ChIP-Seq: High-resolution genome-wide in vivo footprinting of diverse transcription f

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High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells.

Genome Res. 2010 Nov 24;

Authors: Boyle AP, Song L, Lee BK, London D, Keefe D, Birney E, Iyer VR, Crawford GE, Furey TS

Regulation of gene transcription in diverse cell types is largely determined by varied sets of cis-elements where transcription factors bind. Here we demonstrate that data from a single high-throughput DNaseI hypersensitivity assay can delineate hundreds of thousands of base-pair resolution in vivo footprints in human cells that precisely mark individual transcription factor-DNA interactions. These annotations provide a unique resource for the investigation of cis-regulatory elements. We find that footprints for specific transcription factors correlate with ChIP-seq enrichment and can accurately identify functional vs. non-functional transcription factor motifs. We also find that footprints reveal a unique evolutionary conservation pattern that differentiates functional footprinted bases from surrounding DNA. Finally, detailed analysis of CTCF footprints suggests multiple modes of binding and a novel DNA binding motif upstream of the primary binding site.

PMID: 21106903 [PubMed - as supplied by publisher]



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[ttnguyen]

Hi, I downloaded DNase-seq data from ENCODE (http://hgdownload.cse.ucsc.edu/golde...eChromatinMap/) - probably the data used in this paper and I tried to re-map them using Bowtie with the parameters (-m 4 -v 2) as described in this paper (I quoted below). However, the mapping rate was very low, i.e. less than 10% of reads were reported and around 90% of reads were suppressed due to mapping to more than 4 locations. I think this was because read length of the DNase-seq data is too short (20bp) .

So, I applied another mapping option (-v 0 -m 1) that does not allow any mismatches and reports only unique-mapped reads, and I found that the mapping rate was dramatically increased (~ 60% of reads were reported; ~ 10% of reads failed to map). I am just wondering if this option is OK for the DNase-seq data or if anyone can give some suggestions for mapping this kind of data?

Many thanks!

Raw sequence data from technical replicates for each cell type were combined and aligned to the March 2006 (hg18) assembly with MAQ (Li et al. 2008). We retained all sequences that aligned to, at most, four genomic locations and that contained, at most, two mismatches. Those sequences that aligned to multiple genomic locations were randomly assigned one of these locations by MAQ.