Evidence map›Paper›PMID 19619282›Full record

ArticleBMC genomics2009

A comparison of RNA amplification techniques at sub-nanogram input concentration.

Julie E Lang, Mark Jesus M Magbanua, Janet H Scott, G Mike Makrigiorgos, Gang Wang, Scot Federman, Laura J Esserman, John W Park, Christopher M Haqq

Abstract readComparative Study
In one paragraph

Article in BMC genomics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Trial
  2. Parasite-derived microRNAs as a diagnostic biomarker: potential roles, characteristics, and limitations.Journal of parasitic diseases : official organ of the Indian Society for Parasitology · 2021
    Review
  3. Review
  4. Review
  5. Article
  6. Article
  7. Single-Cell Isolation and Gene Analysis: Pitfalls and Possibilities.International journal of molecular sciences · 2015
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. 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

9 authors.

Julie E LangDepartment of Surgery, UCSF Comprehensive Cancer Center, 1500 Divisadero Street, San Francisco, CA 94143, USA. jlang@azcc.arizona.edu
Mark Jesus M Magbanua
Janet H Scott
G Mike Makrigiorgos
Gang Wang
Scot Federman
Laura J Esserman
John W Park
Christopher M Haqq

Funding

UCSF PROSTATE CANCER SPOREP50CA089520 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CARROLL, PETER R · 2000 to 2008
$13.9M
PHAGE ANTIBODY BASED PROBES OF ERBB RECEPTOR FUNCTIONU54CA090788 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PARK, JOHN W · 2001 to 2006
$5.6M
Molecular Effects of Nutrition Supplements in ProstateR01CA101042 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CARROLL, PETER R · 2003 to 2007
$1.7M
NCI NIH HHS P50 CA089520NCI NIH HHS P50 CA89520NCI NIH HHS R01 CA101042NCI NIH HHS R01 CA101042-01NCI NIH HHS U54 CA090788NCI NIH HHS U54 CA90788
6 · The paper itself

Abstract

backgroundGene expression profiling of small numbers of cells requires high-fidelity amplification of sub-nanogram amounts of RNA. Several methods for RNA amplification are available; however, there has been little consideration of the accuracy of these methods when working with very low-input quantities of RNA as is often required with rare clinical samples. Starting with 250 picograms-3.3 nanograms of total RNA, we compared two linear amplification methods 1) modified T7 and 2) Arcturus RiboAmp HS and a logarithmic amplification, 3) Balanced PCR. Microarray data from each amplification method were validated against quantitative real-time PCR (QPCR) for 37 genes.

resultsFor high intensity spots, mean Pearson correlations were quite acceptable for both total RNA and low-input quantities amplified with each of the 3 methods. Microarray filtering and data processing has an important effect on the correlation coefficient results generated by each method. Arrays derived from total RNA had higher Pearson's correlations than did arrays derived from amplified RNA when considering the entire unprocessed dataset, however, when considering a gene set of high signal intensity, the amplified arrays had superior correlation coefficients than did the total RNA arrays.

conclusionGene expression arrays can be obtained with sub-nanogram input of total RNA. High intensity spots showed better correlation on array-array analysis than did unfiltered data, however, QPCR validated the accuracy of gene expression array profiling from low-input quantities of RNA with all 3 amplification techniques. RNA amplification and expression analysis at the sub-nanogram input level is both feasible and accurate if data processing is used to focus attention to high intensity genes for microarrays or if QPCR is used as a gold standard for validation.

Indexed as

Cell LineGene Expression ProfilingHumansNucleic Acid Amplification TechniquesOligonucleotide Array Sequence AnalysisPolymerase Chain ReactionRNASensitivity and SpecificityRNA

Identifiers

PMID19619282
PMCPMC2724417

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.