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Je, a versatile suite to handle multiplexed NGS libraries with unique molecular identifiers

Overview of attention for article published in BMC Bioinformatics, October 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

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2 blogs
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10 X users

Citations

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132 Dimensions

Readers on

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125 Mendeley
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2 CiteULike
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Title
Je, a versatile suite to handle multiplexed NGS libraries with unique molecular identifiers
Published in
BMC Bioinformatics, October 2016
DOI 10.1186/s12859-016-1284-2
Pubmed ID
Authors

Charles Girardot, Jelle Scholtalbers, Sajoscha Sauer, Shu-Yi Su, Eileen E.M. Furlong

Abstract

The yield obtained from next generation sequencers has increased almost exponentially in recent years, making sample multiplexing common practice. While barcodes (known sequences of fixed length) primarily encode the sample identity of sequenced DNA fragments, barcodes made of random sequences (Unique Molecular Identifier or UMIs) are often used to distinguish between PCR duplicates and transcript abundance in, for example, single-cell RNA sequencing (scRNA-seq). In paired-end sequencing, different barcodes can be inserted at each fragment end to either increase the number of multiplexed samples in the library or to use one of the barcodes as UMI. Alternatively, UMIs can be combined with the sample barcodes into composite barcodes, or with standard Illumina® indexing. Subsequent analysis must take read duplicates and sample identity into account, by identifying UMIs. Existing tools do not support these complex barcoding configurations and custom code development is frequently required. Here, we present Je, a suite of tools that accommodates complex barcoding strategies, extracts UMIs and filters read duplicates taking UMIs into account. Using Je on publicly available scRNA-seq and iCLIP data containing UMIs, the number of unique reads increased by up to 36 %, compared to when UMIs are ignored. Je is implemented in JAVA and uses the Picard API. Code, executables and documentation are freely available at http://gbcs.embl.de/Je . Je can also be easily installed in Galaxy through the Galaxy toolshed.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Germany 1 <1%
United Kingdom 1 <1%
Denmark 1 <1%
Argentina 1 <1%
Spain 1 <1%
China 1 <1%
Unknown 116 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 29%
Student > Master 23 18%
Student > Ph. D. Student 22 18%
Student > Bachelor 7 6%
Student > Doctoral Student 4 3%
Other 11 9%
Unknown 22 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 42 34%
Agricultural and Biological Sciences 36 29%
Medicine and Dentistry 9 7%
Immunology and Microbiology 3 2%
Physics and Astronomy 3 2%
Other 7 6%
Unknown 25 20%
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 08 November 2023.
All research outputs
#2,145,831
of 25,321,938 outputs
Outputs from BMC Bioinformatics
#490
of 7,672 outputs
Outputs of similar age
#36,298
of 328,533 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 133 outputs
Altmetric has tracked 25,321,938 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 7,672 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 93% 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 328,533 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 88% of its contemporaries.
We're also able to compare this research output to 133 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.