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VDJPipe: a pipelined tool for pre-processing immune repertoire sequencing data

Overview of attention for article published in BMC Bioinformatics, October 2017
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Title
VDJPipe: a pipelined tool for pre-processing immune repertoire sequencing data
Published in
BMC Bioinformatics, October 2017
DOI 10.1186/s12859-017-1853-z
Pubmed ID
Authors

Scott Christley, Mikhail K. Levin, Inimary T. Toby, John M. Fonner, Nancy L. Monson, William H. Rounds, Florian Rubelt, Walter Scarborough, Richard H. Scheuermann, Lindsay G. Cowell

Abstract

Pre-processing of high-throughput sequencing data for immune repertoire profiling is essential to insure high quality input for downstream analysis. VDJPipe is a flexible, high-performance tool that can perform multiple pre-processing tasks with just a single pass over the data files. Processing tasks provided by VDJPipe include base composition statistics calculation, read quality statistics calculation, quality filtering, homopolymer filtering, length and nucleotide filtering, paired-read merging, barcode demultiplexing, 5' and 3' PCR primer matching, and duplicate reads collapsing. VDJPipe utilizes a pipeline approach whereby multiple processing steps are performed in a sequential workflow, with the output of each step passed as input to the next step automatically. The workflow is flexible enough to handle the complex barcoding schemes used in many immunosequencing experiments. Because VDJPipe is designed for computational efficiency, we evaluated this by comparing execution times with those of pRESTO, a widely-used pre-processing tool for immune repertoire sequencing data. We found that VDJPipe requires <10% of the run time required by pRESTO. VDJPipe is a high-performance tool that is optimized for pre-processing large immune repertoire sequencing data sets.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 18%
Professor 5 15%
Professor > Associate Professor 4 12%
Student > Bachelor 4 12%
Other 3 9%
Other 8 24%
Unknown 4 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 24%
Immunology and Microbiology 6 18%
Medicine and Dentistry 4 12%
Agricultural and Biological Sciences 2 6%
Computer Science 2 6%
Other 4 12%
Unknown 8 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 20 October 2017.
All research outputs
#14,083,124
of 23,005,189 outputs
Outputs from BMC Bioinformatics
#4,496
of 7,312 outputs
Outputs of similar age
#173,355
of 324,711 outputs
Outputs of similar age from BMC Bioinformatics
#57
of 117 outputs
Altmetric has tracked 23,005,189 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 7,312 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 324,711 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 117 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.