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IDS is as sensitive as the Hamilton scale and CGI in detecting between group differences.
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Our method is more efficient in detecting HGT between distantly related bacteria than between closely related bacteria.
There is great interest in detecting associations between human traits and rare genetic variation.
This model is more powerful than the sample-based model in detecting departures between observed and null partitions because of intra-specific aggregation of individuals.
One limitation of short read sequencing is difficulty in detecting indels between ∼15 and 50 bp.
The extrapolation method was also very accurate in detecting changes between two consecutive CTs.
Further, there is greater power in detecting LGT between distantly related species (i.e., interdomain events).
In addition, MANOVA was most powerful in detecting differences between genotypes for simulations based on signals obtained from real data.
Thus, we were interested in detecting differences, between the cytoskeletal volume of control and SMAR1-overexpressing cells.
Compared to structural tests, visual function tests may be more relevant in detecting differences between PD patients and controls.
This method has already proved to be very efficient in detecting dietary differences between species.
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