We implemented an efficient algorithm for high-throughput detection and interpretation of mRNA splicing mutations based on information theory-based position weight matrices of a genome-wide set of curated splice donor and acceptor sites [20].
Next, we were interested what the improvements were for different types of mutations based on the secondary structure and accessible surface area of the mutated residue.
Using automated DNA sequencing, we tested individuals for BRCA mutations, based on positive family history of either breast or ovarian cancer or both.
We developed a LightCycler real-time polymerase chain reaction assay to detect these two mutations based on fluorescence resonance energy transfer technology and melting curve analysis.
In this case, we compare the proposed mutation against classical Evolutionary Programming approaches, with mutations based on Gaussian, Cauchy and chaotic maps.
Genotype-phenotype correlations have been suggested in patients, resulting in classification of FOP mutations based on location within different receptor domains and structural modeling.
Consensus design, the selection of mutations based on the most common amino acid in each position of a multiple sequence alignment, has proven to be an efficient way to engineer stabilized mutants and even to design entire proteins.
We have chosen to define true mutations based on a conservative interpretation of the data.
Here we present a modified version of the SIFT (Sorting Intolerant from Tolerant) algorithm that utilizes protein sequence alignments with homologous sequences to identify functional mutations based on evolutionary fitness.
The MORF library represents 93.2% of the verified S. cerevisiae ORFs and most of these have either been sequenced completely or are expected to lack mutations based on the error rates of the polymerases used to amplify the yeast ORFs.
Mutations based on single amino acid substitutions in either MsbA, an IM, essential ABC transporter in the LPS export pathway or in YhjD suppress the lethal consequence of an incomplete lipid A precursor IVA [11].
