Improving Genome Assemblies Using Multi-platform Sequence Data View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2016

AUTHORS

Pınar Kavak , Bekir Ergüner , Duran Üstek , Bayram Yüksel , Mahmut Şamil Sağıroğlu , Tunga Güngör , Can Alkan

ABSTRACT

Accurate de novo assembly using short reads generated by next generation sequencing technologies is still an open problem. Although there are several assembly algorithms developed for data generated with different sequencing technologies, and some that can make use of hybrid data, the assemblies are still far from being perfect. There is still a need for computational approaches to improve draft assemblies. Here we propose a new method to correct assembly mistakes when there are multiple types of data generated using different sequencing technologies that have different strengths and biases. We exploit the assembly of highly accurate short reads to correct the contigs obtained from less accurate long reads. We apply our method to Illumina, 454, and Ion Torrent data, and also compare our results with existing hybrid assemblers, Celera and Masurca. More... »

PAGES

220-232

Book

TITLE

Computational Intelligence Methods for Bioinformatics and Biostatistics

ISBN

978-3-319-44331-7
978-3-319-44332-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-44332-4_17

DOI

http://dx.doi.org/10.1007/978-3-319-44332-4_17

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1010944729


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