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A general method for implementing this analysis will be presented.
In implementing this analysis, t is chosen such that it encompasses a single round of cell division so that the initial and final states correspond to parent and daughter cells respectively.
However, the program implementing this analysis was designed for processing conventional sequence reads of at least several hundred nucleotides in length and could not be adapted to work with the short 454 sequences.
However, despite implementing this analysis pipeline only 6 of the 11 top candidates identified by RNA-seq were validated, emphasizing the importance of biological replicates and validation by independent techniques to confirm imprinted expression [ 48 ].
An open source toolbox, EEGLAB (Swartz Center for Computational Neuroscience [University of California, San Diego]; La Jolla, CA), implementing this analysis approach is freely available (http://www.sccn.ucsd.edu/eeglab [ 41]).
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To implement this analysis, some additional computational efforts can be justified.
We implement this analysis in a SmartCM tool; thereafter evaluate its capacity to detect such a fault based mutant.
In this work, we implement this analysis step on graphics processing units (GPUs), an architecture that is optimized for massively parallel computation.
We used gada package of R software (http://www.r-project.org/) to implement this analysis.
We implement this analysis strategy at the design stage by decreasing the significance level used in a conventional power computation.
We then implement this analysis on longitudinal 11C-PiB scans in human subjects and compare the outcomes of wS2 with currently used mean/median SUVR and cerebrospinal fluid (CSF) measures of Aβ.
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