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Table 2 Sequencing technologies, features and errors

From: Deep sequencing of evolving pathogen populations: applications, errors, and bioinformatic solutions

Platform Manufacturer Throughput (per machine run) Reported errors Depth (virus) Depth (bacteria) Reference
454 GS Junior Roche ~135 K reads @ ~520 nt ~0.38% indels 7 K 14 [26]
GS-FLX Titanium Roche ~1 M reads @ ~500 nt ~0.28% indels; ~0.12% substitution (max 1.07%) 50 K 100 [27]
MiSeq Illumina ~ 11 M reads @ ~ 150 nt < 0.001% indels, ~0.1% substitutions 165 K 330 [26]
GA IIx Illumina ~ 640 M reads @ 100 nt ~0.001% indels; ~0.31% substitutions (max ~5.85%) 6 M 13 K [27]
HiSeq 2000 Illumina ~ 6G reads @ 100 nt ~0.002% indels; ~0.32% substitutions (max ~8.2%) 60 M 120 K *
Ion Torrent PGM Life technologies ~2 M reads @ ~121 nt ~1.5% indels 24 K 48 [26]
SOLiD Life technologies ~120 M reads @ ~50 nt ~0.09% substitutions (max > 5%) 600 K 1 K [28, 29]
RS Pacific biosystems ~200 K reads @ ~2000 nt (max > 15000 nt) ~14% indels, ~1% substitutions 40 K 80 [30, 31]
tSMS Helicos ~1G reads @ 35 nt ~3% indels, ~0.2% substitutions 3 M 7 K [32]
  1. Indels errors are largely associated with homopolymers for Roche and Ion Torrent. This fact can have a significant impact on the detection of variants associated with homopolymers, as was recently shown for the 2184delA mutation of the cystic fibrosis transmembrane conductance regulator (CFTR) using Ion Torrent PGM [33]. Sequencing errors are also highly dependent on the sequencing context and thus can influence variant calling in a biased, but potentially predictable way. For example, certain GC-rich motifs have been reported to have substitution errors of close to 6% [27] for the Illumina sequencing technology. Depth columns give anticipated read depth for a typical viral (~10 K) or bacterial (~5 M) genome.
  2. *Calculated for this review from control PhiX data using GemSIM v1.6 [27].