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One is based on the total tag count (#specific microRNA tags/ total microRNA sequence tags) and the other is based on the most abundant tag count (#the most abundant tag of specific microRNA/ total microRNA sequence tags).
Table 5 presents an analysis of the tags distribution with respect to the total tag count (and considering service granularity).
This output includes the GSM number, library name, tag count, kinds of tag (with unrepeated tag sequences), total tag count, and TPM.
For comparison, we normalized the RNA seq tag count in the same manner as the SILAC data (i.e. we expressed the transcript tag count from LAU-Me275 with respect to all other samples, including the control melanocytes).
Therefore, we follow the criteria described in the Mouse SAGE (http://mouse.img.cas.cz/sage/help.cgi subj=compare) [14], i.e., the fold factor is computed simply by dividing normalized tag count in pool #2 by normalized tag count in pool #1.
Previous studies carried out window-based approach (tag count within a fixed-size window) to model relationships among epigenomic features.
In addition, a minimal average normalized tag count of 40 TPM was required in the over-expressing dataset.
To test this hypothesis, we divided the ChIP-seq peaks into sets according to peak tag count.
Tag count profiles of NucPos were transformed into logarithm scale.
However, not every genomic position is assigned a tag count.
aAverage tag count count per individaul tunor SAGE library.
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