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BM-BC: a Bayesian method of base calling for Solexa sequence data

Overview of attention for article published in BMC Bioinformatics, August 2012
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Title
BM-BC: a Bayesian method of base calling for Solexa sequence data
Published in
BMC Bioinformatics, August 2012
DOI 10.1186/1471-2105-13-s13-s6
Pubmed ID
Authors

Yuan Ji, Riten Mitra, Fernando Quintana, Alejandro Jara, Peter Mueller, Ping Liu, Yue Lu, Shoudan Liang

Abstract

Base calling is a critical step in the Solexa next-generation sequencing procedure. It compares the position-specific intensity measurements that reflect the signal strength of four possible bases (A, C, G, T) at each genomic position, and outputs estimates of the true sequences for short reads of DNA or RNA. We present a Bayesian method of base calling, BM-BC, for Solexa-GA sequencing data. The Bayesian method builds on a hierarchical model that accounts for three sources of noise in the data, which are known to affect the accuracy of the base calls: fading, phasing, and cross-talk between channels. We show that the new method improves the precision of base calling compared with currently leading methods. Furthermore, the proposed method provides a probability score that measures the confidence of each base call. This probability score can be used to estimate the false discovery rate of the base calling or to rank the precision of the estimated DNA sequences, which in turn can be useful for downstream analysis such as sequence alignment.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
India 1 5%
Sweden 1 5%
Unknown 19 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 27%
Student > Ph. D. Student 3 14%
Student > Master 3 14%
Student > Bachelor 2 9%
Professor 2 9%
Other 4 18%
Unknown 2 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 45%
Medicine and Dentistry 3 14%
Biochemistry, Genetics and Molecular Biology 1 5%
Physics and Astronomy 1 5%
Computer Science 1 5%
Other 2 9%
Unknown 4 18%
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 19 January 2013.
All research outputs
#16,099,609
of 23,891,012 outputs
Outputs from BMC Bioinformatics
#5,488
of 7,455 outputs
Outputs of similar age
#109,859
of 170,935 outputs
Outputs of similar age from BMC Bioinformatics
#64
of 99 outputs
Altmetric has tracked 23,891,012 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
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