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Even though dimensions of the earlier constructed maps have identifiable semantics, those semantics are domain-specific, and there is no visible semantic similarity between our map and various vector representations of semantics of words constructed using LSA, LDA and other approaches.
There were a large number of discrepancies between our map and the build 25 map.
A high synteny between our map and the peach genome was observed as expected.
A high collinearity between our map and the previously published switchgrass maps of crossed populations (Okada et al. 2010) was observed, excepting the following small discrepancies: seven inversions in marker order between our map and the reference maps.
The correlation coefficient between our map and the Paux et al. [ 37] map was high (r = 0.980).
We leveraged emerging Astyanax genomic and transcriptomic resources and Danio rerio genomic and transcriptomic data to locate syntenic regions shared between our map and the Danio genome.
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Excluding heterogeneous plots slightly improved map accuracy and did not lessen the systematic agreement (ACSYS) between our maps and observed plot data.
Unsystematic agreement (ACUNS) between our maps and plots, or random error, improved notably with increasing accuracy assessment extent for all scaling methods, indicating that reliability of most map applications can be improved through coarsening the map grain.
Markers in common between our maps and the Prunus T × E reference map were used to identify corresponding linkage groups.
A possible explanation for the lack of additional matches could be the differences between our maps and studies.
One methodological difference between our maps and these other fish linkage maps is that we used a binned approach, binning SNPs to generate genetic markers, similar to that successfully used in corn (Li et al. 2015; Chen et al. 2014).
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