This last result remains an anomaly. The number of correctly negative bacteria was also important. For the sample with the most apparently false positive Tag4 identifications, A16-4, nevertheless, thirty-one bacteria were correctly negative (Additional file 1: Table S2). For the sample with the most apparently false positive SOLiD identifications, A01-1, nevertheless,

thirty-two bacteria were correctly negative (Additional file 1: Table S2). The large number of SOLiD reads and the high fluorescent intensities on the Tag4 arrays allowed the calculation of Pearson’s correlation learn more coefficient between the two assays and between each assay and the number/percent of BigDye-terminator reads. Pearson’s correlation coefficient ranges from 1 to -1 and represents a quantitative

comparison. The eFT-508 datasheet results are shown in Table 4. There were thirteen comparisons of the SOLiD data to the Tag4 data. Eleven (85%) of the coefficients were > 0.5, and nine (69%) of the coefficients were equal to, or greater than, 0.7. There were twelve comparisons of the SOLiD data to the BigDye-terminator data. Seven had a correlation coefficient of 1, and one had a correlation coefficient of 0.84, for a total of 66%. There were seventeen comparisons of the Tag4 data to the BigDye-terminator data. Eleven had a correlation coefficient of 1, and three AMPK activator had a correlation coefficient of > 0.9 for a total of 82%. Thus, overall, the quantitative correlations were excellent. Table 4 Pearson correlation coefficients among the assays ID SOLiD vs. Tag4 SOLiD vs. BigDye Tag4 vs. BigDye A01-1 0.74 1 1 A03-2 0.45 – 1 1 A03-3     1 A07-1 0.54 – 0.27 – 0.13 A07-2 0.70 – 0.28 – 0.19 A08-2 0.87 1 0.97 A10-2 0.90 1 1 A10-4 0.78 1 1 A13-4     1 A16-2     1 A16-4 0.57     A17-3 0.46 – 0.13 0.18 A19-4 0.88 1 1 A20-3     1 A22-3 0.76 1 0.95 A23-1     0.97 A25-2 0.83 0.84 1 A27-2 0.88 1 1 Discussion Every technology has its advantages and disadvantages. There are two important challenges in detecting bacteria by amplifying and BigDye-terminator (Sanger) sequencing rDNA. (1) rDNA genes are present

at multiple copies per genome, and the copy number differs among bacteria [6, 7]. (2) The “”universal”" primers have mismatches to the rDNAs of highly relevant bacteria [8, 9]. The AZD9291 datasheet negative impact of mismatch between primer and template is substantial [9, 10]. Baker et al. [11] found that no primer pair had good matches to all bacterial rDNA. Therefore, bacterial genomes with few ribosomal RNA genes and/or with rDNA sequence mismatch to the primers will likely be under-represented in the sequencing library. The same considerations make determining the minimum detection limit problematic. In earlier work, we accomplished extensive modeling of the cost/benefit ratio for BigDye-terminator sequencing [12].