Please use this identifier to cite or link to this item:
Title: Inferring regulatory signal from genomic data
Keywords: bioinformatics, transcription regulation, microarray analysis, sequencing analysis, data mining, machine learning
Issue Date: 26-May-2009
Source: VINSENSIUS BERLIAN VEGA S N (2009-05-26). Inferring regulatory signal from genomic data. ScholarBank@NUS Repository.
Abstract: The growth of biological data necessitates development of data mining methods tailored towards understanding complex mechanisms of biological systems. This project focuses on issues related to gene expression regulation, namely: identification key genes from microarray data and analysis of sequencing-based localization of interaction sites of transcription factor (TF) and DNA. In relation to gene expression analysis, we focused on: (i) identifying a minimal gene signature cassette and (ii) identifying primary response genes using time-course expression data. Using high-throughput sequencing-based TF-DNA interaction data, we developed models and formulae to (i) rapidly assess the sequencing adequacy of a given library, (ii) model for ChIP fragment size distribution, (iii) model the signal/noise component and ChIP enrichment quality of a given library, (iv) provide an analytical model of random fragment accumulation, and (v) mitigate the effect of systematic biases arising from aberrant genomic copy number of the underlying biological model system.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
VinsensiusBVegaSN_thesis.pdf1.08 MBAdobe PDF



Page view(s)

checked on Dec 11, 2017


checked on Dec 11, 2017

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.