Ion Torrent Sequence Analysis

Ion PGM sequencing instruments target research and clinical applications in which speed and accuracy are critical.  Users seek the fastest time to results, measured not only by delivery speed, but also accuracy and completeness.  RTG Investigator meets these expectations with native support for Ion Torrent data in streamlined analysis pipelines for variant detection and metagenomics that integrate the many functions required for delivery of comprehensive, accurate results into simple, consistent commands.  These pipelines are both easy to install and use, and operate much more quickly than open source alternatives. 

Ion Torrent sequence data

Ion Torrent semiconductor sequencing technology delivers sequence data in comparatively longer reads with high accuracy, and a moderate propensity to homopolymerization.  RTG supports the Ion Torrent sequence data as a native data type, trimming low quality reads and applying platform-specific models for read error characteristics. 

The RTG mapping engine, in combination with it's variant caller has shown comparable results to alternative tools with significantly shorter run times. With a data set consisting of 2.7 million Ion Torrent reads, RTG map aligned 94.9% of the reads 15 times faster than TMAP, the freely available mapping software from Ion Torrent.

Variant detection analysis

RTG integrates multiple elements of a comprehensive variant calling pipeline, including paired end alignment, quality recalibration steps, and local realignments, into two functions:  map and snp. Fewer steps make for a shorter learning curve and 10x faster execution time.  The RTG snp command takes the mappings and coresponding read quality recalibration information from the mapper to update its Bayesian priors to target both the Ion Torrent reach characteristics and the reference genome type.

Metagenomic analysis

RTG integrates multiple elements of a metagenomic analysis pipeline, including contaminant filtering, translated protein search, similarity clustering, and species estimation functions.  These functions can be applied to single genome microbial analysis as well as metagenomic analysis.  For example, mapping read data from a bacterial sample against a bacterial sequence database can identify potential sequence component inheritance.  This provides considerable insight into genetic mutation in clinical pathogen detection and response applications.