A single-strand ligation approach for concomitant DNA and RNA next-generation sequencing from low abundance inputs

Ended Project Grant Awarded Last action

Award ID
R43HG011877
Recipient
Awarding agency
Obligated
$673,188

Award facts

Assistance listing
93.172 Human Genome Research
Completion date
Originally
Funding
  • $448,792.00 Federal
  • $224,396.00 Non-federal
Place of performance
California, USA

Award details come from USAspending.gov.

Description

Abstract
Integrated genomic and transcriptomic analyses capture a wealth of information that can improve our understanding of normal biological phenomena and disease. Combining DNA and RNA next-generation sequencing (NGS) data from the same sample can improve predictive power of diagnostic and prognostic assays. However, performing multiomic analyses is often not feasible due to limited sample availability or low nucleic acid yields especially in single-cells and liquid biopsies. Extracellular nucleic acids derived from liquid biopsies such as plasma, urine and cerebrospinal fluid are increasingly used for rapid and non-invasive disease detection and treatment response monitoring in cancer and infectious diseases. An integrated protocol that captures both DNA and RNA can be of tremendous value in liquid biopsy-based diagnostics by saving precious clinical samples and reducing reagents requirement and sequencing costs. Traditional NGS library preparation methods for DNA and RNA have some overlap in protocol and typically utilize a common sequencing platform. However, these methods are cannot be adapted for simultaneous library generation due to substrate and enzymatic limitations. In contrast, claret bioscience's innovative single-strand ligation approach facilitates direct library preparation from both RNA and DNA in three simplified steps. The method also adds nucleic acid-specific molecular barcodes which enable in silico deconvolution of DNA and RNA-derived sequences, making this method ideal for comprehensive NGS analyses. In this proposal, we seek to develop and optimize a concomitant DNA and RNA NGS library preparation protocol using a contrived mixture of fragmented human genomic DNA and yeast RNA along with spike-in controls (which serve as "ground truth") as input. We will perform a series of experiments to test the efficacy of the protocol using a range of input amount and variable ratios of DNA and RNA to mimic the compositions frequently encountered in clinical samples. We will evaluate the stringency of adapter ligation using the nucleic acid-specific barcode and generate a user-friendly bioinformatic pipeline that will ultimately accompany the assay, for easy bioinformatic extraction of genomic and transcriptomic data from a given library. To evaluate protocol performance, we determine if the molecular, sequencing and mapping metrics of our method are equivalent to the metrics obtained with individual library preparation protocols, for both DNA and RNA. Finally, we will demonstrate biological utility of the method using our in-house repository of cell-free nucleic acids derived from healthy plasma samples. Successful accomplishment of these aims will generate a robust assay that simultaneously generates DNA and RNA NGS libraries within four hours. When combined with minimally invasive liquid biopsy-based approaches, the combinatorial NGS assay described here can significantly reduce financial and physical burden on the patient.

Transactions

Action Federal Non-federal Date
Amendment $0 $0
Amendment $224,396 $0
Original award $224,396 $224,396

Quick answers

Who received award R43HG011877?

R43HG011877 was awarded to Claret Bioscience LLC by National Human Genome Research Institute (National Institutes of Health) on September 7, 2021.

How much is award R43HG011877 worth?

$673,188 has been obligated on R43HG011877: $448,792 in federal funding and $224,396 in non-federal funding.

On GovTribe

See this award's full record

  • Every transaction and subaward
  • The recipient's other awards and teaming partners
  • Similar grant awards
  • Ask GovTribe AI about this award