While resampling itself requires a linear increase in the amount of computer time necessary for a particular calculation, the increased dataset size made possible by the new data collection methods causes the needed computer time to increase substantially as well.
Therefore, an interesting dilemma may arise when building a new search dataset, between seeking a better signal-to-noise ratio to enhance sensitivity and increasing dataset size for extended motif coverage.
The % correct increased with increasing dataset size; the 100, 500, 1000, and 2000 individuals datasets gave % correct of 71.61%, 96.19%, 98.14%, and 99.11%, respectively, suggesting that datasets of less than 1000 individuals are too small for providing accurate phasing results.
Our quantitative evaluations demonstrate that Cabinet Tree achieves good scalability for increased resolutions and big datasets.
PCR amplification was performed in two steps using nested outer- and inner-primer pairs (See Supplementary dataset 4) for increased target specificity, with PCR conditions described in (Natanaelsson et al., 2006).
For increased reliability of final classification, our dataset of 10,884 UTs was screened for candidate transcripts with an identical prediction by both coding potential prediction tools, CPAT and CPC.
At the same time, the CpGs associated with the outcome of interest were dramatically increased: for Dataset 2, there were 759, 714, and 763 CpG associated with case and control status for QNβ, lumi, and ABnorm normalized data, respectively.
However, the potential for increasing the size of empirical datasets to the point where the branch lengths may be estimated even within 10% in a Bayesian framework (e.g. >10 kb for depth 1 branch lengths >1.4 substitutions/site) is limited.
Within each dataset, the trend for increasing imputation accuracy with increasing levels of genotyping of immediate ancestors (i.e. parents and grandparents) that was observed for mapped SNP, was also observed for unmapped SNP.
Both features are critically important for increasing the power of identifying co-expressed genes in large scale gene expression datasets.
