Abstract
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 paved the way to the construction of FISH probes entirely from synthetic oligonucleotides (oligos), allowing the optimization of thermodynamic properties together with the opportunity to design probes against any sequenced genome. However, comparatively little progress has been made in the development of computational tools to facilitate the oligos design, and even less has been done to extend their accessibility. 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 probe binding specificity from genome-scale sequence alignment information. However, its use is restricted to only those people who are confident with command line interfaces because it lacks a Graphical User Interface (GUI), potentially cutting out many researchers from this technology. Here, we present OligoMinerApp (http://oligominerapp.org), 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.
Highlights
Recent advances in molecular technologies have unleashed the power to explore biological processes at single-cell resolution with unprecedented accuracy [1]
OligoMiner is written in Python using Biopython and is assembled in five independent Python scripts, each one performing a functional step in the probe discovery workflow [21]
SmFISH technologies, require a carefully designed and functional set of probes in order to gain readable pictures up to 200 nm resolution. This level of specialization is achieved by the employment of bioinformatics tools that combine various approaches to probes mining, but rarely implement a useful interface for nonbioinformatics researchers
Summary
Recent advances in molecular technologies have unleashed the power to explore biological processes at single-cell resolution with unprecedented accuracy [1]. The improved efficiency of ultra-high-throughput single-cell sequencing system [2], coupled with new bioinformatics approaches for data processing [3], has abruptly led to a massive increase in genetic profiling of cells. Notwithstanding, ultra-high-throughput single-cell analysis requires cell dissociation, which results in the loss of spatial information and represents a major shortcoming of single-cell methods. The combination of gene expression profiles with spatial coordinates of cells, could be achieved using computational methods or relying on direct quantification of mRNA molecules at single cell resolution [6] through fluorescence in situ hybridization (FISH). FISH methods have become the golden standard for in situ analysis and they have been greatly improved to detect hundreds of genes in thousands of single-cell at a single-molecule resolution (smFISH) [7,8]. The imaging-based single-cell technologies allow the dissection of functional state and grouping of individual cells providing the exact copy number of the molecules of interest in the context of their subcellular localization [9]
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