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For decades, computer designers have struggled with different ways to sidestep the complexity of programming multiple chips, in order to break up problems into pieces to be computed simultaneously so that they can be solved more quickly.
If we expand it into N's multiple chips in a cascaded manner, we can cope with various ranges of image resolutions.
Note that the gene expression profiles derived from multiple chips in the same experiment are counted as different hits.
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DePCRM predicts CRMs by identifying overrepresented combinatorial motif patterns in multiple ChIP datasets in an effective way.
DePCRM predicts CREs and CRMs by identifying overrepresented combinatorial CRE motif patterns in multiple ChIP datasets in an effective way.
Because each symbol is encoded in multiple chips, more than one stream, or channel, of symbols can be encoded in the same frame by using a different orthogonal channelization code for each channel.
In addition, the last two decades have seen an explosion in parallel computing, where problems are broken into smaller parts to be calculated on multiple chips.
Hence, multiple chips should be evaluated.
Our analysis thus suggests that integration of multiple ChIP datasets, especially in cell lines, is important to distinguish true positive from false positive ChIP peaks.
This is a trickier one to pull off, but because it's based on an established practice in multiple chip architectures, it's going to be even trickier to fix.
In this article, we present an integrated solution to this problem by developing a new approach, iASeq, for jointly analyzing allele-specificity in multiple ChIP-seq datasets.
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