Assessment and correction of errors in DNA sequencing technologies

Next Generation Sequencing technologies differ by several parameters where the choice to use whether short or long read sequencing platforms often leads to trade-offs between accuracy and read length. In this thesis, I first demonstrate the problems in reproducibility in analyses using short reads. Our comprehensive analysis on the reproducibility of computational characterization of genomic variants using high throughput sequencing data shows that repeats might be prone to ambiguous mapping. Short reads are more vulnerable to repeats and, thus, may cause reproducibility problems. Next, I introduce a novel algorithm Hercules, the first machine learning-based long read error correction algorithm. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time- efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies. Our algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platform's error profile. The algorithm learns a posterior transition/ emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads.