Using neural networks to filter predicted errors in NGS data

Authors

  • Milko Krachunov Faculty of Mathematics and Informatics, Sofia University "St.Kliment Ohridski", Sofia
  • Ognyan Kulev Faculty of Mathematics and Informatics, Sofia University "St.Kliment Ohridski", Sofia
  • Maria Nisheva Faculty of Mathematics and Informatics, Sofia University "St.Kliment Ohridski", Sofia
  • Valeria Simeonova Faculty of Mathematics and Informatics, Sofia University "St.Kliment Ohridski", Sofia
  • Deyan Peychev AgroBioInstitute, Bioinformatics group, Sofia
  • Dimitar Vassilev AgroBioInstitute, Bioinformatics group, Sofia

DOI:

https://doi.org/10.14806/ej.21.A.827

Abstract

The amount of sequencing errors produced by NGS technologies is low, but not negligible. Some studies, such as SNP calling in metagenomics, are very sensitive to any noise present in the sequencing data, and would greatly benefit from precise error detection techniques to discover incorrect bases without flagging the real variation in the data as erroneous.  A 46-61% decrease in the number of predicted errors, all from incorrectly identified errors, is observed when the neural network is applied over a set predicted with frequencies and thresholds.

Author Biographies

  • Milko Krachunov, Faculty of Mathematics and Informatics, Sofia University "St.Kliment Ohridski", Sofia
    PhD student
  • Dimitar Vassilev, AgroBioInstitute, Bioinformatics group, Sofia
    Bioinformatics group leader
EJ 21.A

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Published

2015-03-25

Issue

Vol. 21: Supplement A

Section

Oral Presentations

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