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Identification and removal of low-complexity sites in allele-specific analysis of ChIP-seq data

Overview of attention for article published in Bioinformatics, November 2013
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Title
Identification and removal of low-complexity sites in allele-specific analysis of ChIP-seq data
Published in
Bioinformatics, November 2013
DOI 10.1093/bioinformatics/btt667
Pubmed ID
Authors

Sebastian M. Waszak, Helena Kilpinen, Andreas R. Gschwind, Andrea Orioli, Sunil K. Raghav, Robert M. Witwicki, Eugenia Migliavacca, Alisa Yurovsky, Tuuli Lappalainen, Nouria Hernandez, Alexandre Reymond, Emmanouil T. Dermitzakis, Bart Deplancke

Abstract

High-throughput sequencing technologies enable the genome-wide analysis of the impact of genetic variation on molecular phenotypes at unprecedented resolution. However, although powerful, these technologies can also introduce unexpected artifacts. Results: We investigated the impact of library amplification bias on the identification of allele-specific (AS) molecular events from high-throughput sequencing data derived from chromatin immunoprecipitation assays (ChIP-seq). Putative AS DNA binding activity for RNA polymerase II was determined using ChIP-seq data derived from lymphoblastoid cell lines of two parent-daughter trios. We found that, at high-sequencing depth, many significant AS binding sites suffered from an amplification bias, as evidenced by a larger number of clonal reads representing one of the two alleles. To alleviate this bias, we devised an amplification bias detection strategy, which filters out sites with low read complexity and sites featuring a significant excess of clonal reads. This method will be useful for AS analyses involving ChIP-seq and other functional sequencing assays.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 6%
France 1 3%
Unknown 31 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 41%
Student > Ph. D. Student 7 21%
Professor 5 15%
Student > Master 2 6%
Student > Doctoral Student 1 3%
Other 5 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 56%
Biochemistry, Genetics and Molecular Biology 11 32%
Mathematics 3 9%
Computer Science 1 3%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 21 January 2014.
All research outputs
#7,302,411
of 25,374,647 outputs
Outputs from Bioinformatics
#6,059
of 12,809 outputs
Outputs of similar age
#79,104
of 315,757 outputs
Outputs of similar age from Bioinformatics
#98
of 185 outputs
Altmetric has tracked 25,374,647 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 12,809 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has gotten more attention than average, scoring higher than 52% 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 315,757 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 74% of its contemporaries.
We're also able to compare this research output to 185 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.