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.

scholarly journals Genesis and Gappa: Processing, Analyzing and Visualizing Phylogenetic (Placement) Data

2019 ◽  
Author(s):  
Lucas Czech ◽  
Pierre Barbera ◽  
Alexandros Stamatakis
Keyword(s):  
Phylogenetic Trees ◽  
Command Line ◽  
Computationally Efficient ◽  
Data Types ◽  
Low Level ◽  
Phylogenetic Placement ◽  
Link Type ◽  
Phylogenetic Data ◽  
Command Line Tool ◽  
High Level

SummaryWe 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 ImplementationBoth 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/[email protected] and [email protected].

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 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.

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 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 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 GRIMM: GRaph IMputation and Matching for HLA Genotypes

2018 ◽  
Author(s):  
Martin Maiers ◽  
Michael Halagan ◽  
Loren Gragert ◽  
Pradeep Bashyal ◽  
Joel Schneider ◽  
...  
Keyword(s):  
Hla Typing ◽  
Supplementary Information ◽  
Graph Database ◽  
Supplementary Data ◽  
Computationally Efficient ◽  
Hla Matching ◽  
Match Algorithm ◽  
Hla Genotypes ◽  
Database Platform ◽  
Probabilistic Context

AbstractMotivation: For over 10 years allele-level HLA matching for bone marrow registries has been performed in a probabilistic context. HLA typing technologies provide ambiguous results in that they could not distinguish among all known HLA allele sequences, therefore registries have implemented matching algorithms that provide lists of donor and cord blood units ordered in terms of the likelihood of allele-level matching at specific HLA loci. With the growth of registry sizes, current match algorithm implementations are unable to provide match results in real time.Results: We present here novel computationally-efficient open source implementation of an HLA imputation and match algorithm using a graph database platform. Using graph traversal, our algorithm runtime grows slowly with registry size. This implementation generates results that agree with consensus output on a publicly-available match algorithm crossvalidation dataset.Availability: The Python, Perl and Neo4jJcode is available at https://git.com/nmdp-bioinformatics/grimmSupplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals PhySortR: a fast, flexible tool for sorting phylogenetic trees in R

2015 ◽  
Author(s):  
Timothy G Stephens ◽  
Debashish Bhattacharya ◽  
Mark A Ragan ◽  
Cheong Xin Chan
Keyword(s):  
Phylogenetic Trees ◽  
R Package ◽  
Command Line ◽  
Flexible Tool ◽  
Command Line Tool ◽  
Whole Tree

A frequent bottleneck in interpreting phylogenomic output is the need to screen often thousands of trees for features of interest, such as robust clades of specific taxa, as evidence of monophyletic relationship and/or reticulated evolution. Here we present PhySortR, a fast, flexible R package for sorting phylogenetic trees. Unlike existing utilities, PhySortR allows for identification of both exclusive and non-exclusive clades uniting the target taxa, with customisable options to assess clades within the context of the whole tree. PhySortR is a command-line tool that is freely available, highly scalable, and easily automatable.

scholarly journals MONET: a toolbox integrating top-performing methods for network modularization

2020 ◽  
Vol 36 (12) ◽  
pp. 3920-3921
Author(s):  
Mattia Tomasoni ◽  
Sergio Gómez ◽  
Jake Crawford ◽  
Weijia Zhang ◽  
Sarvenaz Choobdar ◽  
...  
Keyword(s):  
Molecular Network ◽  
Supplementary Information ◽  
Command Line ◽  
The Core ◽  
Disease Mechanisms ◽  
Different Types ◽  
Command Line Tool ◽  
Disease Module ◽  
Community Effort ◽  
Bioinformatics Community

Abstract Summary We define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful biomarkers. To this end, we launched the ‘Disease Module Identification (DMI) DREAM Challenge’, a community effort to build and evaluate unsupervised molecular network modularization algorithms. Here, we present MONET, a toolbox providing easy and unified access to the three top-performing methods from the DMI DREAM Challenge for the bioinformatics community. Availability and implementation MONET is a command line tool for Linux, based on Docker and Singularity containers; the core algorithms were written in R, Python, Ada and C++. It is freely available for download at https://github.com/BergmannLab/MONET.git. 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.

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