Fluorescence in situ hybridization (FISH) is a powerful single-cell technique that harnesses nucleic acid base pairing to detect the abundance and positioning of cellular RNA and DNA molecules in fixed samples. Recent technology development has opened the doors to the construction of FISH probes entirely from synthetic oligonucleotides (oligos), allowing for the fine optimization of thermodynamic properties together with the opportunity to design probes against any sequenced genome.
Despite these advances, the computational tools to facilitate the oligos design remain limited, particularly in terms of their accessibility to a broad range of users. OligoMiner is an open-source and modular pipeline written in Python that introduces a novel method of assessing probe specificity that employs supervised machine learning to predict binding specificity from genome-scale sequence alignment information. However, its use is restricted to only those people who are confident with a command line interfaces, as it lacks a Graphical User Interface (GUI), thus potentially cutting out many researchers from this technology.
Here you can use OligoMinerApp, a web-based application that aims to extend the OligoMiner framework through the implementation of a smart and easy-to-use GUI and the introduction of new functionalities specially designed to make effective probe mining available to everyone, even on mobile devices.