Interactive companion for experimental design of deep mutational scanning experiments

This page helps you optimize the cost vs. error function of your high-throughput bulk competition experiments. Use the tools below to visualize some key results from Matuszewski et al. 2016 for values that are relevant to your proposed experiment(s). The publication in Genetics can be found here (or see the pre-print version here).

Use the sliders to see the costs for different values of the sequencing depth per sampled time point (D), the number of mutants (K), the total duration of the experiment (t_τ), and the number of equally spaced sampling time points (τ).

# Reads per Sample (D)	# Mutants (K)	Duration (t_τ)
1k >5bil	100 >1mil	1 25
1000	100	13
# Samples Taken (τ)	Use Default Costs?	Minimum Expected 95% Confidence Interval*
2 20	yes no
7

Overall costs^†	Monetary costs ($)^‡

Hourly personel costs (monetary cost):
Salary per hour: $ Number of personel hours per unit of experimental duration (t_τ):
Fixed number of hours needed to set up experiment, prepare samples for sequencing, etc:

Sample preparation costs per sampled time point (monetary costs):
DNA extraction: $ PCR: $ Quality control & quantification: $
Library synthesis: $

Sequencing costs per lane and median number of expected reads per lane (monetary costs):
Sequencing cost 1: $ Median number of reads expected (default is MiSeq value):
Sequencing cost 2: $ Median # reads (default: MidOutput NextSeq):
Sequencing cost 3: $ Median # reads (default: HighOutput NextSeq):

Parameters to weigh the importance of the mean squared error (non-monetary costs):
α: β:

This tool is hosted by the Bank lab and the Gulbenkian Institute. This page was last updated by A.H. Ghenu on:

The interactive companion for "A statistical guide to the design of deep mutational scanning experiments"