We have developed a novel dual enzyme chemistry called rhAmp ® SNP genotyping based on RNase H2-dependent PCR (rhPCR) that provides high signal and specificity for SNP analysis. rhAmp SNP genotyping combines a unique two-enzyme system with 3' end blocked DNA-RNA hybrid primers to interrogate SNP loci. Activation of the blocked primers occurs upon hybridization to its perfectly matched target, which eliminates or greatly reduces primer dimers. A thermostable hot-start RNase H2 cleaves the primer immediately 5' of the ribose sugar, releasing the blocking group and allowing primer extension. PCR specificity is further improved with the use of a mutant Taq DNA polymerase, resulting in improved allelic discrimination. Signal generation is obtained using a universal reporter system which requires only two reporter probes for any bi-allelic SNP. 1000 randomly selected SNPs were chosen to validate the 95% design rate of the design pipeline. A subsampling of 130 human SNP targets was tested and achieved a 98% call rate, and 99% call accuracy. rhAmp SNP genotyping assays are compatible with various qPCR instruments including QuantStudio TM 7 Flex, CFX384 TM , IntelliQube ® , and Biomark HD TM . In comparison to TaqMan ® , rhAmp SNP genotyping assays show higher signal (Rn) and greater cluster separation, resulting in more reliable SNP genotyping performance. The rhAmp SNP genotyping solution is suited for high-throughput SNP genotyping applications in humans and plants.

RNA-seq is a powerful tool to detect tissue-specific gene expression, splicing, and genetic variations associated with disease states. However, current RNA-seq approaches have limitations due to poor signal from low-abundant transcripts. Furthermore, tissue-derived RNA samples are often highly degraded, thereby limiting gene detection and suffering from potential sequencing artifacts. Here we show that target capture of sequencing libraries tagged with unique molecular identifiers (UMIs) using the xGen™ Prism DNA library prep kit, optimized for low-input and degraded samples, can overcome these obstacles. Directional and UMI-tagged RNA-seq libraries were constructed with RNA extracted from a FFPE RNA Fusion Reference Standard and captured with different designs of the xGen Pan-Cancer Panel spiked with probes for fusion genes. The first design strategy used IDT's stocked Pan Cancer V1.5 panel targeted to gDNA coordinates. The second design involved extracting all associated RefSeq NM transcripts associated with the gene list. Probes were designed to each transcript and duplicate probes were removed based on exact sequence match. The third strategy leveraged a multi-strain design, which created probes from fasta inputs, but removed probes with 90% or greater homology. Normalized expression was highly correlated (> 85%) between captured and uncaptured samples regardless of rRNA depletion prior to library prep. Captured samples had a greater depth of coverage with over 90% on target bases. In addition, our panel design strategies identified low frequency fusions with deep sequencing regardless of rRNA depletion prior to library prep. The multi-strain design was more effective in reducing redundant capture probes compared the other design strategies. Enhanced coverage and PCR de-duplication with UMIs allowed us to reproducibly measure expression over a wide range of RNA inputs (5-500 ng). We show that target capture of RNA-seq libraries reliably maintains expression information present in uncaptured libraries while increasing coverage for poorly expressed genes and low frequency fusions. In addition, the target captured libraries without rRNA depletion prior to library prep have comparable on-target rate and target coverage with rRNA-depleted, target captured libraries. The addition of UMIs to differentiate between PCR duplicates and unique starting molecules also makes it possible to reliably analyze even highly amplified libraries. (For research use only). Citation Format: Tzu-Chun Chen, Katelyn Larkin, Shale Dames, Hsiao-Yun Huang, Kevin Lai, Jessica Sheu, Timothy Barnes, Katia Star, Manqing Hong, Bosun Min, Ryan Demeter, Ashley Dvorak, Ushati Das Chakravarty, Patrick Lau, Steven Henck. High conversion library preparation with optimal hybridization capture panel design strategy in RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 327.