Article

Generations of sequencing technologies.

Department of Gene Technology, Royal Institute of Technology (KTH), AlbaNova University Center, Roslagstullsbacken 21, SE-10691 Stockholm, Sweden.
Genomics (Impact Factor: 2.79). 12/2008; 93(2):105-11. DOI: 10.1016/j.ygeno.2008.10.003
Source: PubMed

ABSTRACT Advancements in the field of DNA sequencing are changing the scientific horizon and promising an era of personalized medicine for elevated human health. Although platforms are improving at the rate of Moore's Law, thereby reducing the sequencing costs by a factor of two or three each year, we find ourselves at a point in history where individual genomes are starting to appear but where the cost is still too high for routine sequencing of whole genomes. These needs will be met by miniaturized and parallelized platforms that allow a lower sample and template consumption thereby increasing speed and reducing costs. Current massively parallel, state-of-the-art systems are providing significantly improved throughput over Sanger systems and future single-molecule approaches will continue the exponential improvements in the field.

1 Bookmark
 · 
121 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: With the decrease in sequencing cost and the rise of companies providing sequencing services, it is likely that personalized whole-genome sequencing will eventually become an instrument of common medical practice. We write this series of three reviews to help non-geneticist clinicians get ready for the major breakthroughs that are likely to occur in the coming years in the fast-moving field of personalized medicine. This first paper focuses on the fundamental concepts of molecular genetics. We review how recombination occurs during meiosis, how de novo genetic variations including single nucleotide polymorphisms (SNPs), insertions and deletions are generated and how they are inherited from one generation to the next. We detail how genetic variants can impact protein expression and function, and summarize the main characteristics of the human genome. We also explain how the achievements of the Human Genome Project, the HapMap Project, and more recently, the 1000 Genomes Project, have boosted the identification of genetic variants contributing to common diseases in human populations. The second and third papers will focus on genetic epidemiology and clinical applications in personalized medicine.
    Current Psychiatry Reviews 07/2014; 10(2).
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Next-generation sequencing technologies are extensively used in many fields of biology. One of the problems, related to the utilization of this kind of data, is the analysis of raw sequence quality and removal (trimming) of low-quality segments while retaining sufficient information for subsequent analyses. Here, we present a series of methods useful for converting and for refinishing one or more sequence files. One of the methods proposed, based on dynamic trimming, as implemented in the software StreamingTrim allows a fast and accurate trimming of sequence files, with low memory requirement.
    Methods in Molecular Biology 01/2015; 1231:137-49. · 1.29 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The inventions of microarray and next generation sequencing technologies have revolutionized research in genomics; platforms have led to massive amount of data in gene expression, methylation, and protein-DNA interactions. A common theme among a number of biological problems using high-throughput technologies is differential analysis. Despite the common theme, different data types have their own unique features, creating a "moving target" scenario. As such, methods specifically designed for one data type may not lead to satisfactory results when applied to another data type. To meet this challenge so that not only currently existing data types but also data from future problems, platforms, or experiments can be analyzed, we propose a mixture modeling framework that is flexible enough to automatically adapt to any moving target. More specifically, the approach considers several classes of mixture models and essentially provides a model-based procedure whose model is adaptive to the particular data being analyzed. We demonstrate the utility of the methodology by applying it to three types of real data: gene expression, methylation, and ChIP-seq. We also carried out simulations to gauge the performance and showed that the approach can be more efficient than any individual model without inflating type I error.
    Computational and Mathematical Methods in Medicine 01/2014; 2014:758718. · 1.02 Impact Factor

Preview

Download
1 Download
Available from