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.

Fluorescence in situ hybridization (FISH) allows researchers to visualize the spatial position and quantity of nucleic acids in fixed samples. Recently, considerable progress has been made in developing oligonucleotide (oligo)-based FISH methods that have enabled researchers to study the three-dimensional organization of the genome at super-resolution and visualize the spatial patterns of gene expression for thousands of genes in individual cells. However, there are few existing computational tools to support the bioinformatics workflows necessary to carry out these experiments using oligo FISH probes. Here, we introduce paint server and homology optimization pipeline (PaintSHOP), an interactive platform for the design of oligo FISH experiments. PaintSHOP enables researchers to identify probes for their experimental targets efficiently, to incorporate additional necessary sequences such as primer pairs and to easily generate files documenting library design. PaintSHOP democratizes and standardizes the process of designing complex probe sets for the oligo FISH community.

Genome browsers are an essential tool in genome analysis. Modern genome browsers enable complex and interactive visualization of a wide variety of genomic data modalities. While such browsers are very powerful, they can be challenging to configure and program for bioinformaticians lacking expertise in web development.

There is a need for methods that can image chromosomes with genome-wide coverage, as well as greater genomic and optical resolution. We introduce OligoFISSEQ, a suite of three methods that leverage fluorescence in situ sequencing (FISSEQ) of barcoded Oligopaint probes to enable the rapid visualization of many targeted genomic regions. Applying OligoFISSEQ to human diploid fibroblast cells, we show how four rounds of sequencing are sufficient to produce 3D maps of 36 genomic targets across six chromosomes in hundreds to thousands of cells, implying a potential to image thousands of targets in only five to eight rounds of sequencing. We also use OligoFISSEQ to trace chromosomes at finer resolution, following the path of the X chromosome through 46 regions, with separate studies showing compatibility of OligoFISSEQ with immunocytochemistry. Finally, we combined OligoFISSEQ with OligoSTORM, laying the foundation for accelerated single-molecule super-resolution imaging of large swaths of, if not entire, human genomes.

JBrowse Jupyter is a package that aims to close the gap between Python programming and genomic visualization. Web-based genome browsers are routinely used for publishing and inspecting genome annotations. Historically they have been deployed at the end of bioinformatics pipelines, typically decoupled from the analysis itself. However, emerging technologies such as Jupyter notebooks enable a more rapid iterative cycle of development, analysis and visualization.