SeqProperties: A Python Command-Line Tool for Basic Sequence Analysis

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 .

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FAN-C: A Feature-rich Framework for the Analysis and Visualisation of C data

10.1101/2020.02.03.932517 ◽

2020 ◽

Cited By ~ 6

Author(s):

Kai Kruse ◽

Clemens B. Hug ◽

Juan M. Vaquerizas

Keyword(s):

High Throughput ◽

Matrix Analysis ◽

Set Covering ◽

Command Line ◽

Chromosome Conformation ◽

C Storage ◽

Data Formats ◽

Analysis Tools ◽

Command Line Tool ◽

Broad Feature

Chromosome conformation capture data, particularly from high-throughput approaches such as Hi-C and its derivatives, are typically very complex to analyse. Existing analysis tools are often single-purpose, or limited in compatibility to a small number of data formats, frequently making Hi-C analyses tedious and time-consuming. Here, we present FAN-C, an easy-to-use command-line tool and powerful Python API with a broad feature set covering matrix generation, analysis, and visualisation for C-like data (https://github.com/vaquerizaslab/fanc). Due to its comprehensiveness and compatibility with the most prevalent Hi-C storage formats, FAN-C can be used in combination with a large number of existing analysis tools, thus greatly simplifying Hi-C matrix analysis.

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ScaffoldGraph: an open-source library for the generation and analysis of molecular scaffold networks and scaffold trees

Bioinformatics ◽

10.1093/bioinformatics/btaa219 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3930-3931 ◽

Cited By ~ 1

Author(s):

Oliver B Scott ◽

A W Edith Chan

Keyword(s):

Open Source ◽

High Throughput Screening ◽

Chemical Space ◽

Diversity Analysis ◽

Graph Analysis ◽

Command Line ◽

Molecular Scaffold ◽

Large Sets ◽

Command Line Tool ◽

Scaffold Diversity

Abstract Summary ScaffoldGraph (SG) is an open-source Python library and command-line tool for the generation and analysis of molecular scaffold networks and trees, with the capability of processing large sets of input molecules. With the increase in high-throughput screening data, scaffold graphs have proven useful for the navigation and analysis of chemical space, being used for visualization, clustering, scaffold-diversity analysis and active-series identification. Built on RDKit and NetworkX, SG integrates scaffold graph analysis into the growing scientific/cheminformatics Python stack, increasing the flexibility and extendibility of the tool compared to existing software. Availability and implementation SG is freely available and released under the MIT licence at https://github.com/UCLCheminformatics/ScaffoldGraph.

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Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data

Bioinformatics ◽

10.1093/bioinformatics/btaa070 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3263-3265 ◽

Cited By ~ 14

Author(s):

Lucas Czech ◽

Pierre Barbera ◽

Alexandros Stamatakis

Keyword(s):

Phylogenetic Trees ◽

Supplementary Information ◽

Command Line ◽

Supplementary Data ◽

Computationally Efficient ◽

Data Types ◽

Low Level ◽

Phylogenetic Placement ◽

Command Line Tool ◽

High Level

Abstract Summary We present genesis, a library for working with phylogenetic data, and gappa, an accompanying command-line tool for conducting typical analyses on such data. The tools target phylogenetic trees and phylogenetic placements, sequences, taxonomies and other relevant data types, offer high-level simplicity as well as low-level customizability, and are computationally efficient, well-tested and field-proven. Availability and implementation Both genesis and gappa are written in modern C++11, and are freely available under GPLv3 at http://github.com/lczech/genesis and http://github.com/lczech/gappa. Supplementary information Supplementary data are available at Bioinformatics online.

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Spliceogen: an integrative, scalable tool for the discovery of splice-altering variants

Bioinformatics ◽

10.1093/bioinformatics/btz263 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4405-4407 ◽

Cited By ~ 1

Author(s):

Steven Monger ◽

Michael Troup ◽

Eddie Ip ◽

Sally L Dunwoodie ◽

Eleni Giannoulatou

Keyword(s):

Supplementary Information ◽

Command Line ◽

Supplementary Data ◽

In Silico Prediction ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Prediction Tools ◽

Motif Prediction ◽

Command Line Tool ◽

Genome Scale

Abstract Motivation In silico prediction tools are essential for identifying variants which create or disrupt cis-splicing motifs. However, there are limited options for genome-scale discovery of splice-altering variants. Results We have developed Spliceogen, a highly scalable pipeline integrating predictions from some of the individually best performing models for splice motif prediction: MaxEntScan, GeneSplicer, ESRseq and Branchpointer. Availability and implementation Spliceogen is available as a command line tool which accepts VCF/BED inputs and handles both single nucleotide variants (SNVs) and indels (https://github.com/VCCRI/Spliceogen). SNV databases with prediction scores are also available, covering all possible SNVs at all genomic positions within all Gencode-annotated multi-exon transcripts. Supplementary information Supplementary data are available at Bioinformatics online.

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FAN-C: a feature-rich framework for the analysis and visualisation of chromosome conformation capture data

Genome Biology ◽

10.1186/s13059-020-02215-9 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Kai Kruse ◽

Clemens B. Hug ◽

Juan M. Vaquerizas

Keyword(s):

Chromosome Conformation Capture ◽

Matrix Analysis ◽

Set Covering ◽

Command Line ◽

Chromosome Conformation ◽

C Storage ◽

Data Formats ◽

Analysis Tools ◽

Command Line Tool ◽

Broad Feature

AbstractChromosome conformation capture data, particularly from high-throughput approaches such as Hi-C, are typically very complex to analyse. Existing analysis tools are often single-purpose, or limited in compatibility to a small number of data formats, frequently making Hi-C analyses tedious and time-consuming. Here, we present FAN-C, an easy-to-use command-line tool and powerful Python API with a broad feature set covering matrix generation, analysis, and visualisation for C-like data (https://github.com/vaquerizaslab/fanc). Due to its compatibility with the most prevalent Hi-C storage formats, FAN-C can be used in combination with a large number of existing analysis tools, thus greatly simplifying Hi-C matrix analysis.

