De Novo Assembly of Nucleotide Sequences in a Compressed Feature Space

Tapinos, A. and Robertson, D. L. (2017) De Novo Assembly of Nucleotide Sequences in a Compressed Feature Space. In: 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Manchester, UK, 23-25 Aug 2017, ISBN 9781467389884 (doi: 10.1109/CIBCB.2017.8058556)

Abstract

Sequencing technologies allow for an in-depth analysis of biological species but the size of the generated datasets introduce a number of analytical challenges. Recently, we demonstrated the application of numerical sequence representations and data transformations for the alignment of short reads to a reference genome. Here, we expand out approach for de novo assembly of short reads. Our results demonstrate that highly compressed data can encapsulate the signal suffi- ciently to accurately assemble reads to big contigs or complete genomes.

• Item Type: Conference Proceedings
• Additional Information: We thank Shaun Kandathil, Bede Constandinides and Samaneh Kouchaki for valuable recommendations, data sharing and for assistance with coding. Funding: this work has been supported by the BBSRC [BB/M001121/1], and the VIROGENESIS project which receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 634650.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Robertson, Professor David
• Authors: Tapinos, A., and Robertson, D. L.
• College/School: College of Medical Veterinary and Life Sciences > School of Infection & Immunity; College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research
• ISBN: 9781467389884
• Published Online: 05 October 2017
• Copyright Holders: Copyright © 2017 IEEE
• First Published: First published in 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy