Statistical guidelines for quality control of next-generation sequencing techniques

High- and low-quality files are defined using their label in ENCODE (released and revoked, respectively). The raw FastQ files are used as input for FastQC and Bowtie2 tools, respectively returning RAW and MAP feature sets. The mapped reads from Bowtie2 are used as input for ChIPseeker and ChIPpeakAnno packages, returning the LOC and MAP feature sets, respectively.

Horizontal dashed lines are minimal values given as guidelines. Guidelines as well as files are all from ENCODE version 3 (2013–2018). (A) Number of aligned reads for H3K9me3, ChIP-Seq, and RNA-Seq. The blue dashed line denotes 45 million reads given as minimal guideline for H3K9me3 ChIP-Seq and the red line denotes 30 million reads given as minimal guideline for RNA-Seq. (B) Uniquely mapped reads for DNAse-Seq data. The orange dashed line indicates the ENCODE guideline of 20 million reads. (C) Useable fragments for narrow and broad peak ChIP-Seq. The blue dashed line is at 45 million and the red dashed line at 20 million, indicating ENCODEs guidelines for broad and narrow peak data, respectively.

(A) From left to right: FastQC’s raw sequence median quality score (5), uniquely mapped reads ratio of BWA’s mapping (7), PCR bottleneck coefficient (PBC), fraction of reads in peaks (FRiP) (17), proportion of the 500 most significant peaks overlapping with a union of DNase-seq peaks derived from ENCODE files (PeaksUnionDHSRatio) (20, 34), and number of peaks called by MACS2 with a fold change above 10 (PeaksFoldChangeAbove10). The thresholds for the features are: 0.25, 0.6, 0.8, 0.01, 0.7, and 500, respectively. The x-axis shows how many quality flags were indicating low quality excluding the flag of feature being represented. Boxplots do not show outlier points. (B) Pairwise Pearson’s correlation coefficients of Cistrome’s features. (C) Pearson’s, Spearman’s and Kendall’s correlation coefficients of each Cistrome’s feature with the number of low-quality flags of the other features.

The data were filtered for polyA plus RNA-seq files, a higher level data subset compared to subsets defined for groups A, B, and C. Two example features were selected: per sequence quality score (top row) and multiple mapping (bottom row). On the left-hand side, boxplots show the distributions of values for high- (blue) and low-quality (orange) files for the respective features. On the right-hand side, the histograms of values are shown. Yet, using the dashboard, we can conclude that the multiple mapping feature is more powerful than per sequence quality score to differentiate files by quality.

For each of the three subset groups (A, B, and C), the number of subsets (y-axis) in which a quality feature is significant is shown. Subsets have a minimum of 10 files and the P-value cutoff for significance is defined as either 0.01 (top row), 0.001 (middle row) or 0.0001 (bottom row). The total number of subsets in each subset group is 436 for subset group A, 354 for B and 461 for C. The number of subsets with at least 10 files in each subset group is 38 for subset group A, 41 for B and 33 for C.