With the rapid development of next-generation sequencing NGS technology, we are able to investigate various aspects biological problems, including genome and transcriptome sequencing, genomic structural variation and the mechanism of regulatory small RNAs, etc. An enormous number of associated computational methods have been proposed to study the biological problems using NGS reads, at a low cost of expense and time. Regulatory small RNAs and genomic structure variations are two main problems that we have studied.
In the area of regulatory small RNA, various computational tools have been designed from the prediction of small RNA to target prediction. Regulatory small RNAs play essential roles in plants and bacteria such as in responses to environmental stresses. We focused on sRNAs that in act by base pairing with target mRNA in complementarity. A comprehensive analysis workflow that is able to integrate sRNA-Seq and RNA-Seq analysis and generate regulatory network haven't been designed yet. Thus, we proposed and implemented two small RNA analysis workflow for plants and bacteria respectively.
In the area of genomic structural variations (SV), two types of disease-related SVs have been investigated, including complex low copy repeats (LCRs, also termed as segmental duplications) and tandem duplication (TD). LCRs provide structural basis to form a combination of other SVs which may in turn lead to some serious genetic diseases and TDs of specific areas have been reported for patients. Locating LCRs and TDs in human genome can help researchers to further interrogate the mechanism of related diseases. Therefore, we proposed two computational methods to predict novel LCRs and TDs in human genome. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy
| Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/208038 |
| Date | January 2014 |
| Creators | Liu, Xuan, 刘璇 |
| Contributors | Yiu, SM |
| Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
| Source Sets | Hong Kong University Theses |
| Language | English |
| Detected Language | English |
| Type | PG_Thesis |
| Rights | Creative Commons: Attribution 3.0 Hong Kong License, The author retains all proprietary rights, (such as patent rights) and the right to use in future works. |
| Relation | HKU Theses Online (HKUTO) |
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