Easily phylotyping E. coli via the EzClermont web app and command-line tool

SummaryThe Clermont PCR method of phylotyping Escherichia coli has remained a useful classification scheme despite the proliferation of higher-resolution sequence typing schemes. We have implemented an in silico Clermont PCR method as both a web app and as a command-line tool to allow researchers to easily apply this phylotyping scheme to genome assemblies easily.Availability and ImplementationEzClermont is available as a web app at http://www.ezclermont.org. For local use, EzClermont can be installed with pip or installed from the source code at https://github.com/nickp60/ezclermont. All analysis was done with version [email protected], [email protected] informationTable S1: test dataset; S2: validation dataset; S3: results.

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Easy phylotyping of Escherichia coli via the EzClermont web app and command-line tool

Access Microbiology ◽

10.1099/acmi.0.000143 ◽

2020 ◽

Vol 2 (9) ◽

Cited By ~ 2

Author(s):

Nicholas R. Waters ◽

Florence Abram ◽

Fiona Brennan ◽

Ashleigh Holmes ◽

Leighton Pritchard

Keyword(s):

Escherichia Coli ◽

Type Species ◽

Whole Genome ◽

Command Line ◽

Content Type ◽

Link Type ◽

Command Line Tool ◽

Pcr Method ◽

Web App ◽

Genome Assemblies

The Clermont PCR method for phylotyping Escherichia coli remains a useful classification scheme even though genome sequencing is now routine, and higher-resolution sequence typing schemes are now available. Relating present-day whole-genome E. coli classifications to legacy phylotyping is essential for harmonizing the historical literature and understanding of this important organism. Therefore, we present EzClermont – a novel in silico Clermont PCR phylotyping tool to enable ready application of this phylotyping scheme to whole-genome assemblies. We evaluate this tool against phylogenomic classifications, and an alternative software implementation of Clermont typing. EzClermont is available as a web app at www.ezclermont.org, and as a command-line tool at https://nickp60.github.io/EzClermont/.

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

Bioinformatics ◽

10.1093/bioinformatics/btaa236 ◽

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.

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CoRC: the COPASI R Connector

Bioinformatics ◽

10.1093/bioinformatics/btab033 ◽

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.

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Megadepth: efficient coverage quantification for BigWigs and BAMs

10.1101/2020.12.17.423317 ◽

2020 ◽

Author(s):

Christopher Wilks ◽

Omar Ahmed ◽

Daniel N. Baker ◽

David Zhang ◽

Leonardo Collado-Torres ◽

...

Keyword(s):

Gene Annotation ◽

Command Line ◽

Bioconductor Package ◽

Input File ◽

Link Type ◽

Command Line Tool

AbstractMotivationA common way to summarize sequencing datasets is to quantify data lying within genes or other genomic intervals. This can be slow and can require different tools for different input file types.ResultsMegadepth is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor. Megadepth can summarize coverage within all disjoint intervals of the Gencode V35 gene annotation for more than 19,000 GTExV8 BigWig files in approximately one hour using 32 threads. Megadepth is available both as a command-line tool and as an R/Bioconductor package providing much faster quantification compared to the rtracklayer package.Availabilityhttps://github.com/ChristopherWilks/megadepth, https://bioconductor.org/packages/[email protected]

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NanoPack: visualizing and processing long read sequencing data

10.1101/237180 ◽

2017 ◽

Cited By ~ 2

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.

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CoV-Seq: SARS-CoV-2 Genome Analysis and Visualization

10.1101/2020.05.01.071050 ◽

2020 ◽

Cited By ~ 3

Author(s):

Boxiang Liu ◽

Kaibo Liu ◽

He Zhang ◽

Liang Zhang ◽

Yuchen Bian ◽

...

Keyword(s):

Ad Hoc ◽

Rapid Analysis ◽

Supplementary Information ◽

Command Line ◽

Command Line Interface ◽

Link Type ◽

Global Pandemic ◽

Fast Pace ◽

Public Repositories ◽

Programming Knowledge

AbstractSummaryCOVID-19 has become a global pandemic not long after its inception in late 2019. SARS-CoV-2 genomes are being sequenced and shared on public repositories at a fast pace. To keep up with these updates, scientists need to frequently refresh and reclean datasets, which is ad hoc and labor-intensive. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes. In order to address these challenges, we developed CoV-Seq, a webserver to enable simple and rapid analysis of SARS-CoV-2 genomes. Given a new sequence, CoV-Seq automatically predicts gene boundaries and identifies genetic variants, which are presented in an interactive genome visualizer and are downloadable for further analysis. A command-line interface is also available for high-throughput processing.Availability and ImplementationCoV-Seq is implemented in Python and Javascript. The webserver is available at http://covseq.baidu.com/ and the source code is available from https://github.com/boxiangliu/[email protected] informationSupplementary information are available at bioRxiv online.

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BiasAway: command-line and web server to generate nucleotide composition-matched DNA background sequences

Bioinformatics ◽

10.1093/bioinformatics/btaa928 ◽

2020 ◽

Author(s):

Aziz Khan ◽

Rafael Riudavets Puig ◽

Paul Boddie ◽

Anthony Mathelier

Keyword(s):

Dna Sequences ◽

Source Code ◽

Web Server ◽

Enrichment Analysis ◽

Nucleotide Composition ◽

Supplementary Information ◽

Command Line ◽

Sequence Composition ◽

Command Line Tool ◽

Gc Bias

Abstract Motivation Accurate motif enrichment analyses depend on the choice of background DNA sequences used, which should ideally match the sequence composition of the foreground sequences. It is important to avoid false positive enrichment due to sequence biases in the genome, such as GC-bias. Therefore, relying on an appropriate set of background sequences is crucial for enrichment analysis. Results We developed BiasAway, a command line tool and its dedicated easy-to-use web server to generate synthetic sequences matching any k-mer nucleotide composition or select genomic DNA sequences matching the mononucleotide composition of the foreground sequences through four different models. For genomic sequences, we provide precomputed partitions of genomes from nine species with five different bin sizes to generate appropriate genomic background sequences. Availability and implementation BiasAway source code is freely available from Bitbucket (https://bitbucket.org/CBGR/biasaway) and can be easily installed using bioconda or pip. The web server is available at https://biasaway.uio.no and a detailed documentation is available at https://biasaway.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.

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