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Elisa Ficarra

Publications and source records attributed to Elisa Ficarra.

2 recordsLinked to original sources

Automatic intrinsic DNA curvature computation from AFM images.

Critical information on several biological processes such as DNA-protein interactions and DNA transcription can be derived from analysis of DNA curvature. Under thermal perturbation, the curvature is composed of static and dynamic contributions, thus, can be described as the sum of intrinsic curvature and a fluctuation contribution. Without considering thermal agitations, the DNA curvature is reducible to the intrinsic component, which is a function of the DNA nucleotide sequence only. In this paper, we present an automated algorithm to determine the DNA intrinsic curvature profiles and the molecular spatial orientations in Atomic Force Microscope images. The algorithm allows to reconstruct the intrinsic curvature profile by filtering the thermal contribution. It detects fragment orientation on atomic force microscope images without labels with a percentage of correct molecular-orientation detection of 96.79% in computer-generated benchmarks, for molecules with a high curvature peak. The automated algorithm reconstructs the intrinsic curvature profile of DNA molecules with a mean square error of 3.8122 x 10(-4) rads over a profile with a central peak value of 0.196 rads, and 6.1 x 10(-3) rads over a curvature profile with two symmetric peaks of about 0.08 rads. Moreover, it correctly detects the location of the peaks in the molecules with a deviation of about 1% of molecule length.

Algorithms↗

Automated DNA fragments recognition and sizing through AFM image processing.

This paper presents an automated algorithm to determine DNA fragment size from atomic force microscope images and to extract the molecular profiles. The sizing of DNA fragments is a widely used procedure for investigating the physical properties of individual or protein-bound DNA molecules. Several atomic force microscope (AFM) real and computer-generated images were tested for different pixel and fragment sizes and for different background noises. The automated approach minimizes processing time with respect to manual and semi-automated DNA sizing. Moreover, the DNA molecule profile recognition can be used to perform further structural analysis. For computer-generated images, the root mean square error incurred by the automated algorithm in the length estimation is 0.6% for a 7.8 nm image pixel size and 0.34% for a 3.9 nm image pixel size. For AFM real images we obtain a distribution of lengths with a standard deviation of 2.3% of mean and a measured average length very close to the real one, with an error around 0.33%.

Algorithms↗