DNA sequencing enables biomedical discovery. Next-generation DNA sequencers such as the 454 platform provide cost-effective alternatives to traditional Sanger sequencing. All sequencing platforms rely on sophisticated assembly software to transform a collection of reads into chromosome-length sequences. Celera Assembler is a premier assembler that has remained at the forefront of genomics for 10 years. It has enabled ground-breaking publications of eukaryotic genomes and metagenomic samples sequenced by Sanger chemistry. Now, the Celera Assembler also supports the 454 technology. Celera Assembler is the software most aggressive at contig construction and repeat resolution from 454 read and mate pair data sets. Celera Assembler is the only assembler that handles the large data sets involved in human-genome-scale projects. Renewed funding will enable several important algorithms extensions. The software will show improved performance and yield on Sanger, 454, and hybrid data sets. The software will gain capability to exploit short-read next-generation data during assembly of longer reads. It will gain the ability to incorporate reference sequences as an assist to de novo assembly. Its data model will be refined for increased precision on real data. The software will be able to integrate non-shotgun reads generated by directed sequencing or other means. It will enhance metagenomics assembly of environmental samples. Software engineering improvements will assist the algorithms development. To enable the wider adoption of the Celera Assembler, the project will include documentation, training, end-user support, and support for integration with data sources and analysis tools.
/ Relevance to Public Health This research will enable scientists to study basic biology by helping them decipher the DNA sequences of entire genomes. This research is necessary because current sequencing technology reads very few DNA bases at a time. Sophisticated software is required to reconstruct the chromosome-length sequences that allow biologists to recognize genes, pathways, evolutionary trees, and ecosystem interdependencies.
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