Sentence examples for mutation data generated from inspiring English sources

With an increasing amount of both protein structural data in the PDB database and somatic mutation data generated by next-generation sequencing (NGS) experiments, the integration of protein structural information and large-scale somatic mutations offers an alternative, promising approach to uncovering functionally important somatic mutations in cancer.

After applying this method to cancer gene mutation data generated from large-scale and whole genome sequencing of cancer samples, a network of cancer genes with co-occurring and anti-co-occurring mutations was constructed.

In our tests, mutation data generated poor predictions.

Since high IL6 expression seems to be particularly correlated with VN1203 pathogenesis our model, IL6 could be useful for predicting pathogenicity of viral strains and mutations using data generated in cell culture, though direct comparison of these results with responses to non-pathogenic virus infection will be required to accomplish this.

A formal semiparametric statistical inference framework is proposed for the evaluation of the age-dependent penetrance of a rare genetic mutation, using family data generated under a case-family design, where phenotype and genotype information are collected from first-degree relatives of case probands carrying the targeted mutation.

To obtain insight into the mutation load in specific genes ensuing differentiation and proliferation in SCs, we mapped our mutations to expression data generated by the FANTOM5 project28.

It is still too early to know whether such examples are representative, but based on the structural variation data it seems reasonable to investigate genetic mutations based on data generated across a range of neurodevelopmental phenotypes.

Lung tumor mutation data were generated using mass spectrometry-based genotyping (OncoMap) [ 30].

We have updated SAAPdb and improved its analyses, but with the increasing rate with which mutation data are generated, we have created a new analysis pipeline and web interface.

We benchmarked Mutascope against other mutation callers using sequencing data generated from a mixture of 8 normal DNA samples with known genotypes (MIX sample) resulting in 'somatic mutations' at variable allelic fraction.

Therefore, we evaluated a model in which each of them has a separate mutation rate using the simulated data generated described in Section 3.1.