Current research projects:
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Quantitative genetics on Drosophila melanogaster pupal-stage traits
Quantitative traits usually have a polygenic genetic basis, with relative small effect size from each associated genetic loci. Genome-wide association (GWA) analysis represents a main research paradigm for detection of association variants of quantitative traits, for which an arbitrary statistical threshold is defined. However, the correlation of the statistical significances of genetic variants and their "true" functional effects is largely unexplored. To this aim, we exploited a high-throughput automated phenotyping platform for analysis of quantitiative (morphological and behavioral) traits in the pupal stage of Drosophila melanogaster. Additionally, taking advantage of the ease of genetic mutagenesis in Drosophila melanogaster, we are capable to quantify the phenotypic effects at single gene level, in comparison of their statistical significances from GWA analysis.
Related publication:
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Wenyu Zhang, Guy Reeves, and Diethard Tautz. Identification of a genetic network for an ecologically relevant behavioral phenotype in Drosophila melanogaster. Mol Ecol. 2019 Dec 23. doi: 10.1111/mec.15341. [Link]
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Wenyu Zhang, Guy Reeves, and Diethard Tautz. Testing the omnigenic model for pupal-stage related quantitative traits in Drosophila melanogaster. (In prep)
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Populaion genomics with house mouse as a system
The house mouse (Mus musculus) is the most successful and ubiquitous invasive mammal after man, mainly due to its commensal interaction with humans, which have allowed it to reach and thrive in places outside its natural bounds. After the divergence from the source population in Central/South Asia, three major lineages of Mus musculus (classified as subspecies) are distinguished: the western house mouse Mus mus domesticus, the eastern house mouse Mus mus musculus, and the southeast-Asia house mouse Mus mus castaneus. With the collection of wild mice individuals caught from multiple geographic regions of all these three major lineages, we are combining genomic and transcriptomic sequencing data to trace the origination and adaptation of genetic signatures in the divergence of house mouse wild populations.
Related publication:
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Wenyu Zhang, Chen Xie, Kristian Ullrich, and Diethard Tautz. Characterization of the landscape of gene retroposition in the natural populations of house mouse (Mus musculus). (In prep)
Past research projects:
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Orphan genes and de novo genes in Caenorhabditis elegans
Orphan genes are genes without detectable homologues in other lineages. As a special type of orphan genes, de novo genes are genes that de novo originated from non-coding DNA sequences. Compared with duplication-based new genes, the origination and function mechanisms of orphan genes and de novo genes remain largely unexplored. Herein, through a comprehensive and systematic computational pipeline, we identified 893 orphan genes in the lineage of C. elegans, of which only a low fraction (0.9%) were derived from transposon elements. Six new protein-coding genes that de novo originated from non-coding DNA sequences in the genome of C. elegans were also identified. Orphan genes and de novo genes exhibited simple gene structures, such as, short in protein length, of fewer exons, and are frequently X-linked. RNA-seq data analysis showed these orphan genes are enriched with expression in embryo development and gonad, and their potential function in early development was further supported by gene ontology enrichment analysis results. Meanwhile, de novo genes were found to be with significant expression in gonad, and functional enrichment analysis of the co-expression genes of these de novo genes suggested they may be functionally involved in signaling transduction pathway and metabolism process. Our results presented the first systematic evidence on the evolution of orphan genes and de novo origin of genes in nematodes and their impacts on the functional and phenotypic evolution, and thus could shed new light on our appreciation of the importance of these new genes.
Related publication:
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Wenyu Zhang, Yuanxiao Gao, Manyuan Long, and Bairong Shen. Origination and evolution of orphan genes and de novo genes in the genome of Caenorhabditis elegans. Sci China Life Sci. 2019 Apr;62(4):579-593. doi: 10.1007/s11427-019-9482-0. [Link]
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Evolution of gene interaction networks from the view of new gene origination
The origin of new genes with novel functions creates genetic and phenotypic diversity in organisms. To acquire functional roles, new genes must integrate into ancestral gene-gene interaction (GGI) networks. However, the mechanisms by which new genes are integrated into ancestral networks, and their evolutionary significance, are yet to be characterized. In this project, we presented a study investigating the rates and patterns of new gene-driven evolution of GGI networks in the human and mouse genomes. We examined the network topological and functional evolution of new genes that originated at various stages in the human and mouse lineages by constructing and analyzing three different GGI datasets. We found a large number of new genes integrated into GGI networks throughout vertebrate evolution. These genes experienced a gradual integration process into GGI networks, starting on the network periphery and gradually becoming highly connected hubs, and acquiring pleiotropic and essential functions. We identified a few human lineage-specific hub genes that have evolved brain development-related functions. Finally, we explored the possible underlying mechanisms driving the GGI network evolution and the observed patterns of new gene integration process. Our data cast new conceptual insights into the evolution of genetic networks.
