ShoRAH: estimating the genetic diversity of a mixed sample from next-generation sequencing data

Background: With next-generation sequencing technologies, experiments that were considered prohibitive only a few years ago are now possible. However, while these technologies have the ability to produce enormous volumes of data, the sequence reads are prone to error. This poses fundamental hurdles when genetic diversity is investigated. Results: We developed ShoRAH, a computational method for quantifying genetic diversity in a mixed sample and for identifying the individual clones in the population, while accounting for sequencing errors. The software was run on simulated data and on real data obtained in wet lab experiments to assess its reliability. Conclusions: ShoRAH is implemented in C++, Python, and Perl and has been tested under Linux and Mac OS X. Source code is available under the GNU General Public License at http://www.cbg.ethz.ch/software/shorah. Background The advent of a new generation of high-throughput DNA sequencing technologies, known as deep sequencing or next-gene...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Osvaldo Zagordi, Arnab Bhattacharya, Nicholas Eriksson, Niko Beerenwinkel
• Keywords: Biometrics, BMCBI 2011, GNU General Public License, Sanger Sequencing, Throughput Dna