The vast increase in DNA sequencing capacity over the last decade has quickly turned biology into a data-intensive science. Nevertheless, current sequencers such as Illumia HiSeq have high random per-base error rates, which makes sequencing error correction an indispensable require-ment for many sequence analysis applications. Most existing methods for error correction demand large expensive memory space, which limits their scalability for handling large datasets. In this thesis, we introduce a new disk based method, called DiskBQcor, for sequencing error correction. DiskBQcor stores k-mers for sequencing genome data along with their associated metadata in a disk based index tree, called the BoND-tree, and uses the index to efficiently process specially designed box queries to obtain relevant k-mers and their occurring frequencies. It takes an input read and locates the potential errors in the sequence. It then applies a comprehensive voting mech-anism and possibly an efficient binary encoding based assembly technique to verify and correct an erroneous base in a genome sequence under various conditions. To overcome the drawback of an offline approach such as DiskBQcor for wasting computing resources while DNA sequecing is in process, we suggest an online approach to correcting sequencing errors. The online processing strategies and accuracy measures are discussed. An algorithm for deleting indexed k-mers from the BoND-tree, which is a step stone for the online sequencing error correction, is also introduced. Our experiments demonstrate that the proposed methods are quite promising in error correction for sequencing genome data on disk. The resulting BoND-tree with correct k-mers can also be used for sequence analysis applications such as variant detection.Master of ScienceComputer and Information Science, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/136615/3/Thesis_YarongGu_519_corrected.pd
