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Elucidating tissue specific genes using the Benford distribution

Overview of attention for article published in BMC Genomics, August 2016
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Title
Elucidating tissue specific genes using the Benford distribution
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2921-x
Pubmed ID
Authors

Deepak Karthik, Gil Stelzer, Sivan Gershanov, Danny Baranes, Mali Salmon-Divon

Abstract

The RNA-seq technique is applied for the investigation of transcriptional behaviour. The reduction in sequencing costs has led to an unprecedented trove of gene expression data from diverse biological systems. Subsequently, principles from other disciplines such as the Benford law, which can be properly judged only in data-rich systems, can now be examined on this high-throughput transcriptomic information. The Benford law, states that in many count-rich datasets the distribution of the first significant digit is not uniform but rather logarithmic. All tested digital gene expression datasets showed a Benford-like distribution when observing an entire gene set. This phenomenon was conserved in development and does not demonstrate tissue specificity. However, when obedience to the Benford law is calculated for individual expressed genes across thousands of cells, genes that best and least adhere to the Benford law are enriched with tissue specific or cell maintenance descriptors, respectively. Surprisingly, a positive correlation was found between the obedience a gene exhibits to the Benford law and its expression level, despite the former being calculated solely according to first digit frequency while totally ignoring the expression value itself. Nevertheless, genes with low expression that exhibit Benford behavior demonstrate tissue specific associations. These observations were extended to predict the likelihood of tissue specificity based on Benford behaviour in a supervised learning approach. These results demonstrate the applicability and potential predictability of the Benford law for gleaning biological insight from simple count data.

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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 %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 18%
Researcher 4 18%
Student > Master 3 14%
Unspecified 2 9%
Other 1 5%
Other 4 18%
Unknown 4 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 32%
Biochemistry, Genetics and Molecular Biology 4 18%
Unspecified 2 9%
Computer Science 2 9%
Mathematics 1 5%
Other 1 5%
Unknown 5 23%

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 02 September 2017.
All research outputs
#16,711,744
of 20,753,228 outputs
Outputs from BMC Genomics
#7,697
of 10,132 outputs
Outputs of similar age
#217,901
of 288,594 outputs
Outputs of similar age from BMC Genomics
#13
of 17 outputs
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