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Master Thesis

Assembly improvements by read mapping and phasing

Abstract : Massively parallel DNA sequencing technologies are revolutionizing genomics research. Billions of short reads can be generated at low costs (Illumina launched in January 2014 its HiSeq X Ten Sequencer which delivers the first $1000 human genome at 30x coverage). This storm of genomic data calls for the development of scalable and accurate techniques. The difficulty of de novo assembly and limitation of sequencing techniques have led to dozens of assembly algorithms, none of which is perfect. Here we present a novel approach combining the sequence alignment and the assembly fields: a read mapping algorithm working on a De Bruijn graph instead of sequences named BGREAT (de Bruijn Graph REad mApping Tool). The main focus of this work was the scalability of the tools in both terms of memory usage and throughput. To this end we chose to study state-of-the-art low memory and efficient algorithms. This report will give a vision of the assembly and alignment fields, present the algorithm and it's actual implementation and finally study the results obtained and their implications.
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Master Thesis
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Contributor : Co-Responsables Du Mri V. Gouranton Et S. Blazy <>
Submitted on : Friday, November 28, 2014 - 5:19:08 PM
Last modification on : Thursday, December 12, 2019 - 1:40:09 PM
Long-term archiving on: : Friday, April 14, 2017 - 11:21:33 PM


Distributed under a Creative Commons Attribution 4.0 International License


  • HAL Id : dumas-01088821, version 1



Antoine Limasset. Assembly improvements by read mapping and phasing. Computer Science [cs]. 2014. ⟨dumas-01088821⟩



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