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Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler

Overview of attention for article published in BMC Genomics, September 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

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1 policy source
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11 X users

Citations

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149 Dimensions

Readers on

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131 Mendeley
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2 CiteULike
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Title
Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler
Published in
BMC Genomics, September 2016
DOI 10.1186/s12864-016-3030-6
Pubmed ID
Authors

Samuel S. Shepard, Sarah Meno, Justin Bahl, Malania M. Wilson, John Barnes, Elizabeth Neuhaus

Abstract

Deep sequencing makes it possible to observe low-frequency viral variants and sub-populations with greater accuracy and sensitivity than ever before. Existing platforms can be used to multiplex a large number of samples; however, analysis of the resulting data is complex and involves separating barcoded samples and various read manipulation processes ending in final assembly. Many assembly tools were designed with larger genomes and higher fidelity polymerases in mind and do not perform well with reads derived from highly variable viral genomes. Reference-based assemblers may leave gaps in viral assemblies while de novo assemblers may struggle to assemble unique genomes. The IRMA (iterative refinement meta-assembler) pipeline solves the problem of viral variation by the iterative optimization of read gathering and assembly. As with all reference-based assembly, reads are included in assembly when they match consensus template sets; however, IRMA provides for on-the-fly reference editing, correction, and optional elongation without the need for additional reference selection. This increases both read depth and breadth. IRMA also focuses on quality control, error correction, indel reporting, variant calling and variant phasing. In fact, IRMA's ability to detect and phase minor variants is one of its most distinguishing features. We have built modules for influenza and ebolavirus. We demonstrate usage and provide calibration data from mixture experiments. Methods for variant calling, phasing, and error estimation/correction have been redesigned to meet the needs of viral genomic sequencing. IRMA provides a robust next-generation sequencing assembly solution that is adapted to the needs and characteristics of viral genomes. The software solves issues related to the genetic diversity of viruses while providing customized variant calling, phasing, and quality control. IRMA is freely available for non-commercial use on Linux and Mac OS X and has been parallelized for high-throughput computing.

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X Demographics

The data shown below were collected from the profiles of 11 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 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Belgium 1 <1%
Switzerland 1 <1%
Unknown 128 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 26%
Student > Ph. D. Student 23 18%
Student > Master 16 12%
Student > Bachelor 11 8%
Student > Doctoral Student 9 7%
Other 20 15%
Unknown 18 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 43 33%
Agricultural and Biological Sciences 28 21%
Immunology and Microbiology 12 9%
Medicine and Dentistry 8 6%
Engineering 4 3%
Other 13 10%
Unknown 23 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 29 January 2024.
All research outputs
#4,229,156
of 25,295,968 outputs
Outputs from BMC Genomics
#1,581
of 11,212 outputs
Outputs of similar age
#66,581
of 344,613 outputs
Outputs of similar age from BMC Genomics
#40
of 289 outputs
Altmetric has tracked 25,295,968 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,212 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 85% 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 344,613 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 80% of its contemporaries.
We're also able to compare this research output to 289 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.