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Massive parallel sequencing or massively parallel sequencing is any of several high-throughput approaches to DNA sequencing using the concept of massively parallel processing; it is also called next-generation sequencing (NGS) or second-generation sequencing.
These types of false-positive variants are filtered out by the duplex sequencing method since mutations need to be accurately matched in both strands of DNA to be validated as true mutations. Duplex sequencing can theoretically detect mutations with frequencies as low as 10 −8 compared to the 10 −2 rate of standard NGS methods. [1] [2] [10]
Current DNA sequencing technologies, including NGS, are limited on the basis that genomes are much larger than any read length. Typically, NGS operate with small reads, less than 400 bp, and have a much lower cost per read than previous first generation machines. They are also simpler to operate with higher parallel operation and higher yield. [3]
SNV calling from NGS data is any of a range of methods for identifying the existence of single nucleotide variants (SNVs) from the results of next generation sequencing (NGS) experiments. These are computational techniques, and are in contrast to special experimental methods based on known population-wide single nucleotide polymorphisms (see ...
During sequencing, each base in the template is sequenced twice, and the resulting data are decoded according to this scheme. SOLiD (Sequencing by Oligonucleotide Ligation and Detection) is a next-generation DNA sequencing technology developed by Life Technologies and has been commercially available since
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One type of sequencing method can be used in preference to another depending on the type of the sample, for a genomic sample assembly-based methods is used; for a metagenomic sample it is preferable to use read-based methods. [10] Metagenomic sequencing methods have provided better results than genomics, due to these present fewer false negatives.
These methods represented an important step forward in sequence assembly, as they both use algorithms to reach a global optimum instead of a local optimum. While both of these methods made progress towards better assemblies, the De Bruijn graph method has become the most popular in the age of next-generation sequencing.