Evidence map›Paper›PMID 26758513›Full record

ArticleBMC genomics2016

CANEapp: a user-friendly application for automated next generation transcriptomic data analysis.

Dmitry Velmeshev, Patrick Lally, Marco Magistri, Mohammad Ali Faghihi

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
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

7 citing papers in PubMed, 14 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Dmitry VelmeshevDepartment of Psychiatry, University of Miami Miller School of Medicine, Miami, FL, 33136, USA. dvelmeshev@med.miami.edu.
Patrick LallyDepartment of Psychiatry, University of Miami Miller School of Medicine, Miami, FL, 33136, USA. p.lally1@umiami.edu.
Marco MagistriDepartment of Psychiatry, University of Miami Miller School of Medicine, Miami, FL, 33136, USA. mmagistri@med.miami.edu.
Mohammad Ali FaghihiDepartment of Psychiatry, University of Miami Miller School of Medicine, Miami, FL, 33136, USA. MFaghihi@med.miami.edu.
University of Miami · US

Funding

Antisense RNA Mediated Epigenetic Regulation of Brain Derived Neurotrophic FactorR01NS081208 · NINDS · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI FAGHIHI, MOHAMMAD ALI · 2013 to 2017
$1.7M
NINDS NIH HHS R01 NS081208NINDS NIH HHS R01NS081208-01A1
6 · The paper itself

Abstract

backgroundNext generation sequencing (NGS) technologies are indispensable for molecular biology research, but data analysis represents the bottleneck in their application. Users need to be familiar with computer terminal commands, the Linux environment, and various software tools and scripts. Analysis workflows have to be optimized and experimentally validated to extract biologically meaningful data. Moreover, as larger datasets are being generated, their analysis requires use of high-performance servers.

resultsTo address these needs, we developed CANEapp (application for Comprehensive automated Analysis of Next-generation sequencing Experiments), a unique suite that combines a Graphical User Interface (GUI) and an automated server-side analysis pipeline that is platform-independent, making it suitable for any server architecture. The GUI runs on a PC or Mac and seamlessly connects to the server to provide full GUI control of RNA-sequencing (RNA-seq) project analysis. The server-side analysis pipeline contains a framework that is implemented on a Linux server through completely automated installation of software components and reference files. Analysis with CANEapp is also fully automated and performs differential gene expression analysis and novel noncoding RNA discovery through alternative workflows (Cuffdiff and R packages edgeR and DESeq2). We compared CANEapp to other similar tools, and it significantly improves on previous developments. We experimentally validated CANEapp's performance by applying it to data derived from different experimental paradigms and confirming the results with quantitative real-time PCR (qRT-PCR). CANEapp adapts to any server architecture by effectively using available resources and thus handles large amounts of data efficiently. CANEapp performance has been experimentally validated on various biological datasets. CANEapp is available free of charge at http://psychiatry.med.miami.edu/research/laboratory-of-translational-rna-genomics/CANE-app .

conclusionsWe believe that CANEapp will serve both biologists with no computational experience and bioinformaticians as a simple, timesaving but accurate and powerful tool to analyze large RNA-seq datasets and will provide foundations for future development of integrated and automated high-throughput genomics data analysis tools. Due to its inherently standardized pipeline and combination of automated analysis and platform-independence, CANEapp is an ideal for large-scale collaborative RNA-seq projects between different institutions and research groups.

Indexed as

SoftwareGene Expression ProfilingGenomicsHigh-Throughput Nucleotide SequencingInternetRNAUser-Computer InterfaceRNA

Identifiers

PMID26758513
PMCPMC4710974
OpenAlexW2237954112

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.