10 research outputs found
Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network
Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism
X ray photoemission electron microscopy studies of local magnetization in Py antidot array thin films
Permalloy antidot thin films were grown by sputtering onto anodic alumina templates, replicating their hexagonal order inside micrometric geometric domains. The advanced high spatial and sensitive x ray photoemission electron microscopy technique under an applied magnetic field has enabled magnetic domain structure imaging and quantitative hysteresis loop analysis inside nanoscale regions with geometric order and at border regions. The study has been complemented by vibrating sample magnetometry and magneto optic Kerr effect measurements. The magnetization process is clearly determined by the geometry characteristics of the antidot arrays. Inside geometric ordered domains, the strength of effective in plane magnetic anisotropy depends on the antidot diameter to film thickness ratio, which determines the partial balance between stray fields generated by magnetic charges at the lateral surface of the antidots and those at the upper bottom film surface. In addition, the border regions between geometric domains act as pinning centers for magnetization reversal and eventually generate a harder magnetic regio
Fabrication and Magnetic Characterization of Cobalt Antidot Arrays Effect of the Surrounding Continuous Film
We have performed an experimental study on the influence of a ferromagnetic continuous film in the magnetization reversal processes in discrete submicrometric antidot arrays fabricated on it. In order to compare the magnetic properties, two sets of antidot arrays have been fabricated over a cobalt thin film: embedded in the continuous film, and isolated by a trench surrounding the array. X-ray photoemission electron microscopy images of the virgin state show the same magnetic domain distribution in both sets of samples, finding no evidence of any effect of the surrounding film. This result is supported by the hysteresis loops measured with magneto-optical Kerr effect, as isolated
and non-isolated arrays present almost coincident loops. A huge increase of the coercivity of the film is achieved, and the expected dependence on the geometrical parameters of the array is found,
connecting the previous studies on the micro- and nanometric scales
The effect of lattice constant on the storage capacity of hydrogen hydrates: a Monte Carlo study
Tests of Pore-Size Distributions Deduced from Inversion of Simulated and Real Adsorption Data
Exogenous calcium alleviates the impact of cadmium-induced oxidative stress in Lens culinaris medic. Seedlings through modulation of antioxidant enzyme activities
Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network
10.1038/s41467-021-23143-7Nature Communications121329