PCR Biosystems has launched a new proofreading polymerase mix for NGS library preparation workflows. VeriFi™ Library Amplification Mix combines the powerful and robust performance of VeriFi™ Hot Start Polymerase with greatly reduced GC-dependent bias to push the limits of NGS data. Guaranteeing quality when sequencing long and complex targets, VeriFi™ Library Amplification Mix delivers market-leading performance for precise PCR.

Now, with VeriFi™ Library Amplification Mix, researchers can be more confident than ever before that they are acquiring superior quality sequencing datasets containing a higher number of unique reads.NGS enables researchers to quickly sequence entire genomes and transcriptomes of organisms. This technology unlocks insights into complex biological processes, ultimately enabling the development of targeted treatments and more effective vaccines and contributing to a better understanding of the natural world. Library amplification is a critical step of PCR-based NGS workflows.

However, PCR is susceptible to GC bias, which compromises the quality of sequencing libraries and in turn negatively impacts resulting data quality. Researchers, therefore, typically look to reduce GC bias during NGS library amplification, so they can be confident in the insights they elucidate from the data generated.VeriFi™ Library Amplification Mix has been specially engineered to reduce GC-bias. Incorporating the proven VeriFi™ Hot Start Polymerase with AptaLock™ hot start technology, the mix also utilises proprietary chemistry to minimise GC-bias and enable reliable amplification for sequencing workflows.

Having undergone rigorous testing on IlluminaÒplatforms, the mix was shown to have minimal bias, even during amplification at extremes of GC content ranging from 30 .In an external, blind experiment with three market-leading competitors, the researcher running the study picked VeriFi™ Library Amplification Mix as the best performing and said it would be their mix of choice. They reported that using the VeriFi™ mix allowed the detection of 2% more unique reads, which translates to 80,000-25,000,000 unique reads, depending on the sequencing platform. More unique reads are a significant benefit, leading to greater target sequence coverage in whole genome sequencing studies and more accurate quantitative information, thereby making RNA-Seq experiments more informative.