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

Overview of attention for article published in BMC Bioinformatics, December 2015
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

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7 tweeters

Citations

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10 Dimensions

Readers on

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81 Mendeley
Title
Detrimental effects of duplicate reads and low complexity regions on RNA- and ChIP-seq data
Published in
BMC Bioinformatics, December 2015
DOI 10.1186/1471-2105-16-s13-s10
Pubmed ID
Authors

Mikhail G Dozmorov, Indra Adrianto, Cory B Giles, Edmund Glass, Stuart B Glenn, Courtney Montgomery, Kathy L Sivils, Lorin E Olson, Tomoaki Iwayama, Willard M Freeman, Christopher J Lessard, Jonathan D Wren

Abstract

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. 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
France 1 1%
United Kingdom 1 1%
United States 1 1%
Sweden 1 1%
Unknown 76 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 33%
Researcher 23 28%
Student > Master 10 12%
Professor > Associate Professor 6 7%
Student > Bachelor 3 4%
Other 12 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 46%
Biochemistry, Genetics and Molecular Biology 28 35%
Unspecified 7 9%
Medicine and Dentistry 2 2%
Computer Science 2 2%
Other 5 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 June 2016.
All research outputs
#3,369,510
of 12,010,397 outputs
Outputs from BMC Bioinformatics
#1,623
of 4,370 outputs
Outputs of similar age
#73,524
of 245,533 outputs
Outputs of similar age from BMC Bioinformatics
#56
of 146 outputs
Altmetric has tracked 12,010,397 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 4,370 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 245,533 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.