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Wenyu Zhang, Patrick Landback, Andrea R Gschwend, Bairong Shen, and Manyuan Long. New genes drive the evolution of gene interaction networks in the human and mouse genomes. Genome Biol. 2015 Oct 1;16:202. doi: 10.1186/s13059-015-0772-4. [Link]
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Jian Zu, Yuexi Gu, Yu Li, Chentong Li, Wenyu Zhang, Yong E. Zhang, UnJin Lee, Li Zhang, and Manyuan Long. Topological evolution of coexpression networks by new gene integration maintains the hierarchical and modular structures in human ancestors. Sci China Life Sci. 2019 Apr;62(4):594-608. doi: 10.1007/s11427-019-9483-6. [Link]
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Computational discovery of miRNA biomarkers for cancer diagnosis
MicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers. Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. Nevertheless, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer.
Previous studies have provided evidence of multiple-to-multiple relationships between miRNAs and their target genes. However, our in-depth analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. Based on these observations, we developed a novel cancer miRNA biomarker prediction framework, and applied this approach for the discovery of miRNA biomarkers in several human cancers. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results.
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Wenyu Zhang(*), Jin Zang(*), Xinhua Jing, Zhandong Sun, Wenying Yan, Dongrong Yang, Feng Guo, and Bairong Shen. Identification of candidate miRNA biomarkers from miRNA regulatory network with application to prostate cancer. J Transl Med. 2014 Mar 11;12:66. doi: 10.1186/1479-5876-12-66. [Link]
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Wenyu Zhang and Bairong Shen (2013). Chapter 8: Identification of Cancer MicroRNA Biomarkers Based on miRNA–mRNA Network. Bioinformatics for Diagnosis, Prognosis and Treatment of Complex Diseases. Translational Bioinformatics Volume 4. Springer, Dordrecht. [Link]
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Jiajia Chen, Daqing Zhang, Wenyu Zhang, Yifei Tang, Wenying Yan, Lingchuan Guo, and Bairong Shen. Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis. J Transl Med. 2013 Jul 10;11:169. doi: 10.1186/1479-5876-11-169. [Link]
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Jin Zhu(*), Sugui Wang(*), Wenyu Zhang(*), Junyi Qiu, Yuxi Shan, Dongrong Yang, and Bairong Shen. Screening key microRNAs for castration-resistant prostate cancer based on miRNA/mRNA functional synergistic network. Oncotarget. 2015 Dec 22; 6(41):43819-30. doi: 10.18632/oncotarget.6102. [Link]
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Wenying Yan, Lihua Xu, Zhandong Sun, Yuxin Lin, Wenyu Zhang, Jiajia Chen, Shaoyan Hu, and Bairong Shen. MicroRNA biomarker identification for pediatric acute myeloid leukemia based on a novel bioinformatics model. Oncotarget. 2015 Sep 22;6(28):26424-36. doi: 10.18632/oncotarget.4459. [Link]
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Practical evaluation on the next-generation sequencing data analysis tools
Next-generation sequencing was, and still is (will still be in the near futhre), the dominant sequencing techology in genomic analysis field. The fast developing of next-generation sequencing technologies is accompanied with the development of many whole-genome sequence analysis methods and software, especially for de novo fragment assembly and short read alignment processes. Due to the poor knowledge about the applicability and performance of these software tools, choosing a befitting one becomes a tough task. In this project, we firstly systematically reviewed exisiting analysis tools on the de novo fragment assembly and short read alignment, and then compared the performance of these tools with both simulated datasets and real datasets from various sequencing platforms. We provided the information of adaptivity for each program, and the performance features of these tools, in terms of running time, memory usage, accuracy and sensitivity. Our comparison study could assist researchers in selecting a well-suited analysis tools and offer essential information for the improvement of existing methods or the developing of novel analysis tools.
Related publication:
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Wenyu Zhang, Jiajia Chen, Yang Yang, Yifei Tang, Jing Shang, and Bairong Shen. A practical comparison of de novo genome assembly software tools for next-generation sequencing technologies. PLoS One. 2011 Mar 14;6(3):e17915. doi: 10.1371/ journal.pone.0017915. [Link]
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Jing Shang, Fei Zhu, Wanwipa Vongsangnak, Yifei Tang, Wenyu Zhang, and Bairong Shen. Evaluation and comparison of multiple aligners for next-generation sequencing data analysis. Biomed Res Int. 2014:309650. doi: 10.1155/2014/309650. [Link]