scholarly journals READemption - A tool for the computational analysis of deep-sequencing-based transcriptome data

2014 ◽  
Author(s):  
Konrad Ulrich Förstner ◽  
Jörg Vogel ◽  
Cynthia Mira Sharma
Keyword(s):  
Data Processing ◽  
Deep Sequencing ◽  
Computational Analysis ◽  
Command Line ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Command Line Interface ◽  
Parallel Data ◽  
Full Power ◽  
Computationally Intensive

Summary: RNA-Seq has become a potent and widely used method to qualitatively and quantitatively study transcriptomes. In order to draw biological conclusions based on RNA-Seq data, several steps some of which are computationally intensive, have to betaken. Our READemption pipeline takes care of these individual tasks and integrates them into an easy-to-use tool with a command line interface. To leverage the full power of modern computers, most subcommands of READemption offer parallel data processing. While READemption was mainly developed for the analysis of bacterial primary transcriptomes, we have successfully applied it to analyze RNA-Seq reads from other sample types, including whole transcriptomes, RNA immunoprecipitated with proteins, not only from bacteria, but also from eukaryotes and archaea. Availability and Implementation: READemption is implemented in Python and is published under the ISC open source license. The tool and documentation is hosted at http://pythonhosted.org/READemption (DOI:10.6084/m9.figshare.977849).

scholarly journals UCEasy: A software package for automating and simplifying the analysis of ultraconserved elements (UCEs)

2021 ◽  
Vol 9 ◽  
Author(s):  
Caio Ribeiro ◽  
Lucas Oliveira ◽  
Romina Batista ◽  
Marcos De Sousa
Keyword(s):  
Best Practices ◽  
Software Package ◽  
Phylogenetic Trees ◽  
Computational Analysis ◽  
Data Matrix ◽  
Command Line ◽  
Command Line Interface ◽  
Ultraconserved Elements ◽  
Research Software ◽  
Different Levels

The use of Ultraconserved Elements (UCEs) as genetic markers in phylogenomics has become popular and has provided promising results. Although UCE data can be easily obtained from targeted enriched sequencing, the protocol for in silico analysis of UCEs consist of the execution of heterogeneous and complex tools, a challenge for scientists without training in bioinformatics. Developing tools with the adoption of best practices in research software can lessen this problem by improving the execution of computational experiments, thus promoting better reproducibility. We present UCEasy, an easy-to-install and easy-to-use software package with a simple command line interface that facilitates the computational analysis of UCEs from sequencing samples, following the best practices of research software. UCEasy is a wrapper that standardises, automates and simplifies the quality control of raw reads, assembly and extraction and alignment of UCEs, generating at the end a data matrix with different levels of completeness that can be used to infer phylogenetic trees. We demonstrate the functionalities of UCEasy by reproducing the published results of phylogenomic studies of the bird genus Turdus (Aves) and of Adephaga families (Coleoptera) containing genomic datasets to efficiently extract UCEs.

scholarly journals iREAD: A Tool for Intron Retention Detection from RNA-seq Data

2017 ◽  
Author(s):  
Hong-Dong Li ◽  
Cory C. Funk ◽  
Nathan D. Price
Keyword(s):  
Intron Retention ◽  
Distribution Patterns ◽  
Functional Enrichment ◽  
Command Line ◽  
Rna Seq ◽  
Text File ◽  
Command Line Interface ◽  
Genome Wide ◽  
A Genome ◽  
Retained Introns

AbstractSummaryDetecting intron retention (IR) events is emerging as a specialized need for RNA-seq data analysis. Here we present iREAD (intron REtention Analysis and Detector), a tool to detect IR events genome-wide from high-throughput RNA-seq data. The command line interface for iREAD is implemented in Python. iREAD takes as input an existing BAM file, representing the transcriptome, and a text file containing the intron coordinates of a genome. It then 1) counts all reads that overlap intron regions, 2) detects IR vents by analyzing features of reads such as depth and distribution patterns, and 3) outputs a list of retained introns into a tab-delimited text file. The output can be directly used for further exploratory analysis such as differential intron expression and functional enrichment. iREAD provides a new and generic tool to interrogate poly-A enriched transcriptomic data of intron regions.Availabilitywww.libpls.net/[email protected]

scholarly journals Methods for RNA Modification Mapping Using Deep Sequencing: Established and New Emerging Technologies

Genes ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 35 ◽  
Author(s):  
Yuri Motorin ◽  
Mark Helm
Keyword(s):  
Deep Sequencing ◽  
Global Scale ◽  
High Rate ◽  
Rna Modification ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Rna Modifications ◽  
Accuracy And Precision ◽  
A Cell ◽  
Mapping Techniques

New analytics of post-transcriptional RNA modifications have paved the way for a tremendous upswing of the biological and biomedical research in this field. This especially applies to methods that included RNA-Seq techniques, and which typically result in what is termed global scale modification mapping. In this process, positions inside a cell`s transcriptome are receiving a status of potential modification sites (so called modification calling), typically based on a score of some kind that issues from the particular method applied. The resulting data are thought to represent information that goes beyond what is contained in typical transcriptome data, and hence the field has taken to use the term “epitranscriptome”. Due to the high rate of newly published mapping techniques, a significant number of chemically distinct RNA modifications have become amenable to mapping, albeit with variegated accuracy and precision, depending on the nature of the technique. This review gives a brief overview of known techniques, and how they were applied to modification calling.

