The recorded digital data were stored in a file on a hard disk that could be read by the MATLAB® technical computing environment for further processing.
For each potential binary codon reading, the codon frequency is computed as the sum of all occurrences of the codons that can be read by a given tRNA.
Larger chunks of raw data are read and sent to the dedicated computing nodes at once, significantly improving the performance.
Raw intensity data was read into the R computing environment using the LIMMA package for two-color array analysis and checked for quality.
The sum of codon counts x i that is read by the ith tRNA is computed by where n c is a normalization factor to account for multiple codon reading by tRNA i.
For the chosen match point, four parameters (two from each axis of both observed time-drawdown curve and type curve) are read, and the aquifer parameters were computed using Eqs.
The computed tomography scans were read centrally at the Department of Radiology Bio-Imaging Research Labofathey of the University of Colorado Health Sciences Center.
MaSuRCA's error-correction and super-reads processes reduced the raw paired-end reads to 2,145,090 super-reads that were applied to compute pairwise overlap between these reads.
The alignment files generated from TopHat were used to compute read counts for each gene in the reference annotation (Berkeley Drosophila Genome Project release 5).
Sometimes it may be enough to compute correlation statistics for read counts with genomic landmarks such as genes, or to calculate average tag-density profiles around a set of such landmarks.
