Efficient Synergistic De Novo Co-Assembly Of Bacterial Genomes From Single Cells Using Colored De Bruijn Graph

<p>Recent progress in DNA amplification techniques, particularly multiple displacement</p> <p>amplification (MDA), has made it possible to sequence and assemble bacterial</p> <p>genomes from a single cell. However, the quality of single cell genome assembly has</p> <p>not yet reached the quality of normal multi-cell genome assembly due to the coverage</p> <p>bias (including uneven depth of coverage and region blackout) and errors caused by</p> <p>MDA. Computational methods try to mitigates the amplification bias. In this document</p> <p>we introduce a de novo co-assembly method using colored de Bruijn graph,</p> <p>which can overcome the problem of blackout regions due to amplification bias. The</p> <p>algorithm is implemented in a tool named HyDA (Hybrid De novo Assembler). HyDA</p> <p>can assemble various genome of phylogenetically close species simultaneously whit a</p> <p>high quality while it can determine the genomic relationship between each pair of</p> <p>co-assembled dataset based on their common and exclusive contigs. Moreover, co-assembly can provide a high quality genome assembly from a number of guessed to</p> <p>be identical single cell. Since our algorithm can detect the outlier, it can generate a</p> <p>non-chimeric result with most recovered genome regions. Various techniques, such as</p> <p>iterative assembly with multiple k, are employed in HyDA, which makes it a powerful</p> <p>de novo assembly tool.</p>

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Open Access Dissertation

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2015

Author dc:creator

• Movahedi Tabrizi, Narjes Sadat

Contributors dc:contributor

• Dongxiao Zhu

• Co-assembly
• Colored De Bruijn Graph
• Genome
• Sequencing
• Single Cell
• Bioinformatics
• Computer Sciences

Repository record dc:identifier

https://digitalcommons.wayne.edu/oa_dissertations/1378

OAI identifier oai:identifier

oai:digitalcommons.wayne.edu:oa_dissertations-2377