Fiona

Abstract

Motivation: Fiona is a tool for the automatic correction of sequencing errors in reads produced by high throughput sequencing experiments. It uses an efficient implementation of suffix arrays to detect read overlaps with different seed lengths in parallel. Fiona was compared on several real datasets to state-of-the-art methods and showed overall superior correction accuracy. It was also among the fastest. Additionaly, Fiona embarks unique characteristics which makes it a good choice over existing programs:

• No parameters to set for the user. You just need to know the length of the genome!
• Correction of both substitution and indel errors.
• Optimal correction over a range of seed values.
• Multicore-Parallelization using OpenMP.
• Efficient, memory-saving implementation.

Links

• Download binaries
• View the source code and README on GitHub

Please Cite

• M. H. Schulz, D. Weese, M. Holtgrewe, V. Dimitrova, S. Niu, K. Reinert, H. Richard, “Fiona: a parallel and automatic strategy for read error correction”, vol. 30, iss. 17, 2014.
  cite this publication

  @article{fu_mi_publications1451,
   abstract = {Motivation: Automatic error correction of high-throughput sequencing data can have a
    dramatic impact on the amount of usable base pairs and their quality. It has been shown that the
    performance of tasks such as de novo genome assembly and SNP calling can be dramatically improved
    after read error correction. While a large number of methods specialized for correcting
    substitution errors as found in Illumina data exist, few methods for the correction of indel
    errors, common to technologies like 454 or Ion Torrent, have been proposed.Results: We present
    Fiona, a new stand-alone read error{\^a}??correction method. Fiona provides a new statistical
    approach for sequencing error detection and optimal error correction and estimates its parameters
    automatically. Fiona is able to correct substitution, insertion and deletion errors and can be
    applied to any sequencing technology. It uses an efficient implementation of the partial suffix
    array to detect read overlaps with different seed lengths in parallel. We tested Fiona on several
    real datasets from a variety of organisms with different read lengths and compared its performance
    with state-of-the-art methods. Fiona shows a constantly higher correction accuracy over a broad
    range of datasets from 454 and Ion Torrent sequencers, without compromise in speed.Conclusion:
    Fiona is an accurate parameter-free read error{\^a}??correction method that can be run on
    inexpensive hardware and can make use of multicore parallelization whenever available. Fiona was
    implemented using the SeqAn library for sequence analysis and is publicly available for download
    at http://www.seqan.de/projects/fiona.Contact: mschulz@mmci.uni-saarland.de or
    hugues.richard@upmc.frSupplementary information: Supplementary data are available at
    Bioinformatics online.},
   author = {M. H. Schulz and D. Weese and M. Holtgrewe and V. Dimitrova and S. Niu and K. Reinert and
    H. Richard},
   journal = {Bioinformatics},
   number = {17},
   pages = {i356--i363},
   title = {Fiona: a parallel and automatic strategy for read error correction},
   url = {http://publications.imp.fu-berlin.de/1451/},
   volume = {30},
   year = {2014}
  }

Contact

For questions, comments, or suggestions please contact:

Marcel Schulz