# Robust and cost-effective computational methods for haplotype-resolved genome assemblies > **NIH NIH R00** · YALE UNIVERSITY · 2024 · $249,000 ## Abstract Abstract Background: De nova haplotype-resolved genome assembly not only plays a critical role in the studies of novel species, but also is the most comprehensive solution to discover structural variants and understand repeat-rich regions of the human genome. Moreover, haplotype-resolved assemblies are the fundamental infrastructures for various pangenome references. Recent advances in accurate long-read sequencing technologies open the opportunity to faithfully build high-quality haplotyperesolved assemblies, but most assembly algorithms could not take full advantage of the emerging accurate long-read data. To this end, I have developed a graph-based haplotype-resolved genome assembly algorithm, called hifiasm, which combines accurate long reads with the additional data providing long-range phasing information. Hifiasm has been widely used by multiple large-scale sequencing projects, such as the Human Pangenome Reference Consortium (HPRC), the Genome in a Bottle (GIAB), the Vertebrate Genomes Project (VGP), and the Darwin Tree of Life project. Based on hifiasm, this proposal focuses on developing a set of new haplotype-resolved assembly algorithms to further improve the assembly quality for complex regions and genomes, as well as substantially reduce the assembly cost. Research: My first aim is to develop a hybrid algorithm to produce high-quality haplotype-resolved assemblies for diploid genomes, especially focusing on resolving highly repetitive regions like centromeres. The proposed algorithm will combine the advantages of length and accuracy from different types of long-read data to automatically reconstruct the last unexplored repeat-rich regions of the genome. In the second aim, I will develop a haplotype-aware scaffolding algorithm to achieve chromosome-level haplotype-resolved assemblies for diploid genomes. In the third aim, I will propose different strategies to reduce the sequencing cost and the computational cost of the haplotype-resolved assembly, making it feasible for populationscale studies. I will also develop assembly algorithms to resolve complex genomes including not only two haplotypes. Upon completion, the proposed studies will offer efficient assembly tools for large-scale sequencing projects, and will pave the way to personal genome assembly for genomic research and clinical applications. Career development and training: My long-term career goal is to lead an independent research group focusing on developing novel computational methods for haplotype-resolved assemblies and the relevant applications. In addition to further enhancing my training in computational method development with my mentor Dr. Heng Li, I will obtain systematic training in biomedical research from the advisory committee (Dr. Erich D. Jarvis and Dr. Scott V. Edwards for human and non-human genomes, Dr. Evan E. Eichler and Dr. Karen H. Miga for repeats and structural variations, as well as Dr. Matthew Meyerson for complex genomes including not only two h... ## Key facts - **NIH application ID:** 11179519 - **Project number:** 4R00HG012798-03 - **Recipient organization:** YALE UNIVERSITY - **Principal Investigator:** Haoyu Cheng - **Activity code:** R00 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $249,000 - **Award type:** 4N - **Project period:** 2023-02-13 → 2027-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/11179519 ## Citation > US National Institutes of Health, RePORTER application 11179519, Robust and cost-effective computational methods for haplotype-resolved genome assemblies (4R00HG012798-03). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/11179519. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*