GfaViz: flexible and interactive visualization of GFA sequence graphs

2018 ◽  
Vol 35 (16) ◽  
pp. 2853-2855 ◽  
Author(s):  
Giorgio Gonnella ◽  
Niklas Niehus ◽  
Stefan Kurtz
Keyword(s):  
Interactive Visualization ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Command Line Interface ◽  
Vector Graphics ◽  
Fragment Assembly ◽  
Or Groups ◽  
Graphical Tool ◽  
Standard Configuration

Abstract Summary The graphical fragment assembly (GFA) formats are emerging standard formats for the representation of sequence graphs. Although GFA 1 was primarily targeting assembly graphs, the newer GFA 2 format introduces several features, which makes it suitable for representing other kinds of information, such as scaffolding graphs, variation graphs, alignment graphs and colored metagenomic graphs. Here, we present GfaViz, an interactive graphical tool for the visualization of sequence graphs in GFA format. The software supports all new features of GFA 2 and introduces conventions for their visualization. The user can choose between two different layouts and multiple styles for representing single elements or groups. All customizations can be stored in custom tags of the GFA format itself, without requiring external configuration files. Stylesheets are supported for storing standard configuration options for groups of files. The visualizations can be exported to raster and vector graphics formats. A command line interface allows for batch generation of images. Availability and implementation GfaViz is available at https://github.com/ggonnella/gfaviz Supplementary information Supplementary data are available at Bioinformatics online.

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

2020 ◽  
Vol 36 (10) ◽  
pp. 3263-3265 ◽  
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.

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

2019 ◽  
Vol 35 (21) ◽  
pp. 4405-4407 ◽  
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.

scholarly journals BioKEEN: a library for learning and evaluating biological knowledge graph embeddings

2019 ◽  
Vol 35 (18) ◽  
pp. 3538-3540 ◽  
Author(s):  
Mehdi Ali ◽  
Charles Tapley Hoyt ◽  
Daniel Domingo-Fernández ◽  
Jens Lehmann ◽  
Hajira Jabeen
Keyword(s):  
Supplementary Information ◽  
Knowledge Graph ◽  
Biological Knowledge ◽  
Command Line ◽  
Graph Embeddings ◽  
Command Line Interface ◽  
Software Ecosystem ◽  
Mapping Resource ◽  
Significant Attention

Abstract Summary Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies. Availability and implementation BioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals aCLImatise: automated generation of tool definitions for bioinformatics workflows

2020 ◽  
Author(s):  
Michael Milton ◽  
Natalie Thorne
Keyword(s):  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Automated Generation ◽  
Base Camp ◽  
Python Package ◽  
Bioinformatics Workflow ◽  
Bioinformatics Workflows

Abstract Summary aCLImatise is a utility for automatically generating tool definitions compatible with bioinformatics workflow languages, by parsing command-line help output. aCLImatise also has an associated database called the aCLImatise Base Camp, which provides thousands of pre-computed tool definitions. Availability and implementation The latest aCLImatise source code is available within a GitHub organisation, under the GPL-3.0 license: https://github.com/aCLImatise. In particular, documentation for the aCLImatise Python package is available at https://aclimatise.github.io/CliHelpParser/, and the aCLImatise Base Camp is available at https://aclimatise.github.io/BaseCamp/. Supplementary information Supplementary data are available at Bioinformatics online.

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

2020 ◽  
Vol 36 (9) ◽  
pp. 2934-2935 ◽  
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.

scholarly journals Knot_pull—python package for biopolymer smoothing and knot detection

2019 ◽  
Author(s):  
Aleksandra I Jarmolinska ◽  
Anna Gambin ◽  
Joanna I Sulkowska
Keyword(s):  
Learning Curve ◽  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Steep Learning Curve ◽  
Independent Source ◽  
Python Package

Abstract Summary The biggest hurdle in studying topology in biopolymers is the steep learning curve for actually seeing the knots in structure visualization. Knot_pull is a command line utility designed to simplify this process—it presents the user with a smoothing trajectory for provided structures (any number and length of protein, RNA or chromatin chains in PDB, CIF or XYZ format), and calculates the knot type (including presence of any links, and slipknots when a subchain is specified). Availability and implementation Knot_pull works under Python >=2.7 and is system independent. Source code and documentation are available at http://github.com/dzarmola/knot_pull under GNU GPL license and include also a wrapper script for PyMOL for easier visualization. Examples of smoothing trajectories can be found at: https://www.youtube.com/watch?v=IzSGDfc1vAY. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DamageProfiler: Fast damage pattern calculation for ancient DNA

