I guess when I talk about signal-to-noise I assume the one that is most
relevant to the task at hand. So, to me, I/sigma(I) at the phasing step
would be the average intensity (I) divided by the sigma (standard
deviation) assigned to it AFTER scaling/mergeing. I admit that the
"I/sigma" column from SCALA is potentially confusing, but if you are
dealing with spot intensities, this is the first I/sigma you think
about, so I guess this is what Phil was thinking.
Personally, I think the descriptions of the columns in this table are
clear if you read the caption before the table in the SCALA output, but
Tassos is right that an alarming number of people have never done this.
RTFM?
When it doubt, use mtzdmp to see what the average values of the data
columns really are.
-James Holton
MAD Scientist
Anastassis Perrakis wrote:
I like to think of things in terms of signal-to-noise, and one can use a
rearrangement of the Crick-Magdoff equation to tell you what the I/sigma
of your data set needs to be for delta-F to be greater than
sigma(delta-F):
I/sigma(I) > 1.3*sqrt(Daltons/sites)/f"
where:
I/sigma(I) is the signal-to-noise ratio of the data set required to
solve it by MAD/SAD
Daltons is the molecular weight of the protein in amu
sites is the number of Se sites
f" is the f" of those sites (in "electrons")
let me see .... we recently solved a 200 residues protein, 4 mol AU,
with 2 Se per mol, total 8 Se.
Since 160 residues were ordered, I will make for you a discount,
18,000 D/monomer, 70,000 in AU.
I truncated data to 4.2 for Se search.
1.3*sqrt(70000/8)/6.5= 19
Statistics from Scala:
N 1/d^2 Dmin(A) Rmrg Rfull Rcum Ranom Nanom Av_I SIGMA
I/sigma sd Mn(I/sd)
1 0.0098 10.11 0.048 0.049 0.048 0.036 349 4967 419
11.9 342 25.8
2 0.0196 7.15 0.050 0.044 0.049 0.031 707 5360 462
11.6 372 28.8
3 0.0293 5.84 0.089 0.062 0.057 0.047 975 1634 224
7.3 177 19.4
4 0.0391 5.06 0.065 0.048 0.059 0.039 1140 2107 207
10.2 218 21.0
5 0.0489 4.52 0.061 0.043 0.060 0.034 1315 2523 227
11.1 253 21.9
6 0.0587 4.13 0.072 0.051 0.062 0.035 1470 2142 223
9.6 242 20.2
7 0.0685 3.82 0.091 0.061 0.066 0.042 1605 1566 203
7.7 219 16.1
8 0.0782 3.57 0.128 0.086 0.071 0.052 1737 1034 186
5.6 199 12.3
9 0.0880 3.37 0.189 0.137 0.077 0.074 1859 667 181
3.7 187 8.9
10 0.0978 3.20 0.314 0.224 0.085 0.129 1940 374 170
2.2 178 5.5
So, that would support your argument.
HOWEVER that would mean looking at the M(I/sd) in the table!!!
"Mn(I/sd)" is not the same as "I/sigma" in Scala notation!!!! Most
people think of I/sigma(I) in your notation,
to be the I/sigma in the scala output, or the I devided by sd in the
Denzo output. These are (very) different.
I am not sure which you meant since I/sigma(I) is not the full
notation (place the <> in the favorite place first ...),
but it seems correct if you meant Mn(I/sd) which most people do not
quote or use much ;-)
Greetings,
Tassos
A.
