Scientific Publications

High-quality draft assemblies of mammalian genomes from massively parallel sequence data.

Publication TypeJournal Article
AuthorsGnerre, S., Maccallum I., Przybylski D., Ribeiro FJ, Burton JN, Walker BJ, Sharpe T., Hall G., Shea TP, Sykes S., Berlin AM, Aird D., Costello M., Daza R., Williams L., Nicol R., Gnirke A., Nusbaum C., Lander E. S., and Jaffe DB
AbstractMassively parallel DNA sequencing technologies are revolutionizing genomics by making it possible to generate billions of relatively short (~100-base) sequence reads at very low cost. Whereas such data can be readily used for a wide range of biomedical applications, it has proven difficult to use them to generate high-quality de novo genome assemblies of large, repeat-rich vertebrate genomes. To date, the genome assemblies generated from such data have fallen far short of those obtained with the older (but much more expensive) capillary-based sequencing approach. Here, we report the development of an algorithm for genome assembly, ALLPATHS-LG, and its application to massively parallel DNA sequence data from the human and mouse genomes, generated on the Illumina platform. The resulting draft genome assemblies have good accuracy, short-range contiguity, long-range connectivity, and coverage of the genome. In particular, the base accuracy is high (≥99.95%) and the scaffold sizes (N50 size = 11.5 Mb for human and 7.2 Mb for mouse) approach those obtained with capillary-based sequencing. The combination of improved sequencing technology and improved computational methods should now make it possible to increase dramatically the de novo sequencing of large genomes. The ALLPATHS-LG program is available at http://www.broadinstitute.org/science/programs/genome-biology/crd.
Year of Publication2011
JournalProceedings of the National Academy of Sciences of the United States of America
Volume108
Issue4
Pages1513-8
Date Published (YYYY/MM/DD)2011/01/25
ISSN Number0027-8424
DOI10.1073/pnas.1017351108
PubMedhttp://www.ncbi.nlm.nih.gov/pubmed/21187386?dopt=Abstract