The Genome Assembly Model for Next-Generation Sequencing Data

DOI

10.2991/ammsa-17.2017.21How to use a DOI?

Keywords

de Bruijn graph; greedy strateg; quicksort; algorithm

Abstract

At present, next-generation sequencing technology are quickly applied to every field of life science research. Per base has higher coverage and lower cost. Shorter reads and higher error rates from these new instruments necessitate the development of new algorithm and software[1]. We describe an assembly algorithm for next-generation sequencing data. The algorithms developed to solve this problem are based on de Bruijn graph, greedy strategy and quicksort. We explain the algorithm and present the results of assembling a bacterial artificial chromosome(BAC). The value of scaffold N50 is 71613, and N90 is 157742. And the final running time is 20.796 seconds. The value of N50 and N90 reflect the ability of scaffold sequence covering reference genome, the bigger the better. Therefore, the algorithm seems to be good for solving short reads assembly problem.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yirong Wang
AU  - Chengdong Wei
AU  - Xiaodong Zhang
AU  - Tailin Cen
PY  - 2017/05
DA  - 2017/05
TI  - The Genome Assembly Model for Next-Generation Sequencing Data
BT  - Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)
PB  - Atlantis Press
SP  - 97
EP  - 101
SN  - 1951-6851
UR  - https://doi.org/10.2991/ammsa-17.2017.21
DO  - 10.2991/ammsa-17.2017.21
ID  - Wang2017/05
ER  -