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TECHNIQUES FOR DE NOVO SEQUENCE ASSEMBLY: ALGORITHMS
AND EXPERIMENTAL RESULTS
by
Sungje Cho
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
August 2012
Copyright 2012 Sungje Cho
Object Description
| Title | Techniques for de novo sequence assembly: algorithms and experimental results |
| Author | Cho, Sungje |
| Author email | sungje.cho@gmail.com;sungjech@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2012-04-24 |
| Date submitted | 2012-07-30 |
| Date approved | 2012-07-30 |
| Restricted until | 2012-07-30 |
| Date published | 2012-07-30 |
| Advisor (committee chair) | Kuo, C.-C. Jay |
| Advisor (committee member) |
Leahy , Richard M. Chen, Ting |
| Abstract | The deep sequencing of second generation sequencing technology has enabled us to study complex biological structures, which have multiple DNA units simultaneously such as transcriptomics and metagenomics. Unlike general genome sequence assembly, a DNA unit of these biological structures may have multiple copies with small or substantial structural variations and/or SNPs simultaneously in an experimental sample. Therefore, the deep sequencing is necessary to figure out such variations concurrently. ❧ This dissertation focuses on de novo transcriptome assembly which requires simultaneous assembly of multiple alternatively spliced gene transcripts. In practice, the de novo transcriptome assembly is the only option for studying the transcriptome of organisms that do not have reference genome sequences, and it can also be applied to identify novel transcripts and structural variations in the gene regions of model organisms. We propose WEAV for the de novo transcriptome assembly which consists of two separate processes: clustering and assembly. ❧ WEAV reduces the complexity of RNA-seq dataset by partitioning it into clusters called clustering. WEAV simplify a diverse RNA-seq dataset, which has many genes together, into many, smaller clustered read sets, which have few genes a cluster, in the clustering process. The underlying idea is straightforward. A sequencer samples reads from random place so reads from one gene may have overlaps with others if sequencing depth is enough. The overlaps are the keys to connect reads from one gene. We can transform a dataset into a graph where each read is a node and two reads are connected by an edge when they have an overlap. Each connected component will be a clustered read set. As a result, we can assume that a cluster may have one or few genes; therefore, it will not be mixed. ❧ After this process, WEAV assembles the clustered read set with de Bruijn graph backbone, and a novel error correction process simplify the backbone with a fast mapping tool, PerM. Roughly speaking, WEAV tries to solve the historical Shortest Common Superstring [61] problem with the graph to identify multiple alternatively spliced gene transcripts simultaneously and approaches the problem using Set Cover problem. We propose novel statistical measures to make the NP hard problem manageable. The measures are explainability based on the likelihood of sequences and correctness based on bootstrapping. ❧ We compared WEAV with other assemblers with various, simulated reads. We tested the performance by widely used measures such as specificity, sensitivity, N50, and the length of the longest sequence. After this, we tested WEAV using an experimental dataset having 58.58 million 100bp human brain transcriptome reads. WEAV assembled 156,494 contigs that were longer than 300bp. 96.3% (specificity) of these contigs were mapped onto either RefSeq, Gencode or human Genome sequences (hg19), and they covered >72% sequenced bases annotated in RefSeq and Gencode. These high sensitivity and specificity showed the exceptional power of WEAV for transcriptome assembly. |
| Keyword | computational biology; bioinformatics; sequence assembly |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Rights | Cho, Sungje |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume4/etd-ChoSungje-1068.pdf |
Description
| Title | Page 1 |
| Full text | TECHNIQUES FOR DE NOVO SEQUENCE ASSEMBLY: ALGORITHMS AND EXPERIMENTAL RESULTS by Sungje Cho A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) August 2012 Copyright 2012 Sungje Cho |
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