Serum response factor (SRF) recruits members of two families of signal-regulated coactivators, the extracellular signal-regulated kinase (ERK)-regulated ternary complex factors (TCFs) and the actin-regulated myocardin-related transcription factors (MRTFs), to its target genes through its DNA-binding domain. Whether coactivator association is required for SRF function in vivo and whether particu...

Signature learning from gene expression consists into selecting a subset of molecular markers which best correlate with prognosis. It can be cast as a feature selection problem. Here we use as optimality criterion the separation between survival curves of clusters induced by the selected features. We address some important problems in this fields such as developing an unbiased search procedure ...

We describe here a new approach to marker recycling, a controlled sequence of steps in which a genetic marker is selected and then lost. Marker recycling is important for genetic manipulation, because it allows a single selection marker to be used repeatedly. Our approach relies upon the ability of the CRISPR-Cas9 system to make a targeted double-strand break in DNA and the expectation that a d...

Positive selection can be inferred from its effect on linked neutral variation. In the restrictive case when there is no recombination, all linked variation is removed. If recombination is present but rare, both deterministic and stochastic models of positive selection show that linked variation hitchhikes to either low or high frequencies. While the frequency distribution of variation can be i...

many agriculturally important traits are complex, affected by many genes and the environment. quantitative trait loci (qtl) mapping is a key tool for studying the genetic structure of complex traits in plants. in the present study qtls associated with yield and agronomical traits such as leaf number, leaf length, leaf width, plant height, stem and head diameter were identified by using 70 recom...

Molecular genetics can be integrated with traditional methods of artificial selection on phenotypes by applying marker-assisted selection (MAS). We derive selection indices that maximize the rate of improvement in quantitative characters under different schemes of MAS combining information on molecular genetic polymorphisms (marker loci) with data on phenotypic variation among individuals (and ...

The power of maximum likelihood tests of positive selection on protein-coding genes depends heavily on detecting and accounting for potential biases in the studied data set. Although the influence of transition:transversion and codon biases have been investigated in detail, little is known about how inaccuracy in the phylogeny used during the calculations affects the performance of these tests....

Engineered gene switches and circuits that can sense various biochemical and physical signals, perform computation, and produce predictable outputs are expected to greatly advance our ability to program complex cellular behaviors. However, rational design of gene switches and circuits that function in living cells is challenging due to the complex intracellular milieu. Consequently, most succes...

With the availability of high-density marker maps and cost-effective genotyping, genomic selection methods may provide faster genetic gain than can be achieved by current selection methods based on phenotypes and the pedigree. Here we investigate some of the factors driving the accuracy of genomic selection, namely marker density and marker type (i.e., microsatellite and SNP markers), and the u...

Genomic selection is an upgrading form of marker-assisted selection for quantitative traits, and it differs from the traditional marker-assisted selection in that markers in the entire genome are used to predict genetic values and the QTL detection step is skipped. Genomic selection holds the promise to be more efficient than the traditional marker-assisted selection for traits controlled by po...