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oai:mountainscholar.org:10217/1953412025-12-30T03:38:23Zcom_10217_100532com_10217_100000com_10217_100388com_10217_100303col_10217_100538col_10217_100389

Shrestha, Akash, author Chitsaz, Hamidreza, advisor Ben-Hur, Asa, committee member Abdo, Zaid, committee member 2019-06-14T17:06:25Z 2019-06-14T17:06:25Z 2019 Shrestha_colostate_0053N_15383.pdf https://hdl.handle.net/10217/195341 https://doi.org/10.25675/3.018912 Read alignment is the process of mapping short DNA sequences into the reference genome. With the advent of consecutively evolving "next generation" sequencing technologies, the need for sequence alignment tools appeared. Many scientific communities and the companies marketing the sequencing technologies developed a whole spectrum of read aligners/mappers for different error profiles and read length characteristics. Among the most recent successfully marketed sequencing technologies are Oxford Nanopore and PacBio SMRT sequencing, which are considered top players because of their extremely long reads and low cost. However, the reads may contain error up to 20% that are not generally uniformly distributed. To deal with that level of error rate and read length, proximity preserving hashing techniques, such as Minhash and Minimizers, were utilized to quickly map a read to the target region of the reference sequence. Subsequently, a variant of global or local alignment dynamic programming is then used to give the final alignment. In this research work, we train a Deep Neural Network (DNN) to yield a hashing scheme for the highly erroneous long reads, which is deemed superior to Minhash for mapping the reads. We implemented that idea to build a read alignment tool: DNNAligner. We evaluated the performance of our aligner against the popular read aligners in the bioinformatics community currently — minimap2, bwa-mem and graphmap. Our results show that the performance of DNNAligner is comparable to other tools without any code optimization or integration of other advanced features. Moreover, DNN exhibits superior performance in comparison with Minhashon neighborhood classification. born digital masters theses English eng Colorado State University. Libraries 2000-2019 Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. Minhash pattern discovery sequence alignment neural network DNA Read alignment using deep neural networks Text Master of Science (M.S.) Masters Computer Science Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access