Computational Biology
@ Comenius University in Bratislava

Rastislav Rabatin, Broňa Brejová, Tomáš Vinař. Using Sequence Ensembles for Seeding Alignments of MinION Sequencing Data. Technical Report arXiv:1606.08719, arXiv, 2016.

Download preprint: not available

Download from publisher: http://arxiv.org/abs/1606.08719

Related web page: not available

Bibliography entry: BibTeX

Abstract:

Oxford Nanopore MinION sequencer is currently the smallest sequencing 
device available. While being able to produce very long reads (reads of up 
to 100~kbp were reported), it is prone to high sequencing error rates of up 
to 30%. Since most of these errors are insertions or deletions, it is very 
difficult to adapt popular seed-based algorithms designed for aligning data 
sets with much lower error rates.

Base calling of MinION reads is typically done using hidden Markov models. 
In this paper, we propose to represent each sequencing read by an ensemble 
of sequences sampled from such a probabilistic model. This approach can 
improve the sensitivity and false positive rate of seeding an alignment 
compared to using a single representative base call sequence for each read.