Seed-Set Construction by Equi-entropy Partitioning for Efficient and Sensitive Short-Read Mapping

Spaced seeds have been shown to be superior to continuous seeds for efficient and sensitive homology search based on the seedand-extend paradigm. Much the same is true in genome mapping of high-throughput short-read data. However, a highly sensitive search with multiple spaced patterns often requires the use of a great amount of index data. We propose a novel seed-set construction method for efficient and sensitive genome mapping of short reads with relatively high error rates, which uses only continuous seeds of variable length allowing a few errors. The seed lengths and allowable error positions are optimized on the basis of entropy, which is a measure of ambiguity or repetitiveness of mapping positions. These seeds can be searched efficiently using the Burrows-Wheeler transform of the reference genome. Evaluation using actual biological SOLiD sequence data demonstrated that our method was competitive in speed and sensitivity using much less memory and disk space in comparison to spaced-seed methods.