Evidence map›Paper›PMID 20459774›Full record

ArticleBMC biology2010

Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls.

Daniela Witten, Robert Tibshirani, Sam Guoping Gu, Andrew Fire, Weng-Onn Lui

Open access · goldAbstract read
In one paragraph

Article in BMC biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed, 1 pooled it
5.4field-weighted citation impact, top 4% 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

94 citing papers in PubMed, 1 synthesis or guideline pooled it, 265 citations in OpenAlex.

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  16. IFN-I inducibleFrontiers in immunology · 2022
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34 more citing papers are in PubMed but not listed here.

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 at 3 institutions in 2 countries.

Daniela WittenDepartment of Statistics, Stanford University, Stanford, California 94305-4065, USA.
Robert Tibshirani
Sam Guoping Gu
Andrew Fire
Weng-Onn Lui
Stanford University · USKarolinska University Hospital · SEStanford Health Care · US

Funding

NHLBI PROTEOMICS INITIATIVE-N01HV28183N01HV028183 · HV · STANFORD UNIVERSITY · PI NOLAN, GARRY P · 2002 to 2006
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NHLBI NIH HHS N01-HV-28183
6 · The paper itself

Abstract

backgroundUltra-high throughput sequencing technologies provide opportunities both for discovery of novel molecular species and for detailed comparisons of gene expression patterns. Small RNA populations are particularly well suited to this analysis, as many different small RNAs can be completely sequenced in a single instrument run.

resultsWe prepared small RNA libraries from 29 tumour/normal pairs of human cervical tissue samples. Analysis of the resulting sequences (42 million in total) defined 64 new human microRNA (miRNA) genes. Both arms of the hairpin precursor were observed in twenty-three of the newly identified miRNA candidates. We tested several computational approaches for the analysis of class differences between high throughput sequencing datasets and describe a novel application of a log linear model that has provided the most effective analysis for this data. This method resulted in the identification of 67 miRNAs that were differentially-expressed between the tumour and normal samples at a false discovery rate less than 0.001.

conclusionsThis approach can potentially be applied to any kind of RNA sequencing data for analysing differential sequence representation between biological sample sets.

Indexed as

Cluster AnalysisComputational BiologyDNA, ComplementaryGene Expression ProfilingGene LibraryLinear ModelsMicroRNAsSequence Analysis, DNADNA, ComplementaryMicroRNAs

Identifiers

PMID20459774
PMCPMC2880020
OpenAlexW2030572260

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.