Comprehensive evaluation of error correction methods for high-throughput sequencing data

Computational genomics;Algorithms;Parallel computing

Manikandan, Gowthami Jayashri ; Chen ; Deming

关键词: Computational genomics;    Algorithms;    Parallel computing;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/97787/MANIKANDAN-THESIS-2017.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

The advent of DNA and RNA sequencing has significantly revolutionized the study of genomics and molecular biology. Development of high-throughput sequencing technologies have brought about a quick and cheaper way to sequence genomes. Different technologies use different underlying methods for sequencing and are prone to different error rates. Though many tools exist for error correction in high-throughput sequencing data, no standard technology-independent method is available yet to evaluate the accuracy and effectiveness of these error correction tools. In order to supply a standard way to evaluate error correction methods for DNA and RNA sequencing, this thesis presents a Software Package for Error Correction Tool Assessment on nuCLEic acid sequences (SPECTACLE). SPECTACLE can evaluate corrected DNA and RNA reads from many underlying sequencing technologies and differentiate heterozygous alleles from sequencing errors. The work provides some key insights on many factors that stress the challenges in error correction by compiling high-throughput sequencing read sets from technologies like Illumina, PacBio and ONT. The performances of 23 different error correction tools have been analyzed using SPECTACLE and the compiled datasets. This thesis also provides unique and helpful insights into the strengths and weaknesses of various error correction tools and aims to establish a standard platform for evaluating error correction tools in the future.