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Modeling and correct the GC bias of tumor and normal WGS data for SCNA based tumor subclonal population inferring

Overview of attention for article published in BMC Bioinformatics, April 2018
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Title
Modeling and correct the GC bias of tumor and normal WGS data for SCNA based tumor subclonal population inferring
Published in
BMC Bioinformatics, April 2018
DOI 10.1186/s12859-018-2099-0
Pubmed ID
Authors

Yanshuo Chu, Mingxiang Teng, Yadong Wang

Abstract

Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios between tumor-normal paired samples serve as the inferring proxy. Existing SCNA based subclonal population inferring tools consider the GC bias of tumor and normal sample is of the same fature, and could be fully offset by read count ratio. However, we found that, the read count ratio on SCNA segments presents a Log linear biased pattern, which influence existing read count ratios based subclonal inferring tools performance. Currently no correction tools take into account the read ratio bias. We present Pre-SCNAClonal, a tool that improving tumor subclonal population inferring by correcting GC-bias at SCNAs level. Pre-SCNAClonal first corrects GC bias using Markov chain Monte Carlo probability model, then accurately locates baseline DNA segments (not containing any SCNAs) with a hierarchy clustering model. We show Pre-SCNAClonal's superiority to exsiting GC-bias correction methods at any level of subclonal population. Pre-SCNAClonal could be run independently as well as serving as pre-processing/gc-correction step in conjuntion with exsiting SCNA-based subclonal inferring tools.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 42%
Researcher 3 25%
Student > Master 2 17%
Student > Bachelor 1 8%
Unknown 1 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 42%
Chemical Engineering 1 8%
Agricultural and Biological Sciences 1 8%
Computer Science 1 8%
Immunology and Microbiology 1 8%
Other 2 17%
Unknown 1 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 12 April 2018.
All research outputs
#20,480,611
of 23,041,514 outputs
Outputs from BMC Bioinformatics
#6,893
of 7,318 outputs
Outputs of similar age
#290,344
of 329,169 outputs
Outputs of similar age from BMC Bioinformatics
#90
of 106 outputs
Altmetric has tracked 23,041,514 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,318 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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