Dataset Information

Circtools - a one-stop software solution for circular RNA research.

ABSTRACT: Motivation:Circular RNAs (circRNAs) originate through back-splicing events from linear primary transcripts, are resistant to exonucleases, are not polyadenylated, and have been shown to be highly specific for cell type and developmental stage. CircRNA detection starts from high-throughput sequencing data and is a multi-stage bioinformatics process yielding sets of potential circRNA candidates that require further analyses. While a number of tools for the prediction process already exist, publicly available analysis tools for further characterization are rare. Our work provides researchers with a harmonized workflow that covers different stages of in silico circRNA analyses, from prediction to first functional insights. Results:Here, we present circtools, a modular, Python-based framework for computational circRNA analyses. The software includes modules for circRNA detection, internal sequence reconstruction, quality checking, statistical testing, RNA binding protein enrichment screenings, differential exon RNase R resistance, and circRNA-specific primer design. Circtools supports researchers with visualization options and data export into commonly used formats. Availability:Circtools is available via github.com/dieterich-lab/circtools and circ.tools under GPLv3.0. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Jakobi T

PROVIDER: S-EPMC6596886 | biostudies-other | 2018 Nov

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

circtools-a one-stop software solution for circular RNA research.

Jakobi Tobias T Uvarovskii Alexey A Dieterich Christoph C

Bioinformatics (Oxford, England) 20190701 13

<h4>Motivation</h4>Circular RNAs (circRNAs) originate through back-splicing events from linear primary transcripts, are resistant to exonucleases, are not polyadenylated and have been shown to be highly specific for cell type and developmental stage. CircRNA detection starts from high-throughput sequencing data and is a multi-stage bioinformatics process yielding sets of potential circRNA candidates that require further analyses. While a number of tools for the prediction process already exist, ...[more]

PMID: 30462173

Similar Datasets

Project description:BackgroundCircular RNAs (circRNAs) are a class of non-coding RNAs formed by pre-mRNA back-splicing, which are widely expressed in animal/plant cells and often play an important role in regulating microRNA (miRNA) activities. While numerous databases have collected a large amount of predicted circRNA candidates and provided the corresponding circRNA-regulated interactions, a stand-alone package for constructing circRNA-miRNA-mRNA interactions based on user-identified circRNAs across species is lacking.ResultsWe present CircMiMi (circRNA-miRNA-mRNA interactions), a modular, Python-based software to identify circRNA-miRNA-mRNA interactions across 18 species (including 16 animals and 2 plants) with the given coordinates of circRNA junctions. The CircMiMi-constructed circRNA-miRNA-mRNA interactions are derived from circRNA-miRNA and miRNA-mRNA axes with the support of computational predictions and/or experimental data. CircMiMi also allows users to examine alignment ambiguity of back-splice junctions for checking circRNA reliability and examine reverse complementary sequences residing in the sequences flanking the circularized exons for investigating circRNA formation. We further employ CircMiMi to identify circRNA-miRNA-mRNA interactions based on the circRNAs collected in NeuroCirc, a large-scale database of circRNAs in the human brain. We construct circRNA-miRNA-mRNA interactions comprising differentially expressed circRNAs, and miRNAs in autism spectrum disorder (ASD) and cross-species analyze the relevance of the targets to ASD. We thus provide a rich set of ASD-associated circRNA-miRNA-mRNA axes and a useful starting point for investigation of regulatory mechanisms in ASD pathophysiology.ConclusionsCircMiMi allows users to identify circRNA-mediated interactions in multiple species, shedding light on regulatory roles of circRNAs. The software package and web interface are freely available at https://github.com/TreesLab/CircMiMi and http://circmimi.genomics.sinica.edu.tw/ , respectively.

Project description:BackgroundPolicymakers, stakeholders and researchers have not been able to find research evidence about health systems using an easily understood taxonomy of topics, know when they have conducted a comprehensive search of the many types of research evidence relevant to them, or rapidly identify decision-relevant information in their search results.MethodsTo address these gaps, we developed an approach to building a 'one-stop shop' for research evidence about health systems. We developed a taxonomy of health system topics and iteratively refined it by drawing on existing categorization schemes and by using it to categorize progressively larger bundles of research evidence. We identified systematic reviews, systematic review protocols, and review-derived products through searches of Medline, hand searches of several databases indexing systematic reviews, hand searches of journals, and continuous scanning of listservs and websites. We developed an approach to providing 'added value' to existing content (e.g., coding systematic reviews according to the countries in which included studies were conducted) and to expanding the types of evidence eligible for inclusion (e.g., economic evaluations and health system descriptions). Lastly, we developed an approach to continuously updating the online one-stop shop in seven supported languages.ResultsThe taxonomy is organized by governance, financial, and delivery arrangements and by implementation strategies. The 'one-stop shop', called Health Systems Evidence, contains a comprehensive inventory of evidence briefs, overviews of systematic reviews, systematic reviews, systematic review protocols, registered systematic review titles, economic evaluations and costing studies, health reform descriptions and health system descriptions, and many types of added-value coding. It is continuously updated and new content is regularly translated into Arabic, Chinese, English, French, Portuguese, Russian, and Spanish.ConclusionsPolicymakers and stakeholders can now easily access and use a wide variety of types of research evidence about health systems to inform decision-making and advocacy. Researchers and research funding agencies can use Health Systems Evidence to identify gaps in the current stock of research evidence and domains that could benefit from primary research, systematic reviews, and review overviews.

Dataset Information

Circtools - a one-stop software solution for circular RNA research.

Publications

circtools-a one-stop software solution for circular RNA research.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets