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Shoelaces: an interactive tool for ribosome profiling processing and visualization

Overview of attention for article published in BMC Genomics, July 2018
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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9 X users

Citations

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15 Dimensions

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49 Mendeley
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Title
Shoelaces: an interactive tool for ribosome profiling processing and visualization
Published in
BMC Genomics, July 2018
DOI 10.1186/s12864-018-4912-6
Pubmed ID
Authors

Åsmund Birkeland, Katarzyna ChyŻyńska, Eivind Valen

Abstract

The emergence of ribosome profiling to map actively translating ribosomes has laid the foundation for a diverse range of studies on translational regulation. The data obtained with different variations of this assay is typically manually processed, which has created a need for tools that would streamline and standardize processing steps. We present Shoelaces, a toolkit for ribosome profiling experiments automating read selection and filtering to obtain genuine translating footprints. Based on periodicity, favoring enrichment over the coding regions, it determines the read lengths corresponding to bona fide ribosome protected fragments. The specific codon under translation (P-site) is determined by automatic offset calculations resulting in sub-codon resolution. Shoelaces provides both a user-friendly graphical interface for interactive visualisation in a genome browser-like fashion and a command line interface for integration into automated pipelines. We process 79 libraries and show that studies typically discard excessive amounts of quality data in their manual analysis pipelines. Shoelaces streamlines ribosome profiling analysis offering automation of the processing, a range of interactive visualization features and export of the data into standard formats. Shoelaces stores all processing steps performed in an XML file that can be used by other groups to exactly reproduce the processing of a given study. We therefore anticipate that Shoelaces can aid researchers by automating what is typically performed manually and contribute to the overall reproducibility of studies. The tool is freely distributed as a Python package, with additional instructions, tutorial and demo datasets available at https://bitbucket.org/valenlab/shoelaces .

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 18%
Student > Ph. D. Student 9 18%
Student > Master 7 14%
Student > Bachelor 3 6%
Student > Postgraduate 3 6%
Other 4 8%
Unknown 14 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 27%
Agricultural and Biological Sciences 12 24%
Medicine and Dentistry 3 6%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Social Sciences 2 4%
Other 3 6%
Unknown 14 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 25 November 2019.
All research outputs
#6,344,569
of 23,577,761 outputs
Outputs from BMC Genomics
#2,658
of 10,800 outputs
Outputs of similar age
#107,217
of 330,208 outputs
Outputs of similar age from BMC Genomics
#46
of 194 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 10,800 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 75% 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 330,208 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 194 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.