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Animal Endo-SiRNAs

Overview of attention for book
Attention for Chapter 12: Computing siRNA and piRNA Overlap Signatures.
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2 tweeters

Citations

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Readers on

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13 Mendeley
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Chapter title
Computing siRNA and piRNA Overlap Signatures.
Chapter number 12
Book title
Animal Endo-SiRNAs
Published in
Methods in molecular biology, January 2014
DOI 10.1007/978-1-4939-0931-5_12
Pubmed ID
Book ISBNs
978-1-4939-0930-8, 978-1-4939-0931-5
Authors

Christophe Antoniewski, Antoniewski, Christophe

Abstract

High-throughput sequencing approaches opened the possibility to precisely map full populations of small RNAs to the genomic loci from which they originate. A bioinformatic approach revealed a strong tendency of sense and antisense piRNAs to overlap with each other over ten nucleotides and had a major role in understanding the mechanisms of piRNA biogenesis. Using similar approaches, it is possible to detect a tendency of sense and antisense siRNAs to overlap over 19 nucleotides. Thus, the so-called overlap signature which describes the tendency of small RNA to map in a specific way relative to each other has become the approach of choice to identify and characterize specific classes of small RNAs. Although simple in essence, the bioinformatic methods used for this approach are not easily accessible to biologists. Here we provide a python software that can be run on most of desktop or laptop computers to compute small RNA signatures from files of sequencing read alignments. Moreover, we describe and illustrate step by step two different algorithms at the core of the software and which were previously used in a number of works.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Student > Postgraduate 2 15%
Professor 1 8%
Student > Doctoral Student 1 8%
Other 1 8%
Other 2 15%
Unknown 2 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 38%
Agricultural and Biological Sciences 3 23%
Chemistry 2 15%
Environmental Science 1 8%
Unknown 2 15%

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 24 March 2019.
All research outputs
#11,043,791
of 14,539,325 outputs
Outputs from Methods in molecular biology
#3,859
of 8,757 outputs
Outputs of similar age
#119,328
of 190,081 outputs
Outputs of similar age from Methods in molecular biology
#22
of 109 outputs
Altmetric has tracked 14,539,325 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,757 research outputs from this source. They receive a mean Attention Score of 2.4. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 190,081 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.