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Thermodynamically optimal whole-genome tiling microarray design and validation

Overview of attention for article published in BMC Research Notes, June 2016
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Title
Thermodynamically optimal whole-genome tiling microarray design and validation
Published in
BMC Research Notes, June 2016
DOI 10.1186/s13104-016-2113-4
Pubmed ID
Authors

Hyejin Cho, Hui-Hsien Chou

Abstract

Microarray is an efficient apparatus to interrogate the whole transcriptome of species. Microarray can be designed according to annotated gene sets, but the resulted microarrays cannot be used to identify novel transcripts and this design method is not applicable to unannotated species. Alternatively, a whole-genome tiling microarray can be designed using only genomic sequences without gene annotations, and it can be used to detect novel RNA transcripts as well as known genes. The difficulty with tiling microarray design lies in the tradeoff between probe-specificity and coverage of the genome. Sequence comparison methods based on BLAST or similar software are commonly employed in microarray design, but they cannot precisely determine the subtle thermodynamic competition between probe targets and partially matched probe nontargets during hybridizations. Using the whole-genome thermodynamic analysis software PICKY to design tiling microarrays, we can achieve maximum whole-genome coverage allowable under the thermodynamic constraints of each target genome. The resulted tiling microarrays are thermodynamically optimal in the sense that all selected probes share the same melting temperature separation range between their targets and closest nontargets, and no additional probes can be added without violating the specificity of the microarray to the target genome. This new design method was used to create two whole-genome tiling microarrays for Escherichia coli MG1655 and Agrobacterium tumefaciens C58 and the experiment results validated the design.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 13%
Unknown 7 88%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 25%
Student > Ph. D. Student 2 25%
Student > Doctoral Student 1 13%
Lecturer > Senior Lecturer 1 13%
Professor > Associate Professor 1 13%
Other 0 0%
Unknown 1 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 50%
Biochemistry, Genetics and Molecular Biology 1 13%
Psychology 1 13%
Immunology and Microbiology 1 13%
Unknown 1 13%
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 15 June 2016.
All research outputs
#20,333,181
of 22,877,793 outputs
Outputs from BMC Research Notes
#3,563
of 4,268 outputs
Outputs of similar age
#305,071
of 352,763 outputs
Outputs of similar age from BMC Research Notes
#61
of 76 outputs
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