PT  - JOURNAL ARTICLE
AU  - Samuel S. Minot
AU  - Stephen D. Turner
AU  - Krista L. Ternus
AU  - Dana R. Kadavy
TI  - SIANN: Strain Identification by Alignment to Near Neighbors
AID  - 10.1101/001727
DP  - 2014 Jan 01
TA  - bioRxiv
PG  - 001727
4099  - http://biorxiv.org/content/early/2014/01/10/001727.short
4100  - http://biorxiv.org/content/early/2014/01/10/001727.full
AB  - Next-generation sequencing is increasingly being used to study samples composed of mixtures of organisms, such as in clinical applications where the presence of a pathogen at very low abundance may be highly important. We present an analytical method (SIANN: Strain Identification by Alignment to Near Neighbors) specifically designed to rapidly detect a set of target organisms in mixed samples that achieves a high degree of species- and strain-specificity by aligning short sequence reads to the genomes of near neighbor organisms, as well as that of the target. Empirical benchmarking alongside the current state-of-the-art methods shows an extremely high Positive Predictive Value, even at very low abundances of the target organism in a mixed sample. SIANN is available as an Illumina BaseSpace app, as well as through Signature Science, LLC. SIANN results are presented in a streamlined report designed to be comprehensible to the non-specialist user, providing a powerful tool for rapid species detection in a mixed sample. By focusing on a set of (customizable) target organisms and their near neighbors, SIANN can operate quickly and with low computational requirements while delivering highly accurate results.