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sRNAPipe: a Galaxy-based pipeline for bioinformatic in-depth exploration of small RNAseq data

Overview of attention for article published in Mobile DNA, July 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#34 of 358)
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog
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17 X users

Citations

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24 Dimensions

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60 Mendeley
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Title
sRNAPipe: a Galaxy-based pipeline for bioinformatic in-depth exploration of small RNAseq data
Published in
Mobile DNA, July 2018
DOI 10.1186/s13100-018-0130-7
Pubmed ID
Authors

Romain Pogorelcnik, Chantal Vaury, Pierre Pouchin, Silke Jensen, Emilie Brasset

Abstract

The field of small RNA is one of the most investigated research areas since they were shown to regulate transposable elements and gene expression and play essential roles in fundamental biological processes. Small RNA deep sequencing (sRNA-seq) is now routinely used for large-scale analyses of small RNA. Such high-throughput sequencing typically produces several millions reads. Here we present a computational pipeline (sRNAPipe: small RNA pipeline) based on the Galaxy framework that takes as input a fastq file of small RNA-seq reads and performs successive steps of mapping to categories of genomic sequences: transposable elements, gene transcripts, microRNAs, small nuclear RNAs, ribosomal RNAs and transfer RNAs. It also provides individual mapping and counting for chromosomes, transposable elements and gene transcripts, normalization, small RNA length analysis and plotting of the data along genomic coordinates to build publication-quality graphs and figures. sRNAPipe evaluates 10-nucleotide 5'-overlaps of reads on opposite strands to test ping-pong amplification for putative PIWI-interacting RNAs, providing counts of overlaps and corresponding z-scores. sRNAPipe is easy to use and does not require command-line or coding knowledge. This pipeline gives quick visual and quantitative results, which are usable for publications. sRNAPipe is freely available as a Galaxy tool and via GitHub.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 22%
Researcher 9 15%
Student > Master 8 13%
Student > Doctoral Student 5 8%
Student > Bachelor 5 8%
Other 7 12%
Unknown 13 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 28%
Agricultural and Biological Sciences 16 27%
Computer Science 5 8%
Unspecified 2 3%
Immunology and Microbiology 2 3%
Other 5 8%
Unknown 13 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 30 September 2023.
All research outputs
#2,148,102
of 24,981,585 outputs
Outputs from Mobile DNA
#34
of 358 outputs
Outputs of similar age
#42,926
of 335,532 outputs
Outputs of similar age from Mobile DNA
#3
of 11 outputs
Altmetric has tracked 24,981,585 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 358 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done particularly well, scoring higher than 90% of its peers.
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 335,532 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.