Year: 2011
Type: dissertation
Abstract: Recent advances of DNA sequencing technologies are allowingresearchers to generateimmense amounts of data in a fast and cost effective fashion, enablinggenome-wide association study and populationgenetic research which could not be done a decade ago.There are quite numerous computational challenges arising from thisadvancement, however. Examples include efficient algorithms for processing rawdata generated by sequencing instruments, algorithms for detecting andcorrecting sequencing errors, algorithms for detecting genomevariations from sequence data, just to name a few. Because differentsequencing technologies can have drastically differentcharacteristics, these algorithms often need to be adapted in orderto produce most accurate results.In this thesis, I will address a few of the aforementioned problems. First, Iwill describe two model-based basecalling algorithms for the Illuminasequencing platforms: BayesCall and naiveBayesCall. The novelty of BayesCall algorithmis that it is fully unsupervised, requiring notraining data with known labels, and therefore it is applicable to data without a reference sequence.It also dramatically improves sequencing accuracies.Built upon BayesCall algorithm, naiveBayesCall dramatically improves computationalefficiency by approximating the original model without sacrificingaccuracy. We will also show that improved basecall can have positiveeffects on the downstream sequence analysis, such as the detection ofsingle nucleotide polymorphism and the assembly of novel genomes.In the third chapter, an algorithm, called ECHO, for correcting short-readsequencing errors will be described. The correction algorithm efficiently computesall overlaps between sequencing reads and corrects errorsby using statistical models. Since it does not rely on referencegenomes, ECHO can also be applied to de novo sequencing.Most other error correction algorithms require users to specifykey parameters, but the optimal values for these parameters are unknown tousers and can be hard to specify. Without key parameters beingoptimized, the effectiveness of error correction algorithm couldsometimes be dramatically reduced.Based on statistical models, ECHO optimizes these parametersaccordingly. We will show that ECHO can significantly reducesequence error rates and also facilitate downstream sequenceanalysis. It is also demonstrated that ECHO can be extended to detectheterozygousity from sequencing data.These algorithms are developed in hopes tomake downstream analysis of sequence data easier and ultimatelyfacilitate genome researches.
Author: Wei‐Chun Kao
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W57114836