Sentence examples for mutation datasets from inspiring English sources

Our analysis of large mutation datasets shows that B-SIFT is easily scalable in the way that SIFT is, and the distribution of B-SIFT scores can be used to discover high-level characteristics of the dataset.

Here, our algorithm outperforms SIFT, PolyPhen-2 and Mutation Assessor across all mutation datasets.

HyperModules is thus applicable to a range of networks and mutation datasets.

Also Radivojac et al. (2008) found that somatic cancer mutation datasets have a significant enrichment for mutations disrupting phosphorylation sites.

This model is supported by neighbor joining trees constructed from multiple mutation datasets (SNVs, indels, CNAS, and SVs).

Several studies also have shown that the selection pressures vary by mutation type and sequence location in cancer mutation datasets.

This provided a uniquely high-resolution mutation dataset for mapping LD.

We find that the use of a B-SIFT cutoff allows for enriching a mutation dataset for activating mutations, but there continues to be a high rate of false negatives and false positives (Fig. 2B).

In addition, the rows of the sample mutation dataset to be analyzed must be appended to the modified reference dataset.

For the sample mutation dataset (TCGA UCEC), the same steps used for the modified COSMIC reference dataset must be performed.

By contrast, the mutation dataset used in our independent benchmark was collected from the SwissVar [Mottaz et al., 2010] portal.