Evidence map›Paper›PMID 24625073›Full record

ArticleBMC research notes2014

Analysis options for high-throughput sequencing in miRNA expression profiling.

Tomasz Stokowy, Markus Eszlinger, Michał Świerniak, Krzysztof Fujarewicz, Barbara Jarząb, Ralf Paschke, Knut Krohn

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In one paragraph

Article in BMC research notes, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

54 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

7 authors.

Tomasz Stokowy
Markus Eszlinger
Michał Świerniak
Krzysztof Fujarewicz
Barbara Jarząb
Ralf Paschke
Knut KrohnInterdisciplinary Center for Clinical Research (IZKF), University of Leipzig, Liebigstr, 21, 04103 Leipzig, Germany. krok@medizin.uni-leipzig.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecently high-throughput sequencing (HTS) using next generation sequencing techniques became useful in digital gene expression profiling.Our study introduces analysis options for HTS data based on mapping to miRBase or counting and grouping of identical sequence reads. Those approaches allow a hypothesis free detection of miRNA differential expression.

methodsWe compare our results to microarray and qPCR data from one set of RNA samples. We use Illumina platforms for microarray analysis and miRNA sequencing of 20 samples from benign follicular thyroid adenoma and malignant follicular thyroid carcinoma. Furthermore, we use three strategies for HTS data analysis to evaluate miRNA biomarkers for malignant versus benign follicular thyroid tumors.

resultsHigh correlation of qPCR and HTS data was observed for the proposed analysis methods. However, qPCR is limited in the differential detection of miRNA isoforms. Moreover, we illustrate a much broader dynamic range of HTS compared to microarrays for small RNA studies. Finally, our data confirm hsa-miR-197-3p, hsa-miR-221-3p, hsa-miR-222-3p and both hsa-miR-144-3p and hsa-miR-144-5p as potential follicular thyroid cancer biomarkers.

conclusionsCompared to microarrays HTS provides a global profile of miRNA expression with higher specificity and in more detail. Summarizing of HTS reads as isoform groups (analysis pipeline B) or according to functional criteria (seed analysis pipeline C), which better correlates to results of qPCR are promising new options for HTS analysis. Finally, data opens future miRNA research perspectives for HTS and indicates that qPCR might be limited in validating HTS data in detail.

Indexed as

Gene Expression Regulation, NeoplasticAdenocarcinoma, FollicularBiomarkers, TumorGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansMicroRNAsOligonucleotide Array Sequence AnalysisReproducibility of ResultsReverse Transcriptase Polymerase Chain ReactionSensitivity and SpecificityThyroid NeoplasmsBiomarkers, TumorMicroRNAsMIR221, humanMIR222, humanMIRN144 microRNA, humanMIRN197 microRNA, human

Identifiers

PMID24625073
PMCPMC4007773

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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.