A fully automated deep learning pipeline for high-throughput colony segmentation and classification.

Autor:	Carl SH; Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland marc.buehler@fmi.ch sarahhcarl@gmail.com.; SIB Swiss Institute of Bioinformatics Quartier Sorge - Batiment Amphipole 1015, Lausanne, Switzerland., Duempelmann L; Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland.; University of Basel, Petersplatz 10, 4003 Basel, Switzerland., Shimada Y; Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland., Bühler M; Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland marc.buehler@fmi.ch sarahhcarl@gmail.com.; University of Basel, Petersplatz 10, 4003 Basel, Switzerland.
Jazyk:	angličtina
Zdroj:	Biology open [Biol Open] 2020 Jun 23; Vol. 9 (6). Date of Electronic Publication: 2020 Jun 23.
DOI:	10.1242/bio.052936
Abstrakt:	Adenine auxotrophy is a commonly used non-selective genetic marker in yeast research. It allows investigators to easily visualize and quantify various genetic and epigenetic events by simply reading out colony color. However, manual counting of large numbers of colonies is extremely time-consuming, difficult to reproduce and possibly inaccurate. Using cutting-edge neural networks, we have developed a fully automated pipeline for colony segmentation and classification, which speeds up white/red colony quantification 100-fold over manual counting by an experienced researcher. Our approach uses readily available training data and can be smoothly integrated into existing protocols, vastly speeding up screening assays and increasing the statistical power of experiments that employ adenine auxotrophy. Competing Interests: Competing interestsThe Friedrich Miescher Institute for Biomedical Research (FMI) receives significant financial contributions from the Novartis Research Foundation. Published research reagents from the FMI are shared with the academic community under a Material Transfer Agreement (MTA) having terms and conditions corresponding to those of the UBMTA (Uniform Biological Material Transfer Agreement). (© 2020. Published by The Company of Biologists Ltd.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu