One of the steps in DNA sample preparation that is often overlooked when moving from manual to automated methods, is the quantification and normalization of nucleic acid samples that are destined for downstream analysis in different techniques and applications such as genotyping and NGS.
Next-generation sequencing (NGS*) has revolutionized genomic research, allowing entire genomes to be sequenced in a single day. This has led to massive advances in the diagnosis, prognosis and treatment of disease, answering genetic questions from a wide spectrum of applications and biological systems. Today, NGS is an essential tool for any biologist. Ultra-high throughput NGS solutions have a wide range of applications and are fully scalable—from rapid SNP genotyping of a single individual, to whole genome sequencing (WGS) of entire populations. The explosive demand for NGS often creates pressures upstream to process many more samples and prepare high quality DNA to feed into library prep and analysis. In this article we explore the ideal DNA requirements for NGS and look at some of the most critical parameters for developing an automated nucleic acid extraction workflow.
With the advent of next-generation sequencing (NGS), the field of metagenomics has exploded in recent years, as scientists are now able to study microbes as communities instead of individual organisms. This has revolutionized our understanding of the relationships between microbiota, human health, and the environment.
Next-generation sequencing (NGS) is driving dramatic progress in many fields of research. However, the value of NGS data is often limited by factors such as poor analysis pipelines and poor library quality. One way to improve the quality of your libraries is to optimize your NGS library prep, but this can be challenging, as the process involves multiple steps that can introduce user-user variability and the risk of contamination.
Next-generation sequencing (NGS) has generated a raft of new developments and discoveries. However, NGS is a complex process, and scientists face many technical difficulties throughout the workflow. NGS sample preparation, for example, can be a significant source of inefficiencies that could hinder your research and stifle your progress by wasting resources and increasing costs. So, what can you do to improve your sample preparation efficiency?
Next-generation sequencing (NGS) is helping to advance genomics research at an unprecedented rate. However, the process can be technically challenging, and any errors can significantly impact the reliability and accuracy of your results. NGS library preparation and QC can have a major impact on your success, especially as poor-quality libraries can skew your results and reduce the accuracy of your data.