OligoMinerApp

OligoMinerApp designs genome-scale oligonucleotide probes for in situ hybridization (FISH) to enable specific detection and localization of RNA and DNA in fixed samples, including single-cell analyses.

Key Features:

• Genome-scale probe design: Generates oligonucleotide probe sets at genome scale for in situ hybridization (FISH).
• Targeting RNA and DNA: Supports probe design for detection of both RNA and DNA molecules in fixed samples.
• Supervised machine learning specificity assessment: Employs supervised machine learning to assess and predict probe binding specificity.
• Use of genome-scale sequence alignment information: Predicts probe specificity by analyzing genome-scale sequence alignment data.
• Modular Python pipeline: Implements a modular pipeline written in Python and builds upon the open-source OligoMiner environment.

Scientific Applications:

• Single-Cell Analysis: Enables detection and localization of RNA and DNA within individual cells to study cellular heterogeneity.
• Gene Expression Studies: Facilitates visualization of gene expression patterns at cellular resolution via FISH probe sets.
• Genomic Research: Supports accurate mapping and quantification of nucleic acids across genomes for diverse organisms.

Methodology:

Applies supervised machine learning to predict probe binding specificity from genome-scale sequence alignment information within a modular Python pipeline built on the OligoMiner environment.