title: PAPNC, a novel method to calculate nucleotide diversity from large scale next generation sequencing data.
abstract: Estimating viral diversity in infected patients can provide insight into pathogen evolution and emergence of drug resistance. With the widespread adoption of deep sequencing, it is important to develop tools to accurately calculate population diversity from very large datasets. Current methods for estimating diversity that are based on multiple alignments are not practical to apply to such data. In this study, the authors report a novel method (Pairwise Alignment Positional Nucleotide Counting, PAPNC) for estimating population diversity from 454 sequence data. The diversity measurements determined using this method were comparable to those calculated by average pairwise difference (APD) of multiply aligned sequences using MEGA5. Diversities were estimated for 9 patient plasma HIV samples sequenced with Titanium 454 technology and by single-genome sequencing (SGS). Diversities calculated from deep sequencing using PAPNC ranged from 0.002 to 0.021 while APD measurements calculated from SGS data ranged proximately from 0.001 to 0.018, with the difference being attributable to PCR error (contributing background diversity of 0.0016 in a control sample). Comparison of APDs estimated from 100 sets of sequences drawn at random from 454 generated data and from corresponding SGS data showed very close correlation between the two methods with R(2) of 0.96, and differing on average by about 1% (after correction for PCR error). The authors have developed a novel method that is good for calculating genetic diversities for large scale datasets from next generation sequencing. It can be implemented easily as a function in available variation calling programs like SAMtools or haplotype reconstruction software for nucleotide genetic diversity calculation. A Perl script implementing this method is available upon request.
authors.affiliation: Advanced Biomedical Computing Center, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, United States. Electronic address: shaow@mail.nih.gov.
authors.forename: Wei
authors.initials: W
authors.lastname: Shao
authors.affiliation: HIV Drug Resistance Program, NCI, Frederick, MD, United States.
authors.forename: Mary F
authors.initials: MF
authors.lastname: Kearney
authors.affiliation: HIV Drug Resistance Program, NCI, Frederick, MD, United States.
authors.forename: Valerie F
authors.initials: VF
authors.lastname: Boltz
authors.affiliation: HIV Drug Resistance Program, NCI, Frederick, MD, United States.
authors.forename: Jonathan E
authors.initials: JE
authors.lastname: Spindler
authors.affiliation: Division of Infectious Diseases, University of Pittsburgh, Pittsburgh, PA, United States.
authors.forename: John W
authors.initials: JW
authors.lastname: Mellors
authors.affiliation: HIV Drug Resistance Program, NCI, Frederick, MD, United States.
authors.forename: Frank
authors.initials: F
authors.lastname: Maldarelli
authors.affiliation: Department of Molecular Biology and Microbiology, Tufts University, Boston, MA, United States.
authors.forename: John M
authors.initials: JM
authors.lastname: Coffin
mesh_terms: D019295:Computational Biology / Q000379:methods*; D014644:Genetic Variation; D015658:HIV Infections / Q000821:virology*; D015497:HIV-1 / Q000145:classification* / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans
keywords: HIV-1; Next generation sequencing; Viral population diversity calculation