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

Table 3 Studies applying deep sequencing to within-population bacterial variation

Pathogen	Design	Technology	Ref seq	Filter	Align	SNV	Hap	Application	Reference
M. tuber- culosis	Chemical shearing of pooled PCR-amplified target genes (rpoB, katG, pncA, gyrA, rrs) for each isolate, followed by adapter ligation, barcoding, PCR amplification, and library preparation	Ion Torrent 314 PGM, generating 60-70 bp reads at 300-500×	NS	NS	NS	NS	NA	Detection of low-frequency drug resistance mutations	[40]
S. aureus	Extraction of genomic DNA followed by whole genome standard SOLiD mate-pair library construction, with 3 kb fragment size	SOLiD 3 plus, 2 times 50 bp reads at ~5000×	S. aureus SA957	SOCS package: quality threshold of Q15 and trimming to 42 bp	SOCS package	Detect and filter using SOCS package (min. av. qual 20, 500 < read depth < 15000, apply Bernoulli test (p < 0.001) to remaining SNVs	NA	Genome evolution	[41]

Details of the experimental design and analysis pipeline for the two examples of deep sequencing applied to bacterial populations identified in this review. ‘Design’ describes the types of samples used and any sample processing up to library preparation. ‘Technology’ indicates the type of sequencing employed. ‘Filter’ details any pre-alignment read processing steps. ‘Ref. Seq.’ describes what kinds of reference sequences were used for read alignment, while ‘Align’ gives the actual alignment software used. ‘SNV’ and ‘Hap.’ indicate software used for SNV detection and haplotype reconstruction respectively. ‘Application’ describes the biological motivation for the study. ‘NS’ indicates the method was not specified in the cited publication, while ‘NA’ means not attempted.