Biology subjects

Dibrov, A.

Publications and source records attributed to Dibrov, A..

3 recordsLinked to original sources

bioRxiv2022

Estimating chromosome sizes from karyotype images enables validation of de novo assemblies.

Highly contiguous genome assemblies are essential for genomic research. Chromosome-scale assembly is feasible with the modern sequencing techniques in principle, but in practice, scaffolding errors frequently occur, leading to incorrect number and sizes of chromosomes. Relating the observed chromosome sizes from karyotype images to the generated assembly scaffolds offers a method for detecting these errors. Here, we present KICS, a semi-automated approach for estimating relative chromosome sizes from karyotype images and their subsequent comparison to the corresponding assembly scaffolds. The method relies on threshold-based image segmentation and uses the computed areas of the chromosome-related connected components as a proxy for the actual chromosome size. We demonstrate the validity and practicality of our approach by applying it to karyotype images of humans and various amphibians, birds, fish, insects, mammals, and plants. We found a strong linear relationship between pixel counts and the DNA content of chromosomes. Averaging estimates from eight human karyotype images, KICS predicts most of the chromosome sizes within an error margin of just 6 Mb. Our method provides additional means of validating genome assemblies at low costs. An interactive implementation of KICS is available at https://github.com/mpicbg-csbd/napari-kics.

bioinformatics↗

bioRxiv2019

CLIJ: Enabling GPU-accelerated image processing in Fiji

Graphics processing units (GPU) allow image processing at unprecedented speed. We present CLIJ, a Fiji plugin enabling end-users with entry level experience in programming to benefit from GPU-accelerated image processing. Freely programmable workflows can speed up image processing in Fiji by factor 10 and more using high-end GPU hardware and on affordable mobile computers with built-in GPUs.

bioinformatics↗

bioRxiv2017

Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy

Fluorescence microscopy is a key driver of discoveries in the life-sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how image restoration based on deep learning extends the range of biological phenomena observable by microscopy. On seven concrete examples we demonstrate how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to 10-fold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times higher frame-rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FO_SCPLOWIJIC_SCPLOW, and KO_SCPLOWNIMEC_SCPLOW.

bioinformatics↗