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High-quality genetic mapping with ddRADseq in the non-model tree Quercus rubra

Overview of attention for article published in BMC Genomics, May 2017
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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Title
High-quality genetic mapping with ddRADseq in the non-model tree Quercus rubra
Published in
BMC Genomics, May 2017
DOI 10.1186/s12864-017-3765-8
Pubmed ID
Authors

Arpita Konar, Olivia Choudhury, Rebecca Bullis, Lauren Fiedler, Jacqueline M. Kruser, Melissa T. Stephens, Oliver Gailing, Scott Schlarbaum, Mark V. Coggeshall, Margaret E. Staton, John E. Carlson, Scott Emrich, Jeanne Romero-Severson

Abstract

Restriction site associated DNA sequencing (RADseq) has the potential to be a broadly applicable, low-cost approach for high-quality genetic linkage mapping in forest trees lacking a reference genome. The statistical inference of linear order must be as accurate as possible for the correct ordering of sequence scaffolds and contigs to chromosomal locations. Accurate maps also facilitate the discovery of chromosome segments containing allelic variants conferring resistance to the biotic and abiotic stresses that threaten forest trees worldwide. We used ddRADseq for genetic mapping in the tree Quercus rubra, with an approach optimized to produce a high-quality map. Our study design also enabled us to model the results we would have obtained with less depth of coverage. Our sequencing design produced a high sequencing depth in the parents (248×) and a moderate sequencing depth (15×) in the progeny. The digital normalization method of generating a de novo reference and the SAMtools SNP variant caller yielded the most SNP calls (78,725). The major drivers of map inflation were multiple SNPs located within the same sequence (77% of SNPs called). The highest quality map was generated with a low level of missing data (5%) and a genome-wide threshold of 0.025 for deviation from Mendelian expectation. The final map included 849 SNP markers (1.8% of the 78,725 SNPs called). Downsampling the individual FASTQ files to model lower depth of coverage revealed that sequencing the progeny using 96 samples per lane would have yielded too few SNP markers to generate a map, even if we had sequenced the parents at depth 248×. The ddRADseq technology produced enough high-quality SNP markers to make a moderately dense, high-quality map. The success of this project was due to high depth of coverage of the parents, moderate depth of coverage of the progeny, a good framework map, an optimized bioinformatics pipeline, and rigorous premapping filters. The ddRADseq approach is useful for the construction of high-quality genetic maps in organisms lacking a reference genome if the parents and progeny are sequenced at sufficient depth. Technical improvements in reduced representation sequencing (RRS) approaches are needed to reduce the amount of missing data.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 22%
Student > Master 11 17%
Researcher 7 11%
Student > Doctoral Student 6 10%
Student > Bachelor 6 10%
Other 10 16%
Unknown 9 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 49%
Biochemistry, Genetics and Molecular Biology 12 19%
Environmental Science 3 5%
Unspecified 2 3%
Earth and Planetary Sciences 2 3%
Other 2 3%
Unknown 11 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 26 June 2019.
All research outputs
#6,797,766
of 22,977,819 outputs
Outputs from BMC Genomics
#3,058
of 10,686 outputs
Outputs of similar age
#107,525
of 316,100 outputs
Outputs of similar age from BMC Genomics
#73
of 217 outputs
Altmetric has tracked 22,977,819 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 10,686 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 71% 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 316,100 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 65% of its contemporaries.
We're also able to compare this research output to 217 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.