2021 ◽  
Author(s):  
Judith Neukamm ◽  
Alexander Peltzer ◽  
Kay Nieselt
Keyword(s):  
Ancient Dna ◽  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Central Importance ◽  
Command Line Interface ◽  
Analysis Pipeline ◽  
File Formats ◽  
Programming Knowledge ◽  
User Friendly

Abstract Motivation In ancient DNA research, the authentication of ancient samples based on specific features remains a crucial step in data analysis. Because of this central importance, researchers lacking deeper programming knowledge should be able to run a basic damage authentication analysis. Such software should be user-friendly and easy to integrate into an analysis pipeline. Results DamageProfiler is a Java based, stand-alone software to determine damage patterns in ancient DNA. The results are provided in various file formats and plots for further processing. DamageProfiler has an intuitive graphical as well as command line interface that allows the tool to be easily embedded into an analysis pipeline. Availability All of the source code is freely available on GitHub (https://github.com/Integrative-Transcriptomics/DamageProfiler). Supplementary information Supplementary data are available at Bioinformatics online.

icHET: interactive visualization of cytoplasmic heteroplasmy

2019 ◽  
Vol 35 (21) ◽  
pp. 4411-4412 ◽  
Author(s):  
Vinhthuy Phan ◽  
Diem-Trang Pham ◽  
Caroline Melton ◽  
Adam J Ramsey ◽  
Bernie J Daigle ◽  
...  
Keyword(s):  
Reference Genome ◽  
Interactive Visualization ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Short Reads ◽  
Genome Wide ◽  
Computational Workflow ◽  
Multiple Samples

Abstract Summary Although heteroplasmy has been studied extensively in animal systems, there is a lack of tools for analyzing, exploring and visualizing heteroplasmy at the genome-wide level in other taxonomic systems. We introduce icHET, which is a computational workflow that produces an interactive visualization that facilitates the exploration, analysis and discovery of heteroplasmy across multiple genomic samples. icHET works on short reads from multiple samples from any organism with an organellar reference genome (mitochondrial or plastid) and a nuclear reference genome. Availability and implementation The software is available at https://github.com/vtphan/HeteroplasmyWorkflow. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Phylonium: fast estimation of evolutionary distances from large samples of similar genomes

2019 ◽  
Vol 36 (7) ◽  
pp. 2040-2046 ◽  
Author(s):  
Fabian Klötzl ◽  
Bernhard Haubold
Keyword(s):  
Disease Outbreaks ◽  
Supplementary Information ◽  
Whole Genome ◽  
Command Line ◽  
Supplementary Data ◽  
Large Samples ◽  
Fast Estimation ◽  
Unix Command ◽  
Similar Accuracy ◽  
Single Sequence

Abstract Motivation Tracking disease outbreaks by whole-genome sequencing leads to the collection of large samples of closely related sequences. Five years ago, we published a method to accurately compute all pairwise distances for such samples by indexing each sequence. Since indexing is slow, we now ask whether it is possible to achieve similar accuracy when indexing only a single sequence. Results We have implemented this idea in the program phylonium and show that it is as accurate as its predecessor and roughly 100 times faster when applied to all 2678 Escherichia coli genomes contained in ENSEMBL. One of the best published programs for rapidly computing pairwise distances, mash, analyzes the same dataset four times faster but, with default settings, it is less accurate than phylonium. Availability and implementation Phylonium runs under the UNIX command line; its C++ sources and documentation are available from github.com/evolbioinf/phylonium. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals pyGenomeTracks: reproducible plots for multivariate genomic data sets

2020 ◽  
Author(s):  
Lucille Lopez-Delisle ◽  
Leily Rabbani ◽  
Joachim Wolff ◽  
Vivek Bhardwaj ◽  
Rolf Backofen ◽  
...  
Keyword(s):  
Genomic Data ◽  
Supplementary Information ◽  
Data Sets ◽  
Command Line ◽  
Graphical Interface ◽  
Supplementary Data ◽  
Considerable Effort ◽  
Vector Graphic ◽  
Graphic Software

Abstract Motivation Generating publication ready plots to display multiple genomic tracks can pose a serious challenge. Making desirable and accurate figures requires considerable effort. This is usually done by hand or by using a vector graphic software. Results pyGenomeTracks (PGT) is a modular plotting tool that easily combines multiple tracks. It enables a reproducible and standardized generation of highly customizable and publication ready images. Availability PGT is available through a graphical interface on https://usegalaxy.eu and through the command line. It is provided on conda via the bioconda channel, on pip and it is openly developed on github: https://github.com/deeptools/pyGenomeTracks. Supplementary information Supplementary data are available at Bioinformatics online.

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