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Automatic Outlier Detection for Genome Assembly Quality Assessment

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6 Author(s)
Samak, T. ; Lawrence Berkeley Nat. Lab., Berkeley, CA, USA ; Egan, R. ; Bushnell, B. ; Gunter, D.
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In this work we describe a method to automatically detect errors in de novo assembled genomes. The method extends a Bayesian assembly quality evaluation framework, ALE, which computes the likelihood of an assembly given a set of unassembled data. Starting from ALE output, this method applies outlier detection algorithms to identify the precise locations of assembly errors. We show results from a microbial genome with manually curated assembly errors. Our method detects all deletions, 82.3% of insertions, and 88.8% of single base substitutions. It was also able to detect an inversion error that spans more than 400 bases.

Published in:

eScience (eScience), 2013 IEEE 9th International Conference on

Date of Conference:

22-25 Oct. 2013