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BioSkryb’s ResolveOME Whole Genome and Transcriptome Amplification Kit combines our breakthrough whole-genome amplification (WGA) technology, Primary Template-directed Amplification (PTA), with full-transcript reverse transcription, and our innovative BaseJumper™ computational tools to pave the way for comprehensive single-cell multiomic analysis. Capable of near-complete coverage of the genome and mRNA transcriptome, our(the) ResolveOME Kit meld genome variation data with transcriptional and translational layers of information to provide a more complete picture of the drivers and consequences of clonal heterogeneity within cell populations than ever before.

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Breadth of data accessible with the ResolveOME Whole Genome and Transcriptome Amplification Kit. Unlike droplet-based single-cell DNAseq and 3’-end counting RNAseq platforms, the ResolveOME workflow offers a comprehensive view of the genome, mRNA transcriptome, and inferred impacts of protein sequence alterations. ResolveOME Whole Genome and Transcriptome Amplification Kit supports more modalities (number of segments), and typically offer more complete coverage within individual modalities (length of segments, normalized to 100%). Although droplet-based methods offer one to two logs higher throughput, our ResolveOME Kit yields data from a significantly higher proportion (>3-fold) of input cells. Data dials were generated using a combination of quantitative and qualitative internal and published data.

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The ResolveOME Product Solution enables insights into the identity, role, and fate of individual cells that are:

  • Comprehensive: >90% genome and full-length mRNA coverage reveals the consequence of genomic variation (all major variant classes) on gene expression and transcript structure.
  • Versatile: compatible with all viable, fresh/frozen cells to support a wide range of applications in oncology, neurology, immunology, cardiology, reproductive medicine, microbiology, and bioprocessing.
  • Accurate: a unified worflow for the interrogation of DNA and RNA from the same cell obviates the need for splitting source material or interpret across data sets.
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Delivering high-quality, multiomic data sets at single-cell resolution

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