The GAMETES software, incorporating EDM and COR, is open source and freely available for download, and offers an intuitive and flexible framework for the simulation of complex genetic models, and the option to generate simulated datasets from these models.
Here, we provide the information of adaptivity for each program, then above all, compare the performance of eight distinct tools against eight groups of simulated datasets from Solexa sequencing platform.
Specifically, I simulated datasets from the following three series of six models (Table 3). 1.
We use simulated datasets from population declines with a wide range of bottleneck intensity, sample size and number of loci.
Next, I simulated datasets from three series of models in which the divergence times of the 22 pairs were random (i.e., no clustering; | τ |=22).
Second, we simulated datasets from an extended pedigree with a mutation inherited from a common ancestor and a dataset for a healthy non- related population, and identified the mutation locus from the data.
We systematically evaluated our new tool in simulated datasets from several large-scale TCGA RNA-Seq projects, unbiased benchmark data from compcodeR package, and real RNA-Seq data from the development transcriptome of Drosophila.
To determine what frequency of MCD was deemed significant, we performed a similar analysis of the 10 simulated datasets from before and assessed the proportion of events at each frequency of MCD from 0/9 to 1/9.
To test ustacks, we created simulated datasets from the stickleback reference genome (BROAD S1, Ensembl version 59) by extracting 45,547 reads each 60 bp long in both directions at each SbfI restriction enzyme cut site (CCTGCAvGG).
To study how ignoring conditional dependence will affect the incremental value statistics, we used the simulated datasets from the scenario where T2 had both improved sensitivity and specificity compared to T1 and the two tests are conditionally dependent.
