Sentence examples for markers is typically from inspiring English sources
Clustering of markers is typically symptomatic of saturation of markers on the linkage map [ 89], yet marker saturation in this study is unlikely, for several reasons.
One of them is that the number of independent variables (markers) is typically much more than the number of available samples, often referred as curse of dimensionality.
Most of the proposed models try to reduce the effective dimensionality of the marker data, since the number of markers is typically much larger than the number of phenotyped animals in the reference population.
In this type of EM, labeling by electron-dense markers is typically achieved using the immunolabeling methods and it is a trade-off between obtaining good structural preservation and good labeling.
Within the past decade, microsatellites have often been the marker of choice for many of these types of studies; however, in most cases, development of these markers is typically time-consuming and expensive (e.g., Squirrell et al., 2003).
Additionally, those most informative markers are typically sparse in the whole genome since they usually take only a very small percentage (less than 1%) of the total number of markers.
SNP and RAPD markers are typically bi-allelic, whereas SSR markers are multi-allelic, which has the potential to increase gene diversity (c.f. [ 12]).
Markers showing "Type c" profile with a 3 1 segregation ratio (Table 2) were not employed as the recombination frequencies obtained with such markers are typically inaccurate [ 19].
For instance, allelic odds ratios at markers are typically estimated to be <1.5 and risk alleles can be the minor or major frequency allele.
These markers are typically highly polymorphic and are often transferable to closely related species; for example, 45 91% of 127 Vigna radiata (L).
Such markers are typically identified either by global multiple sequence alignment algorithms, or simply using reciprocal best matches using local alignment tools like BLAST.
