Mapping Genome Occupancy in Embryonic Stem Cells

• Growth Conditions
• Human ES Cell Quality Control
• Technology and Protocols

• Download Data
• Data Analysis
• Normalization and Analysis
• Identification of Bound Regions
• Error Rates

We used sequence-specific PCR to estimate false positive rates for the whole-genome array data (Figure S5). For RNA polymerase II, a subset of the bound probe sets were selected and primer pairs designed to amplify between 100 and 200 bp within each bound probe set. Primers were tested for specificity using BLAST and ePCR. A total of 192 primer pairs were selected, where each primer had 10 or fewer matches to the genome and the pair predicted a single amplicon. For RNA polymerase II IP samples, 10 ng of immunoenriched DNA was used as input to the PCR. For whole cell extract (WCE) samples, a range of unenriched DNA amounts (90, 30 and 10 ng of DNA) was used. The PCR was performed for 28 cycles and products were visualized on an agarose gel stained with SYBR Gold (Amersham) and quantified using ImageQuant (Amersham). Only PCR reactions giving single bands with intensities ordered according to the WCE concentration were used. Genomic regions were considered enriched if the 10 ng IP sample showed either 1.5-fold or greater enrichment compared to the 30 ng WCE sample or greater than 1-fold enrichment compared to the 90 ng WCE sample. Genomic regions were considered not enriched if the band intensity of the 10 ng IP was less than half that of the 30 ng WCE or less than the 10 ng WCE. A total of 119 primer pairs yielded a clear enriched/not enriched decision. 114 of these showed enrichment, indicating a false positive rate of 4.4%. Using this set of PCR results, we were also able produce receiver-operator curves showing how changes in peak identification criteria would affect the false positive and false negative rates. The results suggest that our selected criteria are useful for maximizing the identification of true positives.

Two lines of evidence suggest that the false negative rate is approximately 30%. Estimating a false negative rate is generally much more problematic than measuring a false positive rate because the measurement of a false negative rate assumes perfect knowledge of the true positives in the dataset. As every method will have its own error rate, determining a set of true positives is challenging, if not impossible. Despite this important caveat, we have used both sequence-specific PCR and a comparison with expression datasets to estimate a false negative rate.

To obtain an estimate of the false positive rate for our sequence specific PCR reactions, we designed 49 primer pairs against regions of the genome that had no indication of RNA polymerase II binding (p-value for average X and center probes X > 0.3) despite being in densely tiled regions. We reasoned that any substantial PCR amplification in this region was more likely to reflect a false positive in the PCR then to reflect binding of a very large fraction of the genome to the initiation form of RNA polymerase II. From these PCRs, we measured a false detection rate of ~9%. We then designed a series of PCR primers against probes 'expressing' a broad range of p-values between these absolute negatives and our positive list. 60 of these pairs produced positive PCR amplifications. Correcting for the expected false detection rate of the PCR, we calculate a probe based false negative rate of ~33%.

We also used sequence-specific PCR to estimate false positive and false negative rates for the whole-genome Suz12 array data. For estimating false positives, a total of 108 primer pairs yielded a clear enriched/not enriched decision. 105 of these showed enrichment, indicating a false positive rate of 2.8%. Correcting for the expected false detection rate of the PCR, we calculated a probe based false negative rate of 27%.