Pavan, K. | bioRxiv Science

bioRxiv2026

Design and Assembly of Combinatorial DNA Barcodes for Probe-based Genomics Applications

Probe-based genomics technologies are extending molecular analysis into intact tissues and fixed cells, yet strategies to decode complex experimental conditions encoded in cellular RNA remain limited. Here we present a modular framework that integrates custom software tools with purpose-built cloning reagents to design, assemble, validate, and deploy combinatorial DNA barcodes. Combinatorial barcodes comprise spatially adjacent collections of known sequences, enabling millions of unique molecules to be efficiently distinguished using a limited set of probes. Our software tools integrate with optimized assembly plasmids and whole plasmid long-read sequencing for high-fidelity construction and structural validation of diverse combinatorial barcode architectures. Assembled barcode libraries are flexibly transferred into user-modified expression vectors to support diverse downstream experimental applications. We showcase the versatility of this framework by assembling two structurally distinct combinatorial barcode libraries, each containing millions of unique sequences. Following rabies virus-based delivery to the mouse brain, we validate in vivo decoding of a combinatorial barcode architecture capable of distinguishing ~16.3 million expressed RNAs through probe-based in situ sequencing. Our framework for flexible and accurate combinatorial barcode construction fills a technically demanding niche delivering cost-effective molecular reagents for multiplexed experimentation on current and evolving probe-based genomics platforms.

genomics↗

bioRxiv2024

AnnSQL: A Python SQL-based package for large-scale single-cell genomics analysis on a laptop

As single-cell genomics technologies continue to accelerate biological discovery, software tools that use elegant syntax and minimal computational resources to analyze atlas-scale datasets are increasingly needed. Here we introduce AnnSQL, a Python package that constructs an AnnData-inspired database using the in-process DuckDb engine, enabling orders-of-magnitude performance enhancements for parsing single-cell genomics datasets with the ease of SQL. We highlight AnnSQL functionality and demonstrate transformative runtime improvements by comparing AnnData or AnnSQL operations on a 4.4 million cell single-nucleus RNA-seq dataset: AnnSQL-based operations were executed in minutes on a laptop for which equivalent AnnData operations largely failed (or were [~]700x slower) on a high-performance computing cluster. AnnSQL lowers computational barriers for large-scale single-cell/nucleus RNA-seq analysis on a personal computer, while demonstrating a promising computational infrastructure extendable for complete single-cell workflows across various genome-wide measurements. Availability and ImplementationAnnSQL is a pip installable package that can be found at https://github.com/ArpiarSaundersLab/annsql along with documentation at https://docs.annsql.com.

bioinformatics↗