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Related Concept Videos

Root Mean Square00:57

Root Mean Square

If in an experiment, data values have a probability of being both positive and negative, neither the arithmetic mean, the geometric mean, nor the harmonic mean can be used to calculate the central tendency of the data set. In particular, if the positive and negative values are equally likely, the arithmetic mean is close to zero.
For example, consider the velocity of gas molecules in a container. The gas molecules are moving in different directions, which might impart positive and negative...
Mean Absolute Deviation01:13

Mean Absolute Deviation

The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
Calculating Standard Deviation01:08

Calculating Standard Deviation

The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high variation.       
Let us...
Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...
Effective Value of a Periodic Waveform01:07

Effective Value of a Periodic Waveform

The concept of effective value, the root mean square (RMS) value, is crucial in understanding electrical circuits and power delivery. This idea emerges from the necessity to measure the effectiveness of a voltage or current source in supplying power to a resistive load.
The effective value of a periodic current represents the direct current (DC) that conveys the same average power to a resistor as the periodic current itself. This concept is crucial when assessing AC circuits. To determine the...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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GPU-Q-J, a fast method for calculating root mean square deviation (RMSD) after optimal superposition.

Ling-Hong Hung1, Michal Guerquin, Ram Samudrala

  • 1Department of Microbiology, University of Washington, Seattle WA USA. ram@ram.org.

BMC Research Notes
|April 2, 2011
PubMed
Summary
This summary is machine-generated.

We developed GPU-Q-J, a fast graphics processor unit (GPU) method for calculating root mean square deviation (RMSD) between protein structures. This significantly speeds up large-scale structural comparisons, aiding projects like the Nutritious Rice for the World Project.

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Area of Science:

  • Structural bioinformatics
  • Computational biology
  • High-performance computing

Background:

  • Calculating root mean square deviation (RMSD) is crucial for comparing atomic coordinates of superimposed structures.
  • Existing methods are computationally intensive, posing a bottleneck for large datasets.

Purpose of the Study:

  • To introduce GPU-Q-J, a novel quaternion-based method for RMSD calculation.
  • To enhance the speed and efficiency of structural comparisons using graphics processor units (GPUs).

Main Methods:

  • Developed GPU-Q-J using C/C++ and Brook+ on Linux.
  • Implemented single-precision calculations for stability on GPUs.
  • Tested on an ATI 4770 graphics card.

Main Results:

  • The GPU-Q-J method demonstrated significant speedups, being 260 to 760 times faster than unoptimized CPU methods.
  • Applied to the Nutritious Rice for the World Project, it accelerated the calculation of similarity matrices for large protein structure ensembles.

Conclusions:

  • GPU-Q-J offers a substantial improvement over traditional CPU-based methods for RMSD calculation.
  • This method effectively addresses bottlenecks in clustering large numbers of protein structures.
  • GPU-Q-J has broad applications in large-scale structural comparisons across proteomes and genomes.