People

Research interest

The rapid development of high-throughput DNA sequencing technologies and the decrease in sequencing costs have enabled large-scale genomics and genetics studies, particularly for humans and plants. My research interests focus on developing computational and statistical methods to analyze such biological big data. Specifically, I’m working on (1) genetic variant impact interpretation,  (2) genomic privacy, (3) eukaryotic pan-genomics, and (4) alternative splicing and nonsense-mediated mRNA decay.

Publications

First & co-first author papers:

• Zhang L*, Xiao Y*, Li W*, Hu Z*, et al. 2021. UHRF1 regulates alternative splicing by interacting with splicing factors and U snRNAs in a H3R2me involved manner. Human Molecular Genetics 109:ddad178. doi:10.1093/hmg/ddab178
• Zhang F*, Hu Z*, Wu Z*, et al. 2021. Reciprocal adaptation of rice and Xanthomonas oryzae pv. oryzae: cross-species two-dimensional GWAS reveals the underlying genetics. The Plant Cell 33:2538-2561. doi:10.1093/plcell/koab146
• Desai A, Hu Z, et al. 2020. Networks of Splice Factor Regulation by Unproductive Splicing Coupled With Nonsense Mediated mRNA Decay. bioRxiv. doi:10.1101/2020.05.20.107375
• Hu Z, Wei C, Li Z. 2020. Computational Strategies for Eukaryotic Pan-Genome Analyses. Springer Nature. The Pangenome, p.293.Book chapter. doi:10.1007/978-3-030-38281-0_13
• Hu Z, Yu C, et al. 2019. VIPdb, a genetic variant impact predictor database. Human Mutation. doi:10.1002/humu.23858
• Wang W*, Mauleon R*, Hu Z*, et al. 2018. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557(7003):43. doi:10.1038/s41586-018-0063-9
• Hu Z, Wang W, et al. 2018. Novel sequences, structural variations and gene presence variations of Asian cultivated rice. Scientific Data 5:180079. doi:10.1038/sdata.2018.79
• Hu Z, Sun C, et al. 2017. EUPAN enables high-resolution pan-genome studies of a large number of eukaryotic genomes. Bioinformatics 33:2408-2409. PMID:28369371. doi:10.1093/bioinformatics/btx170
• Li X*, Hu Z*, et al. 2017. Dosage compensation in the process of inactivation/reactivation during both germ cell development and early embryogenesis in mouse. Scientific Reports 7:3729. PMID:28623283. doi:10.1038/s41598-017-03829-z
• Zhu H*, Xu W*, Hu Z*, et al. 2017. RNA virus receptor Rig-I monitors gut flora and suppresses colitis-associated colorectal cancer. Experimental & Clinical Cancer Research 36:2. PMID:28057020. doi:10.1186/s13046-016-0471-3
• Sun C*, Hu Z*, et al. 2016. The 3,000 rice pan-genome browser. Nucleic Acids Research 45:597-605. PMID:27940610. doi:10.1093/nar/gkw958
• Chu X*, Zhou Y*, Hu Z*, et al. 2016. 24-hour-restraint stress induces long-term depressive-like phenotypes in mice. PMID:27609090. Scientific Reports 6:32935. doi:10.1038/srep32935
• Hu Z, Scott HS, et al. 2015. Revealing missing human protein isoforms based on ab initio prediction, RNA-seq and proteomics. PMID:26156868. Scientific Reports 5:10940. doi:10.1038/srep10940

Other papers:

• Sharo AG, Hu Z, Steven SE. (2022) StrVCTVRE: A supervised learning method to predict the pathogenicity of human structural variants. American Journal of Human Genetics 109:195-209. doi:10.1016/j.ajhg.2021.12.007
• Kuzuoglu-Ozturk D, Hu Z, et al. (2021) Revealing molecular pathways for cancer cell fitness through a genetic screen of the cancer translatome. Cell Reports 35(13):109321. doi:10.1016/j.celrep.2021.109321
• Zhang F, et al. (2021) The landscape of gene–CDS–haplotype diversity in rice: properties, population organization, footprints of domestication and breeding, and implications for genetic improvement. Molecular Plant 14(5):787-804. doi:10.1016/j.molp.2021.02.003
• Wang L, et al. (2020) RALF1-FERONIA complex affects splicing dynamics to modulate stress responses and growth in plants. Science Advances 6(21):eaaz1622. doi:10.1126/sciadv.aaz1622
• Clark WT, Kasak L, Bakolitsa C, Hu Z, et al. 2019. Assessment of predicted enzymatic activity of alpha-N-acetylglucosaminidase (NAGLU) variants of unknown significance for CAGI 2016. Human Mutation. doi:10.1002/humu.23875
• Kasak L, et al. 2019. CAGI SickKids challenges: Assessment of phenotype and variant predictions derived from clinical and genomic data of children with undiagnosed diseases. Human Mutation. doi:10.1002/humu.23874
• Kasak L, Bakolitsa C, Hu Z, et al. 2019. Assessing Computational Predictions of the Phenotypic Effect of Cystathionine-beta-Synthase Variants. Human Mutation. doi:10.1002/humu.23868
• Duan Z, et al. 2019. HUPAN: a pan-genome analysis pipeline for human genomes. Genome Biology 20:149. doi:10.1186/s13059-019-1751-y
• Carraro M, et al. 2019. Assessment of patient clinical descriptions and pathogenic variants from gene panel sequences in the CAGI‐5 intellectual disability challenge. Human Mutation. doi:10.1002/humu.23823
• Wang R, Wang Y, Hu Z. 2019. Using secondary structure to predict the effects of genetic variants on alternative splicing. Human Mutation. doi:10.1002/humu.23790
• Yan Z, et al. 2018. Loci and natural alleles underlying robust roots and adaptive domestication of upland ecotype rice in aerobic conditions. PLoS Genetics 14:e1007521. doi:10.1371/journal.pgen.1007521
• Qiao Y, Jia B, Hu Z, et al. 2018. MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms. Biology Direct 13:15. doi:10.1186/s13062-018-0220-y
• Zeng L, Pederson S, Cao D, Qu Z, Hu Z, et al. 2018. Genome-wide analysis of repetitive elements associated with gene regulation. Journal of Computational Biology.  doi:10.1089/cmb.2017.0228
• Jia B, Xuan L, Cai K, Hu Z, et al. 2013. NeSSM: a Next-generation Sequencing Simulator for Metagenomics. PLoS One 8:e75448. PMID:24124490. doi:10.1371/journal.pone.0075448