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The Power Decoder Simulator for the Evaluation of Pooled shRNA Screen Performance.

Stombaugh J, Licon A, Strezoska Ž, Stahl J, Anderson SB, Banos M, van Brabant Smith A, Birmingham A, Vermeulen A - J Biomol Screen (2015)

Bottom Line: Using the negative binomial distribution, it models both the relative abundance of multiple shRNAs within a single screening replicate and the biological noise between replicates for each individual shRNA.We demonstrate that this simulator can successfully model the data from an actual laboratory experiment.The Power Decoder simulator is written in R and Python and is available for download under the GNU General Public License v3.0.

View Article: PubMed Central - PubMed

Affiliation: Dharmacon, part of GE Healthcare, Lafayette, CO, USA.

No MeSH data available.


(A) Power as a function of replicate number. Box plots represent powers derived from DESeq analysis of 900 simulated next-generation sequencing (NGS) experiments of Screen 100_2x per replicate level. For comparison, the actual power of the Screen 500_2x using two biological replicates is also plotted. (B) Power as a function of sequencing coverage. Box plots represent powers derived from DESeq analysis of 900 simulated NGS experiments per coverage. This was done at increments of 100,000 counts per simulation or ~18 sequences per shRNA.
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fig5-1087057115576715: (A) Power as a function of replicate number. Box plots represent powers derived from DESeq analysis of 900 simulated next-generation sequencing (NGS) experiments of Screen 100_2x per replicate level. For comparison, the actual power of the Screen 500_2x using two biological replicates is also plotted. (B) Power as a function of sequencing coverage. Box plots represent powers derived from DESeq analysis of 900 simulated NGS experiments per coverage. This was done at increments of 100,000 counts per simulation or ~18 sequences per shRNA.

Mentions: Monte Carlo simulations were generated using the Power Decoder simulator for Screen 100_2x over a range of 2 to 10 replicates, and the power for each model was compared with actual data acquired using only two replicates for Screen 500_2x (Fig. 5A). Surprisingly, even 10 replicates of Screen 100_2x give a power that is still lower (~82%) than that from two replicates of Screen 500_2x (~83%). This strongly demonstrates the importance of adequate shRNA fold coverage to decrease noise and increase screening power. As it is more labor intensive to perform more replicates than to increase fold coverage, it is clear that increasing shRNA fold coverage is the preferred method to reduce experimental noise. Drawing this conclusion from laboratory data would have been costly and time-consuming, further illustrating the usefulness of the Power Decoder simulator software.


The Power Decoder Simulator for the Evaluation of Pooled shRNA Screen Performance.

Stombaugh J, Licon A, Strezoska Ž, Stahl J, Anderson SB, Banos M, van Brabant Smith A, Birmingham A, Vermeulen A - J Biomol Screen (2015)

(A) Power as a function of replicate number. Box plots represent powers derived from DESeq analysis of 900 simulated next-generation sequencing (NGS) experiments of Screen 100_2x per replicate level. For comparison, the actual power of the Screen 500_2x using two biological replicates is also plotted. (B) Power as a function of sequencing coverage. Box plots represent powers derived from DESeq analysis of 900 simulated NGS experiments per coverage. This was done at increments of 100,000 counts per simulation or ~18 sequences per shRNA.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2 - License 3
Show All Figures
getmorefigures.php?uid=PMC4543901&req=5

fig5-1087057115576715: (A) Power as a function of replicate number. Box plots represent powers derived from DESeq analysis of 900 simulated next-generation sequencing (NGS) experiments of Screen 100_2x per replicate level. For comparison, the actual power of the Screen 500_2x using two biological replicates is also plotted. (B) Power as a function of sequencing coverage. Box plots represent powers derived from DESeq analysis of 900 simulated NGS experiments per coverage. This was done at increments of 100,000 counts per simulation or ~18 sequences per shRNA.
Mentions: Monte Carlo simulations were generated using the Power Decoder simulator for Screen 100_2x over a range of 2 to 10 replicates, and the power for each model was compared with actual data acquired using only two replicates for Screen 500_2x (Fig. 5A). Surprisingly, even 10 replicates of Screen 100_2x give a power that is still lower (~82%) than that from two replicates of Screen 500_2x (~83%). This strongly demonstrates the importance of adequate shRNA fold coverage to decrease noise and increase screening power. As it is more labor intensive to perform more replicates than to increase fold coverage, it is clear that increasing shRNA fold coverage is the preferred method to reduce experimental noise. Drawing this conclusion from laboratory data would have been costly and time-consuming, further illustrating the usefulness of the Power Decoder simulator software.

Bottom Line: Using the negative binomial distribution, it models both the relative abundance of multiple shRNAs within a single screening replicate and the biological noise between replicates for each individual shRNA.We demonstrate that this simulator can successfully model the data from an actual laboratory experiment.The Power Decoder simulator is written in R and Python and is available for download under the GNU General Public License v3.0.

View Article: PubMed Central - PubMed

Affiliation: Dharmacon, part of GE Healthcare, Lafayette, CO, USA.

No MeSH data available.