Dataset Information

MetaRNA-Seq: An Interactive Tool to Browse and Annotate Metadata from RNA-Seq Studies.

ABSTRACT: The number of RNA-Seq studies has grown in recent years. The design of RNA-Seq studies varies from very simple (e.g., two-condition case-control) to very complicated (e.g., time series involving multiple samples at each time point with separate drug treatments). Most of these publically available RNA-Seq studies are deposited in NCBI databases, but their metadata are scattered throughout four different databases: Sequence Read Archive (SRA), Biosample, Bioprojects, and Gene Expression Omnibus (GEO). Although the NCBI web interface is able to provide all of the metadata information, it often requires significant effort to retrieve study- or project-level information by traversing through multiple hyperlinks and going to another page. Moreover, project- and study-level metadata lack manual or automatic curation by categories, such as disease type, time series, case-control, or replicate type, which are vital to comprehending any RNA-Seq study. Here we describe "MetaRNA-Seq," a new tool for interactively browsing, searching, and annotating RNA-Seq metadata with the capability of semiautomatic curation at the study level.

SUBMITTER: Kumar P

PROVIDER: S-EPMC4561952 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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MetaRNA-Seq: An Interactive Tool to Browse and Annotate Metadata from RNA-Seq Studies.

Kumar Pankaj P Halama Anna A Hayat Shahina S Billing Anja M AM Gupta Manish M Yousri Noha A NA Smith Gregory M GM Suhre Karsten K

BioMed research international 20150825

The number of RNA-Seq studies has grown in recent years. The design of RNA-Seq studies varies from very simple (e.g., two-condition case-control) to very complicated (e.g., time series involving multiple samples at each time point with separate drug treatments). Most of these publically available RNA-Seq studies are deposited in NCBI databases, but their metadata are scattered throughout four different databases: Sequence Read Archive (SRA), Biosample, Bioprojects, and Gene Expression Omnibus (G ...[more]

PMID: 26380270

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Project description:BackgroundRNA-Seq is an increasing used methodology to study either coding and non-coding RNA expression. There are many software tools available for each phase of the RNA-Seq analysis and each of them uses different algorithms. Furthermore, the analysis consists of several steps regarding alignment (primary-analysis), quantification, differential analysis (secondary-analysis) and any tertiary-analysis and can therefore be time-consuming to deal with each step separately, in addition to requiring a computer knowledge. For this reason, the development of an automated pipeline that allows the entire analysis to be managed through a single initial command and that is easy to use even for those without computer skills can be useful. Faced with the vast availability of RNA-Seq analysis tools, it is first of all necessary to select a limited number of pipelines to include. For this purpose, we compared eight pipelines obtained by combining the most used tools and for each one we evaluated peak of RAM, time, sensitivity and specificity.ResultsThe pipeline with shorter times, lower consumption of RAM and higher sensitivity is the one consisting in HISAT2 for alignment, featureCounts for quantification and edgeR for differential analysis. Here, we developed ARPIR, an automated pipeline that recurs by default to the cited pipeline, but it also allows to choose, between different tools, those of the pipelines having the best performances.ConclusionsARPIR allows the analysis of RNA-Seq data from groups undergoing different treatment allowing multiple comparisons in a single launch and can be used either for paired-end or single-end analysis. All the required prerequisites can be installed via a configuration script and the analysis can be launched via a graphical interface or by a template script. In addition, ARPIR makes a final tertiary-analysis that includes a Gene Ontology and Pathway analysis. The results can be viewed in an interactive Shiny App and exported in a report (pdf, word or html formats). ARPIR is an efficient and easy-to-use tool for RNA-Seq analysis from quality control to Pathway analysis that allows you to choose between different pipelines.

Project description:BackgroundGene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative.ResultsWe present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species.ConclusionsFINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision-ideal for bench researchers with limited experience in handling computational tools.

Dataset Information

MetaRNA-Seq: An Interactive Tool to Browse and Annotate Metadata from RNA-Seq Studies.

Publications

MetaRNA-Seq: An Interactive Tool to Browse and Annotate Metadata from RNA-Seq Studies.

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