The methods for error estimation and interface treatment are discussed.
Update: A new support document on Apple's site has been issued that details the causes and repair methods for Error 53.
Our automatic methods for error mitigation cannot replace the need for additional sequencing, but they do allow substantial fractions of errors to be masked or eliminated at the cost of modest amounts of over-correction, and they can reduce the impact of error in downstream phylogenomic analyses.
We discuss and compare methods for error control for pathway analysis with gene expression data.
Bootstrap and jackknife are computer intensive statistical methods for error estimation [ 1, 2].
As a result, automated methods for error detection were less efficient at detecting sequencing errors and many incorrect base calls were detected upon visual inspection of assembled trace data.
Bayesian methods for error rate estimation can also be used to improve data integration from multiple independent screens, as has been reported for studies of co-complex membership (Gilchrist et al., 2004).
A second advantage of the software program is its use of advanced methods for error correction in utility elicitations that have been proven to yield more accurate population estimates of utility values [ 31].
In order to eliminate errors due to enzyme misincorporations, template switching and other PCR artifacts, multiple clones from separate PCR reactions were sequenced, and an error rate of ~0.2% was allowed among the clones (see Methods for error rate calculations, and number of clones sequenced).
It is well established that raw sequencing data would drastically overestimate the number of unique clones and the only way to fully overcome this would be to apply sophisticated experimental and bioinformatic methods for error correction (see Shugay et al. [ 21 ] for more information).
Then we shall give the method for error verification of the obtained results.
