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oai:mountainscholar.org:10217/1704022025-12-30T03:17:22Zcom_10217_100532com_10217_100000com_10217_100388com_10217_100303col_10217_100538col_10217_100389

Tolooee, Cameron, author Pallickara, Sangmi, advisor Ben-Hur, Asa, committee member von Fischer, Joseph, committee member 2016-01-11T15:14:01Z 2016-01-11T15:14:01Z 2015 Tolooee_colostate_0053N_13379.pdf http://hdl.handle.net/10217/170402 https://doi.org/10.25675/3.018410 Rapid advances in genomic sequencing technology have resulted in a data deluge in biology and bioinformatics. This increase in data volumes has introduced computational challenges for frequently performed sequence analytics routines such as DNA and protein homology searches; these must also preferably be done in real-time. This thesis proposes a scalable and similarity-aware distributed storage framework, Mendel, that enables retrieval of biologically significant DNA and protein alignments against a voluminous genomic sequence database. Mendel fragments the sequence data and generates an inverted-index, which is then dispersed over a distributed collection of machines using a locality aware distributed hash table. A novel distributed nearest neighbor search algorithm identifies sequence segments with high similarity and splices them together to form an alignment. This paper includes an empirical evaluation of the performance, sensitivity, and scalability of the proposed system over the NCBI's non-redundant protein dataset. In these benchmarks, Mendel demonstrates higher sensitivity and faster query evaluations when compared to other modern frameworks. 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. distributed system homology search sequence similarity search On the use of locality aware distributed hash tables for homology searches over voluminous biological sequence data 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