The long game: Bringing together PCR and big data

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The long game: Bringing together PCR and big data

A team from University of Idaho and Oberlin College has developed an approach that uses long-range PCR (also known as long PCR) to generate templates from any DNA source for next-generation sequencing, creating large data sets. As an example, the team created 58 universal primers that could be used to amplify complete chloroplast genomes (plastomes) in flowering plants (angiosperms). The protocol is available for free viewing in the January 2014 issue of Applications in Plant Sciences.


The long-range PCR technique means that researchers can create larger fragments of DNA than with most traditional PCR techniques. In this study, the researchers generated 3-15 kilobase fragments for 30 species, and then amplified the chloroplast regions in the plants, including several pine species. They then sequenced 15 complete chloroplast genomes using next-generation sequencing techniques.

According to Simon Uribe-Convers, graduate student and lead author, the technique has broader application than just within plastomes: “This can easily be expanded to mitochondrial and nuclear regions, and can be used in combination with any next-generation sequencing platform. Furthermore, this approach is not restricted to plant studies, but will be useful for any organism.”

As Uribe-Convers explains, this method could change how future systematic studies are conducted, by providing researchers with a way to target regions of interest in their study organism, such as single-copy regions of the nuclear genome or portions of organellar genomes. As new PCR (polymerase chain reaction) techniques and next-generation sequencing processes become faster, more efficient and lower cost, these will generate huge amounts of data.

“We want to help move the field of systematics into the realm of big data, and we hope that our approach contributes to that,” says Uribe-Convers.

The use of better and quicker big data techniques and data analytics will help researchers make the most of this data, for example in increasing the accuracy in reconstructing the evolutionary history of organisms, or finding out more about disease.

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