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mInDel: a high-throughput and efficient pipeline for genome-wide InDel marker development

Overview of attention for article published in BMC Genomics, April 2016
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
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Title
mInDel: a high-throughput and efficient pipeline for genome-wide InDel marker development
Published in
BMC Genomics, April 2016
DOI 10.1186/s12864-016-2614-5
Pubmed ID
Authors

Yuanda Lv, Yuhe Liu, Han Zhao

Abstract

Rich in genetic information and cost-effective to genotype, the Insertion-Deletion (InDel) molecular marker system is an important tool for studies in genetics, genomics and for marker-assisted breeding. Advent of next-generation sequencing (NGS) revolutionized the speed and throughput of sequence data generation, and enabled genome-wide identification of insertion and deletion variation. However, current NGS-based InDel mining tools, such as Samtools, GATK and Atlas2, all rely on a reference genome for variant calling which hinders their application on unsequenced organisms and the output of short InDels compromised their use on gel-based genotyping platforms. To address these issues, an enhanced platform is needed to identify longer InDels and develop markers in absence of a reference genome. Here we present mInDel (multiple InDel), a next-generation variant calling tool specifically designed for InDel marker discovery. By taking in raw sequence reads and assembling them into contigs de novo, this software identifies InDel polymorphisms using a sliding window alignment from assembled contigs, rendering a unique advantage when a reference genome is unavailable. By providing an option of combining multiple discovered InDels as output, mInDel is amiable to gel-based genotyping platforms where markers with large polymorphisms are preferred. We demonstrated the usability and performance of this software through a case study using a set of maize NGS data, and experimentally validated the accuracy of markers generated from mInDel. mInDel is a novel and practical tool that enables rapid genome-wide InDel marker discovery. The features of being independent from a reference genome and the flexibility with downstream genotyping platforms will allow a broad range of applications across genetics research and plant breeding. The mInDel pipeline is freely available at www.github.com/lyd0527/mInDel .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
France 1 4%
Unknown 25 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 37%
Student > Doctoral Student 2 7%
Student > Bachelor 2 7%
Student > Postgraduate 2 7%
Professor 2 7%
Other 4 15%
Unknown 5 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 44%
Biochemistry, Genetics and Molecular Biology 5 19%
Engineering 3 11%
Computer Science 1 4%
Unknown 6 22%
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 16 April 2016.
All research outputs
#13,662,605
of 23,577,761 outputs
Outputs from BMC Genomics
#4,896
of 10,800 outputs
Outputs of similar age
#145,025
of 302,349 outputs
Outputs of similar age from BMC Genomics
#113
of 247 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,800 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 52% 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 302,349 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 51% of its contemporaries.
We're also able to compare this research output to 247 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.