Evidence map›Paper›PMID 26169944›Full record

ArticleJournal of translational medicine2015

miFRame: analysis and visualization of miRNA sequencing data in neurological disorders.

Christina Backes, Jan Haas, Petra Leidinger, Karen Frese, Thomas Großmann, Klemens Ruprecht, Benjamin Meder, Eckart Meese, Andreas Keller

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 34% 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

5 citing papers in PubMed, 12 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

9 authors at 4 institutions in 2 countries.

Christina BackesChair for Clinical Computational Biology, Saarland University, Saarbrücken, Germany. c.backes@mx.uni-saarland.de.
Jan HaasInternal Medicine III, Heidelberg University, Heidelberg, Germany. jan.haas@med.uni-heidelberg.de.
Petra LeidingerDepartment of Human Genetics, Saarland University, Saarbrücken, Germany. p.leidinger@mx.uni-saarland.de.
Karen FreseInternal Medicine III, Heidelberg University, Heidelberg, Germany. karen.frese@med.uni-heidelberg.de.
Thomas GroßmannChair for Clinical Computational Biology, Saarland University, Saarbrücken, Germany. thomas.grossmann@ccb.uni-saarland.de.
Klemens RuprechtNeuroinflammation, Charitee, Berlin, Germany. Klemens.Ruprecht@charite.de.
Benjamin MederInternal Medicine III, Heidelberg University, Heidelberg, Germany. benjamin.meder@med.uni-heidelberg.de.
Eckart MeeseDepartment of Human Genetics, Saarland University, Saarbrücken, Germany. hgemee@uks.eu.
Andreas KellerChair for Clinical Computational Biology, Saarland University, Saarbrücken, Germany. andreas.keller@ccb.uni-saarland.de.
Saarland University · DEGerman Society of Surgery · DECharité - Universitätsmedizin Berlin · DEInstitute of Human Genetics · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile in the past decades nucleic acid analysis has been predominantly carried out using quantitative low- and high-throughput approaches such as qRT-PCR and microarray technology, next-generation sequencing (NGS) with its single base resolution is now frequently applied in DNA and RNA testing. Especially for small non-coding RNAs such as microRNAs there is a need for analysis and visualization tools that facilitate interpretation of the results also for clinicians.

methodsWe developed miFRame, which supports the analysis of human small RNA NGS data. Our tool carries out different data analyses for known as well as predicted novel mature microRNAs from known precursors and presents the results in a well interpretable manner. Analyses include among others expression analysis of precursors and mature miRNAs, detection of novel precursors and detection of potential iso-microRNAs. Aggregation of results from different users moreover allows for evaluation whether remarkable results, such as novel mature miRNAs, are indeed specific for the respective experimental set-up or are frequently detected across a broad range of experiments.

resultsWe demonstrate the capabilities of miFRame, which is freely available at http://www.ccb.uni-saarland.de/miframe on two studies, circulating biomarker screening for Multiple Sclerosis (cohort includes clinically isolated syndrome, relapse remitting MS, matched controls) as well as Alzheimer Disease (cohort includes Alzheimer Disease, Mild Cognitive Impairment, matched controls). Here, our tool allowed for an improved biomarker discovery by identifying likely false positive marker candidates.

Indexed as

SoftwareAlzheimer DiseaseBase SequenceComputational BiologyGene Expression ProfilingHumansMicroRNAsMultiple SclerosisNervous System DiseasesProtein IsoformsSequence Analysis, RNAMicroRNAsProtein Isoforms

Identifiers

PMID26169944
PMCPMC4501052
OpenAlexW1560889834

What OpenQuestion holds

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LicenceCC BY
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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.