scholarly journals READemption--a tool for the computational analysis of deep-sequencing-based transcriptome data

2014 ◽  
Vol 30 (23) ◽  
pp. 3421-3423 ◽  
Author(s):  
K. U. Forstner ◽  
J. Vogel ◽  
C. M. Sharma
Keyword(s):  
Deep Sequencing ◽  
Computational Analysis ◽  
Transcriptome Data

scholarly journals Reproducible genomics analysis pipelines with GNU Guix

2018 ◽  
Author(s):  
Ricardo Wurmus ◽  
Bora Uyar ◽  
Brendan Osberg ◽  
Vedran Franke ◽  
Alexander Gosdschan ◽  
...  
Keyword(s):  
Experimental Data ◽  
Data Sets ◽  
Command Line ◽  
Rna Seq ◽  
Software Environment ◽  
Research Fields ◽  
Laboratory Workers ◽  
Computationally Intensive

AbstractIn bioinformatics, as well as other computationally-intensive research fields, there is a need for workflows that can reliably produce consistent output, independent of the software environment or configuration settings of the machine on which they are executed. Indeed, this is essential for controlled comparison between different observations or for the wider dissemination of workflows. Providing this type of reproducibility, however, is often complicated by the need to accommodate the myriad dependencies included in a larger body of software, each of which generally come in various versions. Moreover, in many fields (bioinformatics being a prime example), these versions are subject to continual change due to rapidly evolving technologies, further complicating problems related to reproducibility. Here, we propose a principled approach for building analysis pipelines and managing their dependencies. As a case study to demonstrate the utility of our approach, we present a set of highly reproducible pipelines for the analysis of RNA-seq, ChIP-seq, Bisulfite-seq, and single-cell RNA-seq. All pipelines process raw experimental data, and generate reports containing publication-ready plots and figures, with interactive report elements and standard observables. Users may install these highly reproducible packages and apply them to their own datasets without any special computational expertise beyond the use of the command line. We hope such a toolkit will provide immediate benefit to laboratory workers wishing to process their own data sets or bioinformaticians seeking to automate all, or parts of, their analyses. In the long term, we hope our approach to reproducibility will serve as a blueprint for reproducible workflows in other areas. Our pipelines, along with their corresponding documentation and sample reports, are available at http://bioinformatics.mdc-berlin.de/pigx

scholarly journals smallrnaseq: short non coding RNA-Seq analysis with Python

2017 ◽  
Author(s):  
Damien Farrell
Keyword(s):  
Novel Species ◽  
Command Line ◽  
Rna Seq ◽  
Absolute Abundance ◽  
Command Line Interface ◽  
Novel Mirna ◽  
Non Coding Rna ◽  
Regulatory Functions ◽  
Python Package ◽  
Generation Sequencing

ABSTRACTThe use of next generation sequencing is now a standard approach to elucidate the small non-coding RNA species (sncRNAs) present in tissue and biofluid samples. This has revealed the wide variety of RNAs with regulatory functions the best studied of which are microRNAs. Profiling of sncRNAs by deep sequencing allows measures of absolute abundance and for the discovery of novel species that have eluded previous methods. Specific considerations must be made when quantifying and cataloging sncRNAs and multiple algorithms are now available, mostly focused on miRNA analysis. smallrnaseq is a Python package that implements some of the standard approaches for quantification and analysis of sncRNAs. This includes miRNA quantification and novel miRNA prediction. A command line interface makes the software accessible for general users.

ESIS ON THE WORLD WIDE WEB

1994 ◽  
Vol 05 (05) ◽  
pp. 805-809 ◽  
Author(s):  
SALIM G. ANSARI ◽  
PAOLO GIOMMI ◽  
ALBERTO MICOL
Keyword(s):  
World Wide Web ◽  
Web Application ◽  
World Wide ◽  
Markup Language ◽  
Command Line ◽  
Command Line Interface ◽  
The World ◽  
Hypertext Transfer Protocol ◽  
Hypertext Markup Language ◽  
Near Future

On 3rd November, 1993, ESIS announced its Homepage on the World Wide Web (WWW) to the user community. Ever since then, ESIS has steadily increased its Web support to the astronomical community to include a bibliographic service, the ESIS catalogue documentation and the ESIS Data Browser. More functionality will be added in the near future. All these services share a common ESIS structure that is used by other ESIS user paradigms such as the ESIS Graphical User Interface (Giommi and Ansari, 1993), and the ESIS Command Line Interface. A forms-based paradigm, each ESIS-Web application interfaces to the hypertext transfer protocol (http) translating queries from/to the hypertext markup language (html) format understood by the NCSA Mosaic interface. In this paper, we discuss the ESIS system and show how each ESIS service works on the World Wide Web client.

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