by Next generation sequencing (NGS), also known as high-throughput sequencing, refers to modern sequencing technologies that are capable of sequencing a large number of DNA strands in parallel. These technologies produce millions of sequences simultaneously, allowing researchers to sequence whole genomes and transcriptomes faster and more cost-effectively than previous Sanger sequencing methods. Some key advantages of NGS include higher throughput, lower cost per base, and the ability to sequence whole genomes or transcriptomes in a single run.
Sequencing technologies built upon earlier “first generation” techniques like Sanger sequencing techniques. Where Sanger sequencing involved sequencing short fragments of DNA one by one, Next Generation Sequencing allows researchers to sequence millions of fragments in parallel. This massive parallelization has enabled genome sequencing projects to become feasible that previously were not due to the high cost and time required using earlier methods.
Platforms for Next Generation Sequencing
There are several major sequencing platforms that are commonly used for NGS applications. Some of the most widely used platforms include:
– Illumina sequencing platforms like the HiSeq, MiSeq and NextSeq systems. Illumina sequencers are the most commonly used and are capable of generating terabases of sequence data in a single run.
– Ion Torrent sequencing from Life Technologies. Ion Torrent systems detect hydrogen ions released during DNA polymerization rather than fluorescence, providing fast, affordable sequencing.
– PacBio & Oxford Nanopore sequencing platforms. These platforms generate long reads by directly observing single molecules of DNA or RNA being synthesized. They are capable of generating incredibly long reads up to 1 million base pairs.
– BGISEQ sequencing from MGI Tech (formerly Complete Genomics). The BGISEQ platform utilizes a DNA nanoball-based approach to sequence many fragments of a genome in parallel.
Each platform has its own characteristics like read length, accuracy, throughput and cost. Choosing the right platform depends on factors like budget, required output and desired applications.
Applications of Next Generation Sequencing
There are a huge number of applications that have benefitted from NGS technologies. Here are some of the most common uses:
– Whole genome sequencing – Fully sequencing the complete DNA sequences of organisms to study genetic variation. This has aided in personalized medicine efforts.
– Transcriptome analysis – Sequencing of RNA to analyze gene expression profiles and discover novel transcripts. RNA-Seq is a revolutionary application of NGS.
– Epigenetics – Studying DNA methylation patterns and histone modifications through techniques like bisulfite sequencing and ChIP-Seq.
– Microbiome analysis – Characterizing microbial communities through 16S rRNA or whole genome metagenomic sequencing. This has shed light on microbiomes in the human body and environment.
– Cancer genomics – Discovering mutational signatures, somatic variants and transcriptomic changes driving tumorigenesis through use of NGS on clinical cancer samples.
– Evolutionary studies – Inferring phylogenies through comparison of complete genome sequences from related species. NGS provides a wealth of genetic markers.
– Non-invasive prenatal testing – Fetal DNA analysis from maternal blood samples through techniques like sequencing of cell-free DNA for detection of fetal chromosomal abnormalities.
Limitations and Future Directions
While revolutionary, next generation sequencing technologies still have some limitations. Read lengths are generally still quite short for some applications. Whole genome assembly remains a challenge for organisms with repetitive regions or high heterozygosity. Data analysis and storage also presents a significant computational burden.
Developing technologies that provide longer, more accurate reads at lower cost. Third generation long-read sequencing approaches like Nanopore sequencing are making long read assembly more feasible. Portable, affordable sequencers also promise to decentralize NGS applications. Advances in computational infrastructure will be needed to analyze bigger genomic datasets. Overall, NGS will continue revolutionizing biomedicine, agriculture and basic research in the years ahead.
