Evidence map›Paper›PMID 12907751›Full record

ArticleNucleic acids research2003

Regionalized GC content of template DNA as a predictor of PCR success.

Yair Benita, Ronald S Oosting, Martin C Lok, Michael J Wise, Ian Humphery-Smith

Open access · bronzeAbstract read
In one paragraph

Article in Nucleic acids research, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

29 citing papers in PubMed, 129 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

5 authors at 1 institution in 1 country.

Yair BenitaDepartment of Pharmaceutical Proteomics, Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Sorbonnelaan 16, Utrecht, The Netherlands. y.benita@pharm.uu.nl
Ronald S Oosting
Martin C Lok
Michael J Wise
Ian Humphery-Smith
Utrecht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A set of 1438 human exons was subjected to nested PCR. The initial success rate using a standard PCR protocol required for ligation-independent cloning was 83.4%. Logistic regression analysis was conducted on 27 primer- and template-related characteristics, of which most could be ignored apart from those related to the GC content of the template. Overall GC content of the template was a good predictor for PCR success; however, specificity and sensitivity values for predicted outcome were improved to 84.3 and 94.8%, respectively, when regionalized GC content was employed. This represented a significant improvement in predictability with respect to GC content alone (P < 0.001; chi(2)) and is expected to increase in relative sensitivity as template size increases. Regionalized GC was calculated with respect to a threshold of 61% GC content and a sliding window of 21 bp across the target sequence. Fine-tuning of PCR conditions is not practicable for all target sequences whenever a large number of genes of different lengths and GC content are to be amplified in parallel, particularly if total open reading frame or domain coverage is essential for recombinant protein synthesis. Thus, the present method is proposed as a means of grouping subsets of genes possessing potentially difficult target sequences so that PCR conditions can be optimized separately in order to obtain improved outcomes.

Indexed as

Analysis of VarianceBase CompositionDNADNA PrimersGC Rich SequenceHumansLogistic ModelsPolymerase Chain ReactionSensitivity and SpecificityTemperatureTemplates, GeneticDNADNA Primers

Identifiers

PMID12907751
PMCPMC169991
OpenAlexW2042071496

What OpenQuestion holds

Textmetadata
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.