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Detrimental effects of duplicate reads and low complexity regions on RNA- and ChIP-seq data.

Dozmorov MG, Adrianto I, Giles CB, Glass E, Glenn SB, Montgomery C, Sivils KL, Olson LE, Iwayama T, Freeman WM, Lessard CJ, Wren JD - BMC Bioinformatics (2015)

Bottom Line: We investigate how trimming the adapters, removing duplicates, and filtering out reads overlapping low complexity regions influence the significance of biological signal in RNA- and ChIP-seq experiments.We assessed the effect of data processing steps on the alignment statistics and the functional enrichment analysis results of RNA- and ChIP-seq data.We compared differentially processed RNA-seq data with matching microarray data on the same patient samples to determine whether changes in pre-processing improved correlation between the two.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Adapter trimming and removal of duplicate reads are common practices in next-generation sequencing pipelines. Sequencing reads ambiguously mapped to repetitive and low complexity regions can also be problematic for accurate assessment of the biological signal, yet their impact on sequencing data has not received much attention. We investigate how trimming the adapters, removing duplicates, and filtering out reads overlapping low complexity regions influence the significance of biological signal in RNA- and ChIP-seq experiments.

Methods: We assessed the effect of data processing steps on the alignment statistics and the functional enrichment analysis results of RNA- and ChIP-seq data. We compared differentially processed RNA-seq data with matching microarray data on the same patient samples to determine whether changes in pre-processing improved correlation between the two. We have developed a simple tool to remove low complexity regions, RepeatSoaker, available at https://github.com/mdozmorov/RepeatSoaker, and tested its effect on the alignment statistics and the results of the enrichment analyses.

Results: Both adapter trimming and duplicate removal moderately improved the strength of biological signals in RNA-seq and ChIP-seq data. Aggressive filtering of reads overlapping with low complexity regions, as defined by RepeatMasker, further improved the strength of biological signals, and the correlation between RNA-seq and microarray gene expression data.

Conclusions: Adapter trimming and duplicates removal, coupled with filtering out reads overlapping low complexity regions, is shown to increase the quality and reliability of detecting biological signals in RNA-seq and ChIP-seq data.

No MeSH data available.


Related in: MedlinePlus

RepeatSoaker comparisons. Overview of the various permutations of the three data processing steps compared.
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Figure 1: RepeatSoaker comparisons. Overview of the various permutations of the three data processing steps compared.

Mentions: To elucidate the effects of adapter removal, elimination of duplicates, and filtering of low complexity regions, we performed systematic testing of sequencing data with and without applying these three preprocessing steps (Figure 1). At each step, we compared the alignment statistics, the number of differentially expressed genes (RNA-seq) or identified transcription factor binding peaks (ChIP-seq), and the results of Gene Ontology, KEGG and Reactome pathway enrichment analyses (RNA-seq) and motif enrichment analyses (ChIP-seq). We also compared combinations of data preprocessing steps, e.g., how duplicate removal affected trimmed and untrimmed data. At each comparison, we evaluated how the data preprocessing steps affected biological signals as judged by the functional enrichment analysis.


Detrimental effects of duplicate reads and low complexity regions on RNA- and ChIP-seq data.

Dozmorov MG, Adrianto I, Giles CB, Glass E, Glenn SB, Montgomery C, Sivils KL, Olson LE, Iwayama T, Freeman WM, Lessard CJ, Wren JD - BMC Bioinformatics (2015)

RepeatSoaker comparisons. Overview of the various permutations of the three data processing steps compared.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4597324&req=5

Figure 1: RepeatSoaker comparisons. Overview of the various permutations of the three data processing steps compared.
Mentions: To elucidate the effects of adapter removal, elimination of duplicates, and filtering of low complexity regions, we performed systematic testing of sequencing data with and without applying these three preprocessing steps (Figure 1). At each step, we compared the alignment statistics, the number of differentially expressed genes (RNA-seq) or identified transcription factor binding peaks (ChIP-seq), and the results of Gene Ontology, KEGG and Reactome pathway enrichment analyses (RNA-seq) and motif enrichment analyses (ChIP-seq). We also compared combinations of data preprocessing steps, e.g., how duplicate removal affected trimmed and untrimmed data. At each comparison, we evaluated how the data preprocessing steps affected biological signals as judged by the functional enrichment analysis.

Bottom Line: We investigate how trimming the adapters, removing duplicates, and filtering out reads overlapping low complexity regions influence the significance of biological signal in RNA- and ChIP-seq experiments.We assessed the effect of data processing steps on the alignment statistics and the functional enrichment analysis results of RNA- and ChIP-seq data.We compared differentially processed RNA-seq data with matching microarray data on the same patient samples to determine whether changes in pre-processing improved correlation between the two.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Adapter trimming and removal of duplicate reads are common practices in next-generation sequencing pipelines. Sequencing reads ambiguously mapped to repetitive and low complexity regions can also be problematic for accurate assessment of the biological signal, yet their impact on sequencing data has not received much attention. We investigate how trimming the adapters, removing duplicates, and filtering out reads overlapping low complexity regions influence the significance of biological signal in RNA- and ChIP-seq experiments.

Methods: We assessed the effect of data processing steps on the alignment statistics and the functional enrichment analysis results of RNA- and ChIP-seq data. We compared differentially processed RNA-seq data with matching microarray data on the same patient samples to determine whether changes in pre-processing improved correlation between the two. We have developed a simple tool to remove low complexity regions, RepeatSoaker, available at https://github.com/mdozmorov/RepeatSoaker, and tested its effect on the alignment statistics and the results of the enrichment analyses.

Results: Both adapter trimming and duplicate removal moderately improved the strength of biological signals in RNA-seq and ChIP-seq data. Aggressive filtering of reads overlapping with low complexity regions, as defined by RepeatMasker, further improved the strength of biological signals, and the correlation between RNA-seq and microarray gene expression data.

Conclusions: Adapter trimming and duplicates removal, coupled with filtering out reads overlapping low complexity regions, is shown to increase the quality and reliability of detecting biological signals in RNA-seq and ChIP-seq data.

No MeSH data available.


Related in: MedlinePlus