A Reinforcement Learning Framework for Pooled Oligonucleotide Design

The goal of oligonucleotide (oligo) design is to select oligos that optimize a set of design criteria. Oligo design problems are combinatorial in nature and require computationally intensive models to evaluate design criteria. Even relatively small problems can be intractable for brute-force approaches that test every possible combination of oligos, so heuristic approaches must be used to find near-optimal solutions. We present a general reinforcement learning framework, called OligoRL, to solve oligo design problems with complex constraints. OligoRL allows "black-box" design criteria and can be adapted to solve many oligo design problems. We highlight the flexibility of OligoRL by building tools to solve three distinct design problems: 1.) finding pools of random DNA barcodes that lack restriction enzyme recognition sequences (CutFreeRL); 2.) compressing large, non-degenerate oligo pools into smaller degenerate ones (OligoCompressor); and 3.) finding Not-So-Random hexamer primer pools that avoid rRNA and other unwanted transcripts during RNA-seq library preparation (NSR-RL). OligoRL demonstrates how reinforcement learning offers a general solution for complex oligo design problems. OligoRL and its associated software tools are available as a Julia package at http://jensenlab.net/tools.

Ribo-ODDR: Oligo design pipeline for experiment-specific rRNA depletion in ribo-seq

Motivation Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. Results Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a “one-size-fits-all” approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. Availability Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR Supplementary information Supplementary data are available at Bioinformatics online.

Optimized design of antisense oligomers for targeted rRNA depletion

RNA sequencing (RNA-seq) is extensively used to quantify gene expression transcriptome-wide. Although often paired with polyadenylate (poly(A)) selection to enrich for messenger RNA (mRNA), many applications require alternate approaches to counteract the high proportion of ribosomal RNA (rRNA) in total RNA. Recently, digestion using RNaseH and antisense DNA oligomers tiling target rRNAs has emerged as an alternative to commercial rRNA depletion kits. Here, we present a streamlined, more economical RNaseH-mediated rRNA depletion with substantially lower up-front costs, using shorter antisense oligos only sparsely tiled along the target RNA in a 5-min digestion reaction. We introduce a novel Web tool, Oligo-ASST, that simplifies oligo design to target regions with optimal thermodynamic properties, and additionally can generate compact, common oligo pools that simultaneously target divergent RNAs, e.g. across different species. We demonstrate the efficacy of these strategies by generating rRNA-depletion oligos for Xenopus laevis and for zebrafish, which expresses two distinct versions of rRNAs during embryogenesis. The resulting RNA-seq libraries reduce rRNA to <5% of aligned reads, on par with poly(A) selection, and also reveal expression of many non-adenylated RNA species. Oligo-ASST is freely available at https://mtleelab.pitt.edu/oligo to design antisense oligos for any taxon or to target any abundant RNA for depletion.

Mutation Maker, An Open Source Oligo Design Platform for Protein Engineering

Protein engineering is the discipline of developing useful proteins for applications in research, therapeutic and industrial processes by modification of naturally occurring proteins or by invention of de novo proteins. Modern protein engineering relies on the ability to rapidly generate and screen diverse libraries of mutant proteins. However, design of mutant libraries is typically hampered by scale and complexity, necessitating development of advanced automation and optimization tools that can improve efficiency and accuracy. At present, automated library design tools are functionally limited or not freely available. To address these issues, we developed Mutation Maker, an open source mutagenic oligo design software for large-scale protein engineering experiments. Mutation Maker is not only specifically tailored to multi-site random and directed mutagenesis protocols, but also pioneers bespoke mutagenic oligo design for de novo gene synthesis workflows. Enabled by a novel bundle of orchestrated heuristics, optimization, constraint-satisfaction and backtracking algorithms, Mutation Maker offers a versatile toolbox for gene diversification design at industrial scale. Supported by in-silico simulations and compelling experimental validation data, Mutation Maker oligos produce diverse gene libraries at high success rates irrespective of genes or vectors used. Finally, Mutation Maker was created as an extensible platform on the notion that directed evolution techniques will continue to evolve and revolutionize current and future-oriented applications.

Optimized design of antisense oligomers for targeted rRNA depletion

ABSTRACTRNA sequencing (RNA-seq) has become a standard method for quantifying gene expression transcriptome-wide. Although RNA-seq is often paired with polyadenylate (poly(A)) selection to enrich for messenger RNA (mRNA), many applications require alternate approaches to counteract the high proportion of ribosomal RNA (rRNA) in total RNA. Recently, selective rRNA digestion, using RNaseH and antisense DNA oligomers that tile the entire length of target RNAs, has emerged as an alternative to commercial rRNA depletion kits. Here, we present a streamlined, more economical RNaseH-mediated rRNA depletion method with substantially lower up-front costs, using shorter antisense oligos only sparsely tiled along the target RNA, in a digestion reaction of only 5 minutes. We introduce a novel Web tool, Oligo-ASST, that simplifies oligo design to target regions with optimal thermodynamic properties, and additionally can generate compact, common oligo pools that simultaneously target divergent RNAs, e.g. across different species. We demonstrate the efficacy of these strategies by designing oligo sets to deplete rRNA in Xenopus laevis and in zebrafish, which expresses two distinct versions of rRNAs during embryogenesis. The resulting RNA-seq libraries reduce rRNA to <5% of aligned reads, on par with poly(A) selection, and also reveal expression of many non-adenylated RNA species. Oligo-ASST is freely available at https://mtleelab.pitt.edu/oligo to design antisense oligos for any taxon or to target any abundant RNA for depletion.Abstract Figure

Ribo-Pop: Simple, cost-effective, and widely applicable ribosomal RNA depletion

ABSTRACTThe measurement of RNA abundance derived from massively parallel sequencing experiments is an essential technique. Methods that reduce ribosomal RNA levels are usually required prior to sequencing library construction because ribosomal RNA typically comprises the vast majority of a total RNA sample. For some experiments, ribosomal RNA depletion is favored over poly(A) selection because it offers a more inclusive representation of the transcriptome. However, methods to deplete ribosomal RNA are generally proprietary, complex, inefficient, applicable to only specific species, or compatible with only a narrow range of RNA input levels. Here, we describe Ribo-Pop (ribosomal RNA depletion for popular use), a simple workflow and antisense oligo design strategy that we demonstrate works over a wide input range and can be easily adapted to any organism with a sequenced genome. We provide a computational pipeline for probe selection, a streamlined 20-minute protocol, and ready-to-use oligo sequences for several organisms. We anticipate that our simple and generalizable “open source” design strategy would enable virtually any lab to pursue full transcriptome sequencing in their organism of interest with minimal time and resource investment.

Ribo-ODDR: Oligo Design pipeline for experiment-specific Depletion of Ribosomal RNAs in Ribo-seq

Ribosome profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet, several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of ribosomal RNA (rRNA) fragments, which frequently make up more than 90% of sequencing reads if not depleted. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, such kits may perform suboptimally in Ribo-seq. There is therefore potential to significantly increase the information that can be gleaned from Ribo-seq experiments. Here we show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a “one-size-fits-all” approach may result in inefficient rRNA depletion. In order to overcome this, it is possible to use custom-designed biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. We have developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We show that Ribo-ODDR designed oligos lead to a significant increase in rRNA depletion, and increased sequencing depth as a result, providing substantial information that would otherwise have been lost. Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR