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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.

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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

Gene comparison of distribution and expression levels. Effect of data processing on expression (A) and fold change (B) distribution of differentially expressed genes.
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Figure 4: Gene comparison of distribution and expression levels. Effect of data processing on expression (A) and fold change (B) distribution of differentially expressed genes.

Mentions: Lastly, we investigated the effect of data preprocessing steps upon the expression and fold change levels in RNA-seq data. Removing duplicates had decreased the overall level of expression (Figure 4A), as can be expected from losing ~40% of reads. The effect of other processing steps on gene expression level was negligible, which is reflected in the virtually unchanged Pearson's correlation coefficient of RNA-seq and microarray gene expression data.


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)

Gene comparison of distribution and expression levels. Effect of data processing on expression (A) and fold change (B) distribution of differentially expressed genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Gene comparison of distribution and expression levels. Effect of data processing on expression (A) and fold change (B) distribution of differentially expressed genes.
Mentions: Lastly, we investigated the effect of data preprocessing steps upon the expression and fold change levels in RNA-seq data. Removing duplicates had decreased the overall level of expression (Figure 4A), as can be expected from losing ~40% of reads. The effect of other processing steps on gene expression level was negligible, which is reflected in the virtually unchanged Pearson's correlation coefficient of RNA-seq and microarray gene expression data.

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