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START : a parallel signal track analytical research tool for flexible and efficient analysis of genomic data

Signal Track Analytical Research Tool (START), is a parallel system for analyzing large-scale genomic data. Currently, genomic data analyses are usually performed by using custom scripts developed by individual research groups, and/or by the integrated use of multiple existing tools (such as BEDTools and Galaxy). The goals of START are 1) to provide a single tool that supports a wide spectrum of genomic data analyses that are commonly done by analysts; and 2) to greatly simplify these analysis tasks by means of a simple declarative language (STQL) with which users only need to specify what they want to do, rather than the detailed computational steps as to how the analysis task should be performed.

START consists of four major components: 1) A declarative language called Signal Track Query Language (STQL), which is a SQL-like language we specifically designed to suit the needs for analyzing genomic signal tracks. 2) A STQL processing system built on top of a large-scale distributed architecture. The system is based on the Hadoop distributed storage and the MapReduce Big Data processing framework. It processes each user query using multiple machines in parallel. 3) A simple and user-friendly web site that helps users construct and execute queries, upload/download compressed data files in various formats, man-age stored data, queries and analysis results, and share queries with other users.
It also provides a complete help system, detailed specification of STQL, and a large number of sample queries for users to learn STQL and try START easily. Private files and queries are not accessible by other users. 4) A repository of public data popularly used for large-scale genomic data analysis, including data from ENCODE and Roadmap Epigenomics, that users can use in their analyses. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Identiferoai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/211136
Date January 2015
CreatorsZhu, Xinjie, 朱信杰
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Source SetsHong Kong University Theses
LanguageEnglish
Detected LanguageEnglish
TypePG_Thesis
RightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License
RelationHKU Theses Online (HKUTO)

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