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A 3rd order Markov model background was generated using a combination of the oligo-analysis and random-seq programs from a local installation of the RSAT package [ 10].
As both our data sets of experimentally verified TFBSs and false positives are derived from non-coding upstream sequences, we selected this available option in the random-seq tool.
Finally, two data sets of 232 random DNA sequences with a length of 6 kb were generated using the random-seq application included in the RSA-tools from van Helden [ 16].
Only regions covered by the random 4C-seq libraries are shown.
Only the region covered by the random 4C-seq library is shown.
Different transcripts are discriminated by their 3′-most tags and expression levels estimated with higher accuracy than could be achieved by random RNA-Seq [ 52].
Fragments are classified as positive either in ES (orange) or in E10.5 forebrain (green) cells within the region covered by the random 4C-seq libraries.
Then the frequency distribution of the number of random ChIP-seq DNA fragments in a cluster overlap (peak heights) was constructed and the threshold value at the given specificity level for the original empirical frequency distribution of the peak height value was defined.
Random corrected 4C-seq and ChIP-seq samples were correlated by ranking experimental values within restriction fragments (Table 1).
To search for a more realistic frequency distribution representing Chip-seq data, 224 random samples of the 94 ChIP-seq loci were chosen without replacement from the same ChIP-seq library.
random T compatible mutate seq p 20. end for 21.
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