequences can display characteristics of unpredictability and sensitivity to the initial conditions that may be dictated by the environment, expression patterns and presence of other genomic alterations. Employing a DNA walk as a form of 2D analysis of the nucleotide sequence, we demonstrate that chaotic behavior in the sequence of a mutated gene can be predicted.ResultsUsing fractal analysis for these DNA walks, we have determined the complexity and nucleotide variance of commonly observed mutated genes in non-small cell lung cancer, and their wild-type counterparts. DNA walks for wild-type genes demonstrate varying levels of chaos, with BRAF, NTRK1 and MET exhibiting greater levels of chaos than KRAS, paxillin and EGFR. Analyzing changes in chaotic properties, such as changes in periodicity and linearity, reveal that while deletion mutations indicate a notable disruption in fractal ‘self-similarity’, fusion mutations demonstrate bifurcations between the two genes. Our results suggest that the fractals generated by DNA walks can yield important insights into potential consequences of these mutated genes.Availability and implementationIntroduction to Turtle graphics in Python is an open source article on learning to develop a script for Turtle graphics in Python, freely available on the web at https://docs.python.org/2/library/turtle.html. cDNA sequences were obtained through NCBI RefSeq database, an open source database that contains information on a large array of genes, such as their nucleotide and amino acid sequences, freely available at https://www.ncbi.nlm.nih.gov/refseq/. FracLac plugin for Fractal analysis in ImageJ is an open source plugin for the ImageJ program to perform fractal analysis, free to download at https://imagej.nih.gov/ij/plugins/fraclac/FLHelp/Introduction.html.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals A Software Tool for Assisting Experimentation in Dynamic Environments

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Pavel Novoa-Hernández ◽  
Carlos Cruz Corona ◽  
David A. Pelta
Keyword(s):  
Statistical Analysis ◽  
User Interface ◽  
Graphical User Interface ◽  
Optimization Problems ◽  
Software Tool ◽  
Dynamic Environments ◽  
Experiment Management ◽  
And Performance ◽  
Main Components

In real world, many optimization problems are dynamic, which means that their model elements vary with time. These problems have received increasing attention over time, especially from the viewpoint of metaheuristics methods. In this context, experimentation is a crucial task because of the stochastic nature of both algorithms and problems. Currently, there are several technologies whose methods, problems, and performance measures can be implemented. However, in most of them, certain features that make the experimentation process easy are not present. Examples of such features are the statistical analysis of the results and a graphical user interface (GUI) that allows an easy management of the experimentation process. Bearing in mind these limitations, in the present work, we present DynOptLab, a software tool for experimental analysis in dynamic environments. DynOptLab has two main components: (1) an object-oriented framework to facilitate the implementation of new proposals and (2) a graphical user interface for the experiment management and the statistical analysis of the results. With the aim of verifying the benefits of DynOptLab’s main features, a typical case study on experimentation in dynamic environments was carried out.

CellTracker (not only) for dummies

2015 ◽  
Vol 32 (6) ◽  
pp. 955-957 ◽  
Author(s):  
Filippo Piccinini ◽  
Alexa Kiss ◽  
Peter Horvath
Keyword(s):  
Graphical User Interface ◽  
Open Source Software ◽  
Phase Contrast ◽  
Cell Tracking ◽  
Source Code ◽  
Software Tool ◽  
Time Lapse ◽  
Supplementary Information ◽  
Differential Interference Contrast ◽  
User Friendly

Abstract Motivation: Time-lapse experiments play a key role in studying the dynamic behavior of cells. Single-cell tracking is one of the fundamental tools for such analyses. The vast majority of the recently introduced cell tracking methods are limited to fluorescently labeled cells. An equally important limitation is that most software cannot be effectively used by biologists without reasonable expertise in image processing. Here we present CellTracker, a user-friendly open-source software tool for tracking cells imaged with various imaging modalities, including fluorescent, phase contrast and differential interference contrast (DIC) techniques. Availability and implementation: CellTracker is written in MATLAB (The MathWorks, Inc., USA). It works with Windows, Macintosh and UNIX-based systems. Source code and graphical user interface (GUI) are freely available at: http://celltracker.website/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals CRAFT: Compact genome Representation towards large-scale Alignment-Free daTabase

2020 ◽  
Author(s):  
Yang Young Lu ◽  
Jiaxing Bai ◽  
Yiwen Wang ◽  
Ying Wang ◽  
Fengzhu Sun
Keyword(s):  
Dna Sequences ◽  
Sequence Comparison ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Practical Interest ◽  
Supplementary Information ◽  
Computationally Efficient ◽  
Sequencing Technologies ◽  
Alignment Free

AbstractMotivationRapid developments in sequencing technologies have boosted generating high volumes of sequence data. To archive and analyze those data, one primary step is sequence comparison. Alignment-free sequence comparison based on k-mer frequencies offers a computationally efficient solution, yet in practice, the k-mer frequency vectors for large k of practical interest lead to excessive memory and storage consumption.ResultsWe report CRAFT, a general genomic/metagenomic search engine to learn compact representations of sequences and perform fast comparison between DNA sequences. Specifically, given genome or high throughput sequencing (HTS) data as input, CRAFT maps the data into a much smaller embedding space and locates the best matching genome in the archived massive sequence repositories. With 102 – 104-fold reduction of storage space, CRAFT performs fast query for gigabytes of data within seconds or minutes, achieving comparable performance as six state-of-the-art alignment-free measures.AvailabilityCRAFT offers a user-friendly graphical user interface with one-click installation on Windows and Linux operating systems, freely available at https://github.com/jiaxingbai/[email protected]; [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals AMi: a GUI-based, open-source system for imaging samples in multi-well plates

2019 ◽  
Vol 75 (8) ◽  
pp. 531-536 ◽  
Author(s):  
Andrew Bohm
Keyword(s):  
User Interface ◽  
Open Source ◽  
Real Time ◽  
Graphical User Interface ◽  
Field Enhancement ◽  
Depth Of Field ◽  
Raspberry Pi ◽  
Real Time Imaging ◽  
Translation Stage ◽  
Multiple Sample

Described here are instructions for building and using an inexpensive automated microscope (AMi) that has been specifically designed for viewing and imaging the contents of multi-well plates. The X, Y, Z translation stage is controlled through dedicated software (AMiGUI) that is being made freely available. Movements are controlled by an Arduino-based board running grbl, and the graphical user interface and image acquisition are controlled via a Raspberry Pi microcomputer running Python. Images can be written to the Raspberry Pi or to a remote disk. Plates with multiple sample wells at each row/column position are supported, and a script file for automated z-stack depth-of-field enhancement is written along with the images. The graphical user interface and real-time imaging also make it easy to manually inspect and capture images of individual samples.

scholarly journals The SoilExp software: An open-source Graphical User Interface (GUI) for post-processing spatial and temporal soil surveys

2020 ◽  
Vol 142 ◽  
pp. 104553
Author(s):  
G. Boudoire ◽  
M. Liuzzo ◽  
S. Cappuzzo ◽  
G. Giuffrida ◽  
P. Cosenza ◽  
...  
Keyword(s):  
User Interface ◽  
Open Source ◽  
Graphical User Interface ◽  
Post Processing ◽  
Soil Surveys

X-Light: an open-source software written in Python to determine the residual stress by X-ray diffraction

2021 ◽  
Vol 54 (4) ◽  
Author(s):  
Tu-Quoc-Sang Pham ◽  
Guillaume Geandier ◽  
Nicolas Ratel-Ramond ◽  
Charles Mareau ◽  
Benoit Malard
Keyword(s):  
Residual Stress ◽  
User Interface ◽  
Synchrotron Radiation ◽  
Stress Analysis ◽  
Open Source ◽  
Graphical User Interface ◽  
Open Source Software ◽  
Diffraction Data ◽  
X Ray Diffraction ◽  
X Ray

X-Light is an open-source software that is written in Python with a graphical user interface. X-Light was developed to determine residual stress by X-ray diffraction. This software can process the 0D, 1D and 2D diffraction data obtained with laboratory diffractometers or synchrotron radiation. X-Light provides several options for stress analysis and five functions to fit a peak: Gauss, Lorentz, Pearson VII, pseudo-Voigt and Voigt. The residual stress is determined by the conventional sin2ψ method and the fundamental method.

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