John Healy

Error Tolerant Indexing and Alignment of Short Reads with Covering Template Families

The rapid adoption of high-throughput next generation sequence data in biological research is presenting a major challenge for sequence alignment tools—specifically, the efficient alignment of vast amounts of short reads to large references in the presence of differences arising from sequencing errors and biological sequence variations. To address this challenge, we developed a short read aligner for high-throughput sequencer data that is tolerant of errors or mutations of all types—namely, substitutions, deletions, and insertions. The aligner utilizes a multi-stage approach in which template-based indexing is used to identify candidate regions for alignment with dynamic programming. A template is a pair of gapped seeds, with one used with the read and one used with the reference. In this article, we focus on the development of template families that yield error-tolerant indexing up to a given error-budget. A general algorithm for finding those families is presented, and a recursive construction that creates families with higher error tolerance from ones with a lower error tolerance is developed.

Validation of S. pombe sequence assembly by microarray hybridization

We describe a method to make physical maps of genomes using correlative hybridization patterns of probes to random pools of BACs. We derive thereby an estimated distance between probes, and then use this estimated distance to order probes. To test the method, we used BAC libraries from Schizzosaccharomyces pombe. We compared our data to the known sequence assembly, in order to assess accuracy. We demonstrate a small number of significant discrepancies between our method and the map derived by sequence assembly. Some of these discrepancies may arise because genome order within a population is not stable; imposing a linear order on a population may not be biologically meaningful.