Unfortunately, the standard normal (from which all others can be determined, since the normal is a location-scale family) quantile function does not admit a closed form (i.e. a 'pretty formula'). The closest thing to a closed form is that the standard normal quantile function is the function, $w$, that satisfies the differential equation
$$ \frac{d^2 w}{d p^2} = w \left(\frac{d w}{d p}\right)^2 $$
and the initial conditions $w(1/2) = 0$ and $w'(1/2) = \sqrt{2 \pi}$. In most computing environments there is a function that numerically calculates the normal quantile function. In R, you would type
qnorm(p, mean=mu, sd=sigma)
to get the $p$'th quantile of the $N(\mu, \sigma^2)$ distribution.
Edit: With a modified understanding of the problem, the data is generated from a mixture of normals, so that the density of the observed data is:
$$ p(x) = \sum_{i} w_{i} p_{i}(x) $$
where $\sum_{i} w_{i} = 1$ and each $p_{i}(x)$ is some normal density with mean $\mu_{i}$ and standard deviation $\sigma_{i}$. It follows that the CDF of the observed data is
$$ F(y) = \int_{-\infty}^{y} \sum_{i} w_{i} p_{i}(x) dx
= \sum_{i} w_{i} \int_{-\infty}^{y} p_{i}(x)
= \sum_{i} w_{i} F_{i}(y) $$
where $F_{i}(x)$ is the normal CDF with mean $\mu_{i}$ and standard deviation $\sigma_{i}$. Integration and summation can be interchanged because these integrals are finite. This CDF is continuous and easy enough to calculate on a computer, so the inverse CDF, $F^{-1}$, also known as the quantile function, can be calculated by doing a line search. I default to this option because no simple formula for the quantile function of a mixture of normals, as a function of the quantiles of the constituent distributions, comes to mind.
The following R code numerically calculates $F^{-1}$ using bisection for the line search. The function F_inv() is the quantile function, you need to supply the vector containing each $w_{i}, \mu_{i}, \sigma_{i}$ and the quantile to be solved for, $p$.
# evaluate the function at the point x, where the components
# of the mixture have weights w, means stored in u, and std deviations
# stored in s - all must have the same length.
F = function(x,w,u,s) sum( w*pnorm(x,mean=u,sd=s) )
# provide an initial bracket for the quantile. default is c(-1000,1000).
F_inv = function(p,w,u,s,br=c(-1000,1000))
{
G = function(x) F(x,w,u,s) - p
return( uniroot(G,br)$root )
}
#test
# data is 50% N(0,1), 25% N(2,1), 20% N(5,1), 5% N(10,1)
X = c(rnorm(5000), rnorm(2500,mean=2,sd=1),rnorm(2000,mean=5,sd=1),rnorm(500,mean=10,sd=1))
quantile(X,.95)
95%
7.69205
F_inv(.95,c(.5,.25,.2,.05),c(0,2,5,10),c(1,1,1,1))
[1] 7.745526
# data is 20% N(-5,1), 45% N(5,1), 30% N(10,1), 5% N(15,1)
X = c(rnorm(5000,mean=-5,sd=1), rnorm(2500,mean=5,sd=1),
rnorm(2000,mean=10,sd=1), rnorm(500, mean=15,sd=1))
quantile(X,.95)
95%
12.69563
F_inv(.95,c(.2,.45,.3,.05),c(-5,5,10,15),c(1,1,1,1))
[1] 12.81730
