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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44264

Title: Mining polymorphic SSRs from individual genome sequences
Authors: Chi-Pong Sio
Yu-Lun Lu
Chien-Ming Chen
Tun-Wen Pai
Hao-Teng Chang
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Simple Sequence Repeat
genetic marker
genetic disease
next generation sequencing (NGS)
1000 genomes project
Date: 2013-07
Issue Date: 2017-11-20T08:24:39Z
Publisher: The 7th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2013)
Abstract: Abstract:Simple Sequence Repeats (SSRs) are abundant in genome sequences and become popular biomarkers for genetic studies. Several SSRs were proved essential for gene regulation, abnormal repeat patterns of these critical SSRs might cause lethal diseases. The Next Generation Sequencing technologies provided efficient approaches for SSR polymorphism detection. However, inefficient and manually curated processes were unavoidable for identifying SSR markers in previous approaches. An automatic and efficient system for detecting polymorphic SSRs at genomic scales was proposed without manual curated and examining works. The workflow accepted multiple NGS sequencing datasets and started with assembly by de novo or reference mapping approaches. The consensus sequences were then obtained from previously assembled contigs, and calibrated coordinates in each individual contig were aligned according to the selected reference sequences. Next, the mining SSR mechanism was designed to retrieve all potential polymorphic SSRs whenever the circumstances were occurred due to insertion or deletion mechanisms. The 1000 genomes Trio projects were employed as the testing sequence datasets, and the CODIS SSR markers and 9 well known disease-related SSR motifs were verified as the testing targets. The results have shown the proposed method could identify the known polymorphic SSRs as well as novel SSR markers when there was no sequencing or mapping errors within the consensus sequences. The proposed method employed NGS technologies to identify SSR polymorphism and accelerate related researches, which facilitates novel SSR biomarker selection and regulatory elements discovery.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44264
Appears in Collections:[資訊工程學系] 演講及研討會

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