June 18, 2008

Scanning the human genome at kilobase resolution

This is yet another step for higher resolution study of human variation. In the current crop of association or population studies, scientists use microarrays to examine a few hundred thousand SNPs or copy number variations. The end goal is to read all bases in a person's genome (full genome sequencing). The cost of these two technologies is at least two orders of magnitude apart. This paper proposes to offer a more thorough scan of the human genome, about an order of magnitude higher than current techniques.

Genome Res. 2008 May;18(5):751-62. Epub 2008 Feb 21.

Scanning the human genome at kilobase resolution.

Chen J, Kim YC, Jung YC, Xuan Z, Dworkin G, Zhang Y, Zhang MQ, Wang SM.

Normal genome variation and pathogenic genome alteration frequently affect small regions in the genome. Identifying those genomic changes remains a technical challenge. We report here the development of the DGS (Ditag Genome Scanning) technique for high-resolution analysis of genome structure. The basic features of DGS include (1) use of high-frequent restriction enzymes to fractionate the genome into small fragments; (2) collection of two tags from two ends of a given DNA fragment to form a ditag to represent the fragment; (3) application of the 454 sequencing system to reach a comprehensive ditag sequence collection; (4) determination of the genome origin of ditags by mapping to reference ditags from known genome sequences; (5) use of ditag sequences directly as the sense and antisense PCR primers to amplify the original DNA fragment. To study the relationship between ditags and genome structure, we performed a computational study by using the human genome reference sequences as a model, and analyzed the ditags experimentally collected from the well-characterized normal human DNA GM15510 and the leukemic human DNA of Kasumi-1 cells. Our studies show that DGS provides a kilobase resolution for studying genome structure with high specificity and high genome coverage. DGS can be applied to validate genome assembly, to compare genome similarity and variation in normal populations, and to identify genomic abnormality including insertion, inversion, deletion, translocation, and amplification in pathological genomes such as cancer genomes.


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