↓ Skip to main content

sciReptor: analysis of single-cell level immunoglobulin repertoires

Overview of attention for article published in BMC Bioinformatics, February 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 X users
facebook
1 Facebook page

Citations

dimensions_citation
31 Dimensions

Readers on

mendeley
101 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
sciReptor: analysis of single-cell level immunoglobulin repertoires
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0920-1
Pubmed ID
Authors

Katharina Imkeller, Peter F. Arndt, Hedda Wardemann, Christian E. Busse

Abstract

The sequencing of immunoglobulin (Ig) transcripts from single B cells yields essential information about Ig heavy:light chain pairing, which is lost in conventional bulk sequencing experiments. The previously limited throughput of single-cell approaches has recently been overcome by the introduction of multiple next-generation sequencing (NGS)-based platforms. Furthermore, single-cell techniques allow the assignment of additional data types (e.g. cell surface marker expression), which are crucial for biological interpretation. However, the currently available computational tools are not designed to handle single-cell data and do not provide integral solutions for linking of sequence data to other biological data. Here we introduce sciReptor, a flexible toolkit for the processing and analysis of antigen receptor repertoire sequencing data at single-cell level. The software combines bioinformatics tools for immunoglobulin sequence annotation with a relational database, where raw data and analysis results are stored and linked. sciReptor supports attribution of additional data categories such as cell surface marker expression or immunological metadata. Furthermore, it comprises a quality control module as well as basic repertoire visualization tools. sciReptor is a flexible framework for standardized sequence analysis of antigen receptor repertoires on single-cell level. The relational database allows easy data sharing and downstream analyses as well as immediate comparisons between different data sets.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 101 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 31%
Student > Master 13 13%
Student > Ph. D. Student 12 12%
Professor 8 8%
Student > Bachelor 6 6%
Other 18 18%
Unknown 13 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 26%
Biochemistry, Genetics and Molecular Biology 22 22%
Immunology and Microbiology 19 19%
Computer Science 7 7%
Medicine and Dentistry 5 5%
Other 5 5%
Unknown 17 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 February 2016.
All research outputs
#14,427,926
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#4,561
of 7,400 outputs
Outputs of similar age
#205,682
of 400,208 outputs
Outputs of similar age from BMC Bioinformatics
#86
of 133 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 35th percentile – i.e., 35% 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 400,208 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
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 is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.