Dataset Information

SuperTranscripts: a data driven reference for analysis and visualisation of transcriptomes.

ABSTRACT: Numerous methods have been developed to analyse RNA sequencing (RNA-seq) data, but most rely on the availability of a reference genome, making them unsuitable for non-model organisms. Here we present superTranscripts, a substitute for a reference genome, where each gene with multiple transcripts is represented by a single sequence. The Lace software is provided to construct superTranscripts from any set of transcripts, including de novo assemblies. We demonstrate how superTranscripts enable visualisation, variant detection and differential isoform detection in non-model organisms. We further use Lace to combine reference and assembled transcriptomes for chicken and recover hundreds of gaps in the reference genome.

SUBMITTER: Davidson NM

PROVIDER: S-EPMC5543425 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

SuperTranscripts: a data driven reference for analysis and visualisation of transcriptomes.

Davidson Nadia M NM Hawkins Anthony D K ADK Oshlack Alicia A

Genome biology 20170804 1

Numerous methods have been developed to analyse RNA sequencing (RNA-seq) data, but most rely on the availability of a reference genome, making them unsuitable for non-model organisms. Here we present superTranscripts, a substitute for a reference genome, where each gene with multiple transcripts is represented by a single sequence. The Lace software is provided to construct superTranscripts from any set of transcripts, including de novo assemblies. We demonstrate how superTranscripts enable visu ...[more]

PMID: 28778180

Similar Datasets

Project description:IntroductionChronic conditions require long periods of care and often involve repeated interactions with multiple healthcare providers. Faced with increasing illness burden and costs, healthcare systems are currently working towards integrated care to streamline these interactions and improve efficiency. To support this, one promising resource is the information on routine care delivery stored in various electronic healthcare databases (EHD). In chronic conditions, care delivery pathways (CDPs) can be constructed by linking multiple data sources and extracting time-stamped healthcare utilisation events and other medical data related to individual or groups of patients over specific time periods; CDPs may provide insights into current practice and ways of improving it. Several methods have been proposed in recent years to quantify and visualise CDPs. We present the protocol for a systematic review aiming to describe the content and development of CDP methods, to derive common recommendations for CDP construction.Methods and analysisThis protocol followed the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols. A literature search will be performed in PubMed (MEDLINE), Scopus, IEEE, CINAHL and EMBASE, without date restrictions, to review published papers reporting data-driven chronic CDPs quantification and visualisation methods. We will describe them using several characteristics relevant for EHD use in long-term care, grouped into three domains: (1) clinical (what clinical information does the method use and how was it considered relevant?), (2) data science (what are the method's development and implementation characteristics?) and (3) behavioural (which behaviours and interactions does the method aim to promote among users and how?). Data extraction will be performed via deductive content analysis using previously defined characteristics and accompanied by an inductive analysis to identify and code additional relevant features. Results will be presented in descriptive format and used to compare current CDPs and generate recommendations for future CDP development initiatives.Ethics and disseminationDatabase searches will be initiated in May 2019. The review is expected to be completed by February 2020. Ethical approval is not required for this review. Results will be disseminated in peer-reviewed journals and conference presentations.Prospero registration numberCRD42019140494.

Project description:BackgroundHealth and social care interventions are often complex and can be decomposed into multiple components. Multicomponent interventions are often evaluated in randomised controlled trials. Across trials, interventions often have components in common which are given alongside other components which differ across trials. Multicomponent interventions can be synthesised using component NMA (CNMA). CNMA is limited by the structure of the available evidence, but it is not always straightforward to visualise such complex evidence networks. The aim of this paper is to develop tools to visualise the structure of complex evidence networks to support CNMA.MethodsWe performed a citation review of two key CNMA methods papers to identify existing published CNMA analyses and reviewed how they graphically represent intervention complexity and comparisons across trials. Building on identified shortcomings of existing visualisation approaches, we propose three approaches to standardise visualising the data structure and/or availability of data: CNMA-UpSet plot, CNMA heat map, CNMA-circle plot. We use a motivating example to illustrate these plots.ResultsWe identified 34 articles reporting CNMAs. A network diagram was the most common plot type used to visualise the data structure for CNMA (26/34 papers), but was unable to express the complex data structures and large number of components and potential combinations of components associated with CNMA. Therefore, we focused visualisation development around representing the data structure of a CNMA more completely. The CNMA-UpSet plot presents arm-level data and is suitable for networks with large numbers of components or combinations of components. Heat maps can be utilised to inform decisions about which pairwise interactions to consider for inclusion in a CNMA model. The CNMA-circle plot visualises the combinations of components which differ between trial arms and offers flexibility in presenting additional information such as the number of patients experiencing the outcome of interest in each arm.ConclusionsAs CNMA becomes more widely used for the evaluation of multicomponent interventions, the novel CNMA-specific visualisations presented in this paper, which improve on the limitations of existing visualisations, will be important to aid understanding of the complex data structure and facilitate interpretation of the CNMA results.

Dataset Information

SuperTranscripts: a data driven reference for analysis and visualisation of transcriptomes.

Publications

SuperTranscripts: a data driven reference for analysis and visualisation of transcriptomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets