scholarly journals Alview: Portable Software for Viewing Sequence Reads in BAM Formatted Files

2015 ◽  
Vol 14 ◽  
pp. CIN.S26470 ◽  
Author(s):  
Richard P. Finney ◽  
Qing-Rong Chen ◽  
Cu V. Nguyen ◽  
Chih Hao Hsu ◽  
Chunhua Yan ◽  
...  
Keyword(s):  
Graphical User Interface ◽  
Reference Genome ◽  
Source Code ◽  
Software Tool ◽  
Command Line ◽  
Sequencing Data ◽  
Genome Data ◽  
Command Line Tool ◽  
Portable Software ◽  
Microsoft Windows

The name Alview is a contraction of the term Alignment Viewer. Alview is a compiled to native architecture software tool for visualizing the alignment of sequencing data. Inputs are files of short-read sequences aligned to a reference genome in the SAM/BAM format and files containing reference genome data. Outputs are visualizations of these aligned short reads. Alview is written in portable C with optional graphical user interface (GUI) code written in C, C++, and Objective-C. The application can run in three different ways: as a web server, as a command line tool, or as a native, GUI program. Alview is compatible with Microsoft Windows, Linux, and Apple OS X. It is available as a web demo at https://cgwb.nci.nih.gov/cgi-bin/alview . The source code and Windows/Mac/Linux executables are available via https://github.com/NCIP/alview .

scholarly journals NanoPack: visualizing and processing long read sequencing data

2017 ◽  
Author(s):  
Wouter De Coster ◽  
Svenn D’Hert ◽  
Darrin T. Schultz ◽  
Marc Cruts ◽  
Christine Van Broeckhoven
Keyword(s):  
Web Service ◽  
Graphical User Interface ◽  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Sequencing Data ◽  
Link Type ◽  
Oxford Nanopore ◽  
Long Read ◽  
Oxford Nanopore Technologies

AbstractSummary: Here we describe NanoPack, a set of tools developed for visualization and processing of long read sequencing data from Oxford Nanopore Technologies and Pacific Biosciences.Availability and Implementation: The NanoPack tools are written in Python3 and released under the GNU GPL3.0 Licence. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.Contact:[email protected] information: Supplementary tables and figures are available at Bioinformatics online.

scholarly journals idCOV: a pipeline for quick clade identification of SARS-CoV-2 isolates

2020 ◽  
Author(s):  
Xun Zhu ◽  
Ti-Cheng Chang ◽  
Richard Webby ◽  
Gang Wu
Keyword(s):  
Personal Computer ◽  
Source Code ◽  
Command Line ◽  
Sequencing Data ◽  
Link Type ◽  
Public Dataset ◽  
Virus Isolates

AbstractidCOV is a phylogenetic pipeline for quickly identifying the clades of SARS-CoV-2 virus isolates from raw sequencing data based on a selected clade-defining marker list. Using a public dataset, we show that idCOV can make equivalent calls as annotated by Nextstrain.org on all three common clade systems using user uploaded FastQ files directly. Web and equivalent command-line interfaces are available. It can be deployed on any Linux environment, including personal computer, HPC and the cloud. The source code is available at https://github.com/xz-stjude/idcov. A documentation for installation can be found at https://github.com/xz-stjude/idcov/blob/master/README.md.

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 Guidelines for a Standardized Filesystem Layout for Scientific Data

Data ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 43
Author(s):  
Florian Spreckelsen ◽  
Baltasar Rüchardt ◽  
Jan Lebert ◽  
Stefan Luther ◽  
Ulrich Parlitz ◽  
...  
Keyword(s):  
Graphical User Interface ◽  
Software Tool ◽  
Original Data ◽  
Scientific Data ◽  
Command Line ◽  
Scientific Data Management ◽  
File Structure ◽  
Widespread Acceptance ◽  
Broad Variety ◽  
Existing Data

Storing scientific data on the filesystem in a meaningful and transparent way is no trivial task. In particular, when the data have to be accessed after their originator has left the lab, the importance of a standardized filesystem layout cannot be underestimated. It is desirable to have a structure that allows for the unique categorization of all kinds of data from experimental results to publications. They have to be accessible to a broad variety of workflows, e.g., via graphical user interface as well as via command line, in order to find widespread acceptance. Furthermore, the inclusion of already existing data has to be as simple as possible. We propose a three-level layout to organize and store scientific data that incorporates the full chain of scientific data management from data acquisition to analysis to publications. Metadata are saved in a standardized way and connect original data to analyses and publications as well as to their originators. A simple software tool to check a file structure for compliance with the proposed structure is presented.

CoRC: the COPASI R Connector

2021 ◽  
Author(s):  
Jonas Förster ◽  
Frank T Bergmann ◽  
Jürgen Pahle
Keyword(s):  
Graphical User Interface ◽  
Academic Research ◽  
R Package ◽  
Supplementary Information ◽  
Command Line ◽  
Graphical Interface ◽  
Thought Process ◽  
Extensive Analysis ◽  
Command Line Tool ◽  
High Level

Abstract Motivation COPASI is a biochemical simulator and model analyzer which has found widespread use in academic research, teaching and beyond. One of COPASI’s strengths is its graphical user interface, and this is what most users work with. COPASI also provides a command-line tool. So far, an intuitive scripting interface that allows the creation and documentation of systems biology workflows was missing though. Results We have developed CoRC, the COPASI R Connector, an R package which provides a high-level scripting interface for COPASI. It closely mirrors the thought process of a (graphical interface) user and should therefore be very easy to use. This allows for complex workflows to be reproducibly scripted, utilizing COPASI’s powerful analytic toolset in combination with R’s extensive analysis and package ecosystem. Availability and implementation CoRC is a free and open-source R package, available via GitHub at https://jpahle.github.io/CoRC/ under the Artistic-2.0 license.   Supplementary information: We provide tutorial articles as well as several example scripts on the project’s website.