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Visualization of circular RNAs and their internal splicing events from transcriptomic data

Bioinformatics ◽

10.1093/bioinformatics/btaa033 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2934-2935 ◽

Cited By ~ 1

Author(s):

Yi Zheng ◽

Fangqing Zhao

Keyword(s):

Supplementary Information ◽

Circular Rnas ◽

Visualization Tool ◽

Command Line ◽

Supplementary Data ◽

Transcriptomic Data ◽

Command Line Tool ◽

Transcriptome Comparison ◽

Multiple Samples ◽

Splicing Patterns

Abstract Summary Circular RNAs (circRNAs) are proved to have unique compositions and splicing events distinct from canonical mRNAs. However, there is no visualization tool designed for the exploration of complex splicing patterns in circRNA transcriptomes. Here, we present CIRI-vis, a Java command-line tool for quantifying and visualizing circRNAs by integrating the alignments and junctions of circular transcripts. CIRI-vis can be applied to visualize the internal structure and isoform abundance of circRNAs and perform circRNA transcriptome comparison across multiple samples. Availability and implementation https://sourceforge.net/projects/ciri/files/CIRI-vis. Supplementary information Supplementary data are available at Bioinformatics online.

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Research on Active Control of Rotating Motion and Vibration of Flexible Manipulator

Shock and Vibration ◽

10.1155/2021/6661910 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Qingpeng Han ◽

Wenwen Dong ◽

Bin Wu ◽

Xinhang Shen ◽

Meilin Zhang ◽

...

Keyword(s):

Control Strategy ◽

Piezoelectric Actuators ◽

Flexible Manipulator ◽

Joint Control ◽

Command Line ◽

Control Torque ◽

Pd Control ◽

Lagrange Principle ◽

Lyapunov Approach ◽

Command Line Tool

In this study, PZT (piezoelectric) actuators and PD control (PDs’ command line tool) method is selected to control the vibration of the flexible manipulator. The dynamic equations of the flexible manipulator system are established based on Lagrange principle. The control strategy of PZT actuators and joint control torque are designed. It is investigated by a Lyapunov approach that a combined scheme of PD feedback and command voltages applies to segmented PZT actuators. By comparison, only PD feedback control is also considered to control the flexible manipulator. The numerical simulations prove that the method of the designed PZT actuators’ control strategy and PD control is effective to compress the vibration of the flexible manipulator.

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Methplotlib: analysis of modified nucleotides from nanopore sequencing

10.1101/826107 ◽

2019 ◽

Author(s):

Wouter De Coster ◽

Mojca Strazisar

Keyword(s):

Gene Expression Regulation ◽

Command Line ◽

Nanopore Sequencing ◽

Modified Nucleotides ◽

Oxford Nanopore ◽

Command Line Tool ◽

Within Subjects ◽

Allele Specific ◽

Sequencing Platforms ◽

Oxford Nanopore Technologies

AbstractSummaryModified nucleotides play a crucial role in gene expression regulation. Here we describe methplotlib, a tool developed for the visualization of modified nucleotides detected from Oxford Nanopore Technologies sequencing platforms, together with additional scripts for statistical analysis of allele specific modification within subjects and differential modification frequency across subjects.Availability and implementationThe methplotlib command-line tool is written in Python3, is compatible with Linux, Mac OS and the MS Windows 10 Subsystem for Linux and released under the MIT license. The source code can be found at https://github.com/wdecoster/methplotlib and can be installed from PyPI and bioconda. Our repository includes test data and the tool is continuously tested at [email protected]

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LAVA: a streamlined visualization tool for longitudinal analysis of viral alleles

10.1101/2019.12.17.879320 ◽

2019 ◽

Cited By ~ 1

Author(s):

Michelle J. Lin ◽

Ryan C. Shean ◽

Negar Makhsous ◽

Alexander L. Greninger

Keyword(s):

Genome Evolution ◽

Longitudinal Analysis ◽

Viral Evolution ◽

Read Depth ◽

Visualization Tool ◽

Command Line ◽

Interactive Analysis ◽

Command Line Tool ◽

User Friendly ◽

Over Time

AbstractWith their small genomes, fast evolutionary rates, and clinical significance, viruses have long been fodder for studies of whole genome evolution. One common need in these studies is the analysis of viral evolution over time through longitudinal sampling. However, there exists no simple tool to automate such analyses. We created a simple command-line visualization tool called LAVA (Longitudinal Analysis of Viral Alleles). LAVA allows dynamic and interactive visualization of viral evolution across the genome and over time. Results are easily shared via a single HTML file that also allows interactive analysis based on read depth and allele frequency. LAVA requires minimal input and runs in minutes for most use cases. LAVA is programmed mainly in Python 3 and is compatible with Mac and Linux machines. LAVA is a user-friendly command-line tool for generating, visualizing, and sharing the results of longitudinal viral genome evolution analysis. Instructions for downloading, installing, and using LAVA can be found at https://github.com/michellejlin/lava.

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