Error correction and diversity analysis of population mixtures determined by NGS
- Published
- Accepted
- Subject Areas
- Biodiversity, Bioinformatics, Computational Biology, Genetics, Mathematical Biology
- Keywords
- calibration, error correction, metagenome, nucleotide diversity, honeybee, viral mix, standard sample
- Copyright
- © 2014 Wood et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2014. Error correction and diversity analysis of population mixtures determined by NGS. PeerJ PrePrints 2:e441v1 https://doi.org/10.7287/peerj.preprints.441v1
Abstract
The impetus for this work was the need to analyse nucleotide diversity in a viral mix taken from honeybees; the methods are illustrated using honeybee viral samples. The paper has two findings. First, a method for correction of next generation sequencing error in the distribution of nucleotides at a site is developed. Second, a package of methods for assessment of nucleotide diversity is assembled. A statistically based error correction method is presented, which works at the level of the nucleotide distribution rather than the level of individual nucleotides. The method relies on an error model and a sample of known viral genotypes that is used for model calibration. A compendium of existing and new diversity analysis tools is also presented, allowing hypotheses about diversity and mean diversity to be tested and associated confidence intervals to be calculated. Software in both Excel and Matlab and a guide are available at http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/, the Warwick University Systems Biology Centre software download site.
Author Comment
This preprint is currently submitted to PeerJ for review.