Enabling personal genomics: discovering and understanding genetic variation

高通量测序(HTS)技术，如Illumina/Solexa和AB Solid，能够每周对完整的人类基因组进行测序，成本比以前的方法低200倍。由此产生的数据由大约35-120个核苷酸片段组成，来自基因组中的未知位置。对这些数据集的分析带来了前所未有的信息学挑战，这既是因为一台HTS机器一次运行就能产生大量的读数，也因为这些读数比以前可用的时间要短得多。在过去几年里，短读数据集的分析方法已经开始出现，虽然科学界免费提供的可靠工具即将解决一些问题，但仍然存在许多挑战。根据这项提议，我计划开发分析HTS数据的方法，以发现和理解基因组变异，并将这些方法应用于对Ciona savignyi模式生物的研究。

High-throughput sequencing (HTS) technologies, such as Illumina/Solexa and AB SOLiD, are able to sequence a full human genome per week at a cost 200-fold less than previous methods. The resulting data consist of pieces - reads - about 35-120 nucleotides long, from unknown locations in the genome. Analysis of these datasets poses an unprecedented informatics challenge, both because of the sheer number of reads that a single run of an HTS machine can produce, and because the reads are significantly shorter than previously available. Methods for the analysis of short-read datasets have started to become available over the last few years, and while some of the problems are close to being solved with reliable tools freely available to the scientific community, there are still many challenges remaining. Under this proposal I plan to develop methodologies to analyze HTS data in order to discover and understand genomic variants, and to apply these methods towards the study of the Ciona savignyi model organism.