Evidence map›Paper›PMID 33349239›Full record

ArticleBMC bioinformatics2020

ARPIR: automatic RNA-Seq pipelines with interactive report.

Giulio Spinozzi, Valentina Tini, Alessia Adorni, Brunangelo Falini, Maria Paola Martelli

Abstract read
In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
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  10. LOXL3 Silencing Affected Cell Adhesion and Invasion in U87MG Glioma Cells.International journal of molecular sciences · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Giulio SpinozziDepartment of Medicine, Section of Hematology, University of Perugia, Perugia, Italy. giulio.spinozzi@unipg.it.ORCID http://orcid.org/0000-0002-4220-2474
Valentina TiniDepartment of Medicine, Section of Hematology, University of Perugia, Perugia, Italy.
Alessia AdorniDepartment of Medicine, Section of Hematology, University of Perugia, Perugia, Italy.
Brunangelo FaliniDepartment of Medicine, Section of Hematology, University of Perugia, Perugia, Italy.
Maria Paola MartelliDepartment of Medicine, Section of Hematology, University of Perugia, Perugia, Italy. maria.martelli@unipg.it.

Funding

CINECA HP10CZ1LLUELIXIR-IIB ELIX4_spinozziEuropean Research Council 725725
6 · The paper itself

Abstract

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.

Indexed as

RNA-SeqSoftwareAutomationDatabases, GeneticGene OntologyHumansSequence AlignmentBioinformaticsDEAGene ontologyGenomicsPathwaysPipelinesRNA-seq

Identifiers

PMID33349239
PMCPMC7751108

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.