scholarly journals Raritas and RaritasVox: Programs for counting high diversity categorical data with highly unequal abundances

2018 ◽  
Author(s):  
David Lazarus ◽  
Johan Renaudie ◽  
Dorina Lenz ◽  
Patrick Diver ◽  
Jens Klump
Keyword(s):  
Graphical User Interface ◽  
Categorical Data ◽  
Quantitative Data ◽  
Source Code ◽  
Flexible Structure ◽  
Command Line ◽  
File Format ◽  
Occurrence Data ◽  
Rare Category ◽  
High Diversity

Acquiring data on the occurrences of many types of difficult to identify objects are often still made by human observation, e.g. in biodiversity and paleontologic research. Existing computer counting programs used to record such data have various limitations, including inflexibility and cost. We describe a pair of new open-source programs for this purpose - Raritas and RaritasVox, which share a similar graphical user interface for mouse based counting, and file output format. Raritas is written in Python and can be run as a standalone app for recent versions of either MacOS or Windows, or from the command line as easily customized source code. RaritasVox in addition supports voice based counting but is written in Java and is more complex to install or modify. Both programs explicitly support a rare category count mode which makes it easier to collect quantitative data on rare categories, e.g. rare species which are important in biodiversity surveys. Lastly, as to our knowledge no standards exist yet, we describe a new stratigraphic occurrence data (SOD) unitary file format which combines extensive metadata and a flexible structure for recording occurrence data of species or other categories in a series of samples.

scholarly journals SgTiler: a fast method to design tiling sgRNAs for CRISPR/Cas9 mediated screening

2017 ◽  
Author(s):  
Musaddeque Ahmed ◽  
Housheng Hansen He
Keyword(s):  
Source Code ◽  
Regions Of Interest ◽  
Fast Method ◽  
Command Line ◽  
Regulatory Regions ◽  
Guide Rnas ◽  
Command Line Tool ◽  
Genomic Regions ◽  
The Web ◽  
Target Effects

AbstractSummaryScreening of genomic regions of interest using CRISPR/Cas9 is getting increasingly popular. The system requires designing of single guide RNAs (sgRNAs) that can efficiently guide the Cas9 endonuclease to the targeted region with minimal off-target effects. Tiling sgRNAs is the most effective way to perturb regulatory regions, such as promoters and enhancers. sgTiler is the first tool that provides a fast method for designing tiling sgRNAs.Availability and ImplementationsgTiler is a command line tool that requires only one command to execute. Its source code is freely available on the web at https://github.com/HansenHeLab/sgTiler. sgTiler is implemented in Python and supported on any platform with Python and Bowtie.

scholarly journals Bedshift: perturbation of genomic interval sets

2020 ◽  
Author(s):  
Aaron Gu ◽  
Hyun Jae Cho ◽  
Nathan C. Sheffield
Keyword(s):  
Euclidean Distance ◽  
Source Code ◽  
Similarity Metrics ◽  
Random Perturbations ◽  
Command Line ◽  
Genomic Interval ◽  
Command Line Tool ◽  
Evaluation Dataset ◽  
Original File ◽  
Coverage Score

Results of functional genomics experiments such as ChIP-Seq or ATAC-Seq produce data summarized as a region set. Many tools have been developed to analyze region sets, including computing similarity metrics to compare them. However, there is no way to objectively evaluate the effectiveness of region set similarity metrics. In this paper we present bedshift, a command-line tool and Python API to generate new BED files by making random perturbations to an original BED file. Perturbed files have known similarity to the original file and are therefore useful to benchmark similarity metrics. To demonstrate, we used bedshift to create an evaluation dataset of 3,600 perturbed files generated by shifting, adding, and dropping regions from a reference BED file. Then, we compared four similarity metrics: Jaccard score, coverage score, Euclidean distance, and cosine similarity. The results show that the Jaccard score is most sensitive to detecting adding and dropping regions, while the coverage score is more sensitive to shifted regions.AvailabilityBSD2-licensed source code and documentation can be found at https://bedshift.databio.org.

scholarly journals omicplotR: visualizing omic datasets as compositions

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Daniel J. Giguere ◽  
Jean M. Macklaim ◽  
Brandon Y. Lieng ◽  
Gregory B. Gloor
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
High Throughput Sequencing ◽  
Principal Component ◽  
Visual Exploration ◽  
Command Line ◽  
Sequencing Data ◽  
Software Packages ◽  
High Throughput Sequencing Data ◽  
Differential Abundance Analysis

Abstract Background Differential abundance analysis is widely used with high-throughput sequencing data to compare gene abundance or expression between groups of samples. Many software packages exist for this purpose, but each uses a unique set of statistical assumptions to solve problems on a case-by-case basis. These software packages are typically difficult to use for researchers without command-line skills, and software that does offer a graphical user interface do not use a compositionally valid method. Results omicplotR facilitates visual exploration of omic datasets for researchers with and without prior scripting knowledge. Reproducible visualizations include principal component analysis, hierarchical clustering, MA plots and effect plots. We demonstrate the functionality of omicplotR using a publicly available metatranscriptome dataset. Conclusions omicplotR provides a graphical user interface to explore sequence count data using generalizable compositional methods, facilitating visualization for investigators without command-line experience.

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