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To assign genes to mapped ChIP peaks, we used intersectBed and closestBed and subsequently manually pruned the gene lists.
We define a list of uniquely mapped ChIP-seq DNA sequences observed in a given ChIP-seq experiment as the Chip-seq library.
In the absence of a reference genome, we mapped ChIP-seq data to the clusters of the C. aquatica HG genome for cluster-based repeat identification.
In order to control for such errors, we used only uniquely mapped ChIP-seq reads, thus excluding fragments exhibiting dual genomic/mtDNA localization.
(A ) The mapped ChIP-Seq signals for the listed modifications were derived from the total signal over the gene-body (green) or 2.5 kb promoter region (red).
We mapped ChIP-Seq and ChIP-Chip derived transcription factor binding sites obtained from ENCODE, CISTROME and Kittler et al. to all RefSeq gene promoters in order to calculate in-silico ChIP-Seq binding enrichment.
Consistent with these data and those published in a previous report [ 18], analysis of the uniquely mapped ChIP-seq reads reveals a high level of H3K9me3 in the regions flanking IAPEz, MusD and MLV ERVs.
The mapped ChIP-seq datasets were downloaded from T2G GIS DB (http://t2g.bii.a-star.edu.sg; see also NIH GEO ID GSE 11431).The extended ChIP-seq DNA fragments were clustered and the number of overlapping fragments were summed at each locus and used to construct empirical frequency distribution of TF-DNA binding.
The genome-wide binding profiles of hundreds of proteins have been mapped using ChIP-chip and ChIP-seq in organisms ranging from bacteria to humans.
Transcriptome analysis using SAGE and microarrays has been performed in various tissues [8], [9], recently in combination with chromatin immunoprecipitation based mapping (ChIP-chip) of genomewide GR occupancy [10].
For mapping ChIP-Seq data to genomic scaffolds, we considered only unique hits (-m 1 option).
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