Biomedical Engineering Reference
In-Depth Information
coverage = overlaps->CalcIndexCoverage(MATCH_GAPS,IGNORE_STRAND,USE_
VALUES);
Progress PRG('Printing densities . . .',RefRegSet.n_regions);
for (long int k=0; k<RefRegSet.n_regions; k++) {
GenomicRegion *qreg = RefRegSet.R[k];
long int qreg_size = MATCH_GAPS? (qreg->I.back()->STOP-qreg->I.
front()->START+1): qreg->GetSize();
double density = (double)coverage[k]/qreg_size;
if (density>=MIN_DENSITY) printf('%s/t%.4e/n', qreg->LABEL, density);
PRG.Check();
}
PRG.Done();
delete coverage;
delete overlaps;
coverage = overlaps->CalcIndexCoverage(MATCH_GAPS,IGNORE_STRAND,USE_
VALUES);
Progress PRG('Printing densities . . .',RefRegSet.n_regions);
for (long int k=0; k<RefRegSet.n_regions; k++) {
GenomicRegion *qreg = RefRegSet.R[k];
long int qreg_size = MATCH_GAPS? (qreg->I.back()->STOP-qreg->I.
front()->START+1): qreg->GetSize();
double density = (double)coverage[k]/qreg_size;
if (density>=MIN_DENSITY) printf('%s/t%.4e/n', qreg->LABEL, density);
PRG.Check();
}
PRG.Done();
delete coverage;
delete overlaps;
8.5 Case study: a simple ChIP-seq pipeline
In this chapter, we demonstrate the utility of GenomicTools in constructing
a simple pipeline for ChIP-seq analysis. The pipeline helps accomplish the
following tasks: (1) produce data for popular plots such as read profi les
and read density heatmaps; (2) create genome browser tracks for
visualization; (3) identify peaks as potential binding sites; and (4) perform
an enrichment analysis. ChIP-seq studies are now widely used to elucidate
the molecular function of the cell under normal conditions as well as
under stress or disease (see for example [32-34]). As they reveal the
genomic positions of protein interactions, such as transcription factors
and histone modifi cation, with DNA, they can help create networks of
interactions and reveal undiscovered biological mechanisms. Our tools
help set up computational pipelines that drive this discovery. The
following examples use UNIX command-line functions, but they also run
on Cygwin under MS Windows.
8.5.1 Creating ChIP-seq read profi les
ChIP-seq read profi les are heavily used in ChIP-seq studies because they
offer an easy method for data validation regarding the relative position
of the ChIP-seq peaks (i.e. potential binding sites) with respect to chosen
genomic features, such as gene transcriptional start sites (TSSs) or binding
sites of other factors, such as enhancers. Additional validation is possible
if expression data are available and the transcription factor or histone
modifi cation mark under ChIP-seq investigation is activating or
repressive. In such a case, its read profi le can be computed separately for
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