Fig. 1

Visualizations of ggcoverage on selected NGS datasets. A ggcoverage on WGS data to visualize CNV. Genome coverage plot shows read counts of all bins. GC content annotation shows GC content of every bin (red line: mean GC content of the whole region). Copy number annotation shows normalized read counts of all bins in grey dot, estimated copy number in red line and ploidy in black line. Chromosome ideogram annotation shows the displayed region on the chromosome with red rectangle and highlights the centromeres with green rectangle. B ggcoverage on WGS data to visualize SNV. Genome coverage plot shows read counts of every locus. Base annotation shows base frequency (red line: 0.5) and reference base of every locus. Candidate SNV is highlighted with the twill mark. Amino acid annotation shows corresponding amino acids with 0, 1, 2 offsets. C ggcoverage on ChIP-seq data. Different from (A), genome coverage plot discriminates sample groups (the first track in red is the ChIP sample and the last track in grey is the control sample), the light grey rectangle indicates the highlight region. Gene annotation shows genes in given region (rightwards arrow with dark green: gene on plus strand, leftwards arrow with dark blue: gene on minus strand; element height: gene part (exon > UTR > intron)). Peak annotation shows all peaks identified. D ggcoverage on RNA-seq data with HNRNPC knockdown. Different from (A), we use transcript annotation instead of gene annotation to visualize gene’s all transcripts