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Statistical Approaches to Detecting and Analyzing Tandem Repeats in Genomic Sequences

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, March 2015
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Title
Statistical Approaches to Detecting and Analyzing Tandem Repeats in Genomic Sequences
Published in
Frontiers in Bioengineering and Biotechnology, March 2015
DOI 10.3389/fbioe.2015.00031
Pubmed ID
Authors

Maria Anisimova, Julija Pečerska, Elke Schaper

Abstract

Tandem repeats (TRs) are frequently observed in genomes across all domains of life. Evidence suggests that some TRs are crucial for proteins with fundamental biological functions and can be associated with virulence, resistance, and infectious/neurodegenerative diseases. Genome-scale systematic studies of TRs have the potential to unveil core mechanisms governing TR evolution and TR roles in shaping genomes. However, TR-related studies are often non-trivial due to heterogeneous and sometimes fast evolving TR regions. In this review, we discuss these intricacies and their consequences. We present our recent contributions to computational and statistical approaches for TR significance testing, sequence profile-based TR annotation, TR-aware sequence alignment, phylogenetic analyses of TR unit number and order, and TR benchmarks. Importantly, all these methods explicitly rely on the evolutionary definition of a tandem repeat as a sequence of adjacent repeat units stemming from a common ancestor. The discussed work has a focus on protein TRs, yet is generally applicable to nucleic acid TRs, sharing similar features.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 2%
France 1 2%
Switzerland 1 2%
Unknown 46 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 22%
Student > Master 9 18%
Student > Bachelor 8 16%
Researcher 7 14%
Student > Doctoral Student 2 4%
Other 7 14%
Unknown 5 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 31%
Agricultural and Biological Sciences 15 31%
Computer Science 6 12%
Engineering 2 4%
Nursing and Health Professions 1 2%
Other 4 8%
Unknown 6 12%
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 09 April 2015.
All research outputs
#18,403,994
of 22,796,179 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#3,388
of 6,524 outputs
Outputs of similar age
#209,339
of 286,345 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#33
of 44 outputs
Altmetric has tracked 22,796,179 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,524 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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