Browsing by Author "Zhang, Jian"

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    De novo Transcriptome Analysis and Molecular Marker Development of Two Hemarthria Species
    Huang, Xiu; Yan, Haidong; Zhang, Xinquan; Zhang, Jian; Frazier, Taylor P.; Huang, Dejun; Lu, Lu; Huang, Linkai; Liu, Wei; Peng, Yan; Ma, Xiao; Yan, Yan-Hong (Frontiers, 2016-04-18)
    Hemarthria R. Br. is an important genus of perennial forage grasses that is widely used in subtropical and tropical regions. Hemarthria grasses have made remarkable contributions to the development of animal husbandry and agro-ecosystem maintenance; however, there is currently a lack of comprehensive genomic data available for these species. In this study, we used Illumina high-throughput deep sequencing to characterize of two agriculturally important Hemarthria materials, H. compressa "Yaan" and H. altissima "1110." Sequencing runs that used each of four normalized RNA samples from the leaves or roots of the two materials yielded more than 24 million high-quality reads. After de novo assembly, 137,142 and 77,150 unigenes were obtained for "Yaan" and "1110," respectively. In addition, a total of 86,731 "Yawn" and 48,645 "1110" unigenes were successfully annotated. After consolidating the unigenes for both materials, 42,646 high-quality SNPs were identified in 10,880 unigenes and 10,888 SSRs were identified in 8330 unigenes. To validate the identified markers, high quality PCR primers were designed for both SNPs and SSRs. We randomly tested 16 of the SNP primers and 54 of the SSR primers and found that the majority of these primers successfully amplified the desired PCR product. In addition, high cross-species transferability (61.11-87.04%) of SSR markers was achieved for four other Poaceae species. The amount of RNA sequencing data that was generated for these two Hemarthria species greatly increases the amount of genomic information available for Hemarthria and the SSR and SNP markers identified in this study will facilitate further advancements in genetic and molecular studies of the Hemarthria genus.
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    Real-time, MinION-based, amplicon sequencing for lineage typing of infectious bronchitis virus from upper respiratory samples
    Butt, Salman L.; Erwood, Eric C.; Zhang, Jian; Sellers, Holly S.; Young, Kelsey T.; Lahmers, Kevin K.; Stanton, James B. (2020-03)
    Infectious bronchitis (IB) causes significant economic losses in the global poultry industry. Control of IB is hindered by the genetic diversity of the causative agent, infectious bronchitis virus (IBV), which has led to the emergence of several serotypes that lack complete serologic cross-protection. Although serotyping requires immunologic characterization, genotyping is an efficient means to identify IBVs detected in samples. Sanger sequencing of the S1 subunit of the spike gene is currently used to genotype IBV; however, the universal S1 PCR was created to work from cultured IBV, and it is inefficient at detecting multiple viruses in a single sample. We describe herein a MinION-based, amplicon-based sequencing (AmpSeq) method that genetically categorized IBV from clinical samples, including samples with multiple IBVs. Total RNA was extracted from 15 tracheal scrapings and choanal cleft swab samples, randomly reverse transcribed, and PCR amplified using modified S1-targeted primers. Amplicons were barcoded to allow for pooling of samples, processed per manufacturer's instructions into a 1D MinION sequencing library, and then sequenced on the MinION. The AmpSeq method detected IBV in 13 of 14 IBV-positive samples. AmpSeq accurately detected and genotyped both IBV lineages in 3 of 5 samples containing 2 IBV lineages. Additionally, 1 sample contained 3 IBV lineages, and AmpSeq accurately detected 2 of the 3 lineages. Strain identification, including detection of different IBVs from the same lineage, was also possible with this AmpSeq method. Our results demonstrate the feasibility of using MinION-based AmpSeq for rapid and accurate identification and lineage typing of IBV from oral swab samples.
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    Support Vector Machines (SVMs) Based Framework for Classification of Fallers and Non-Fallers
    Zhang, Jian (Virginia Tech, 2014-06-03)
    The elderly population is growing at a rapid pace, and falls are a significant problem facing adults aged 65 and older in terms of both human suffering and economic losses. Falls are the leading cause of mortality among older adults, and non-fatal falls result in reduced function and poor quality of life for older adults. Although much is known about the mechanisms and contributing risk factors relevant to falls, falls still remain a significant problem associated with this age group. Therefore, new strategies and knowledge need to be introduced to understand and prevent falls. Studies show that early detection of impaired mobility is critical to the prevention of falls. In this study, the relationship between gait and postural parameters and falls among elderly participants using wearable inertial sensors was investigated. As such, the aim of this study is to investigate the critical gait and postural parameters contributing to falls, then further to classify fallers and non-fallers by utilizing gait and postural parameters and machine learning techniques, e.g. support vector machines (SVMs). Additionally, as the assessment of fall risk is linked to noisy environment, it is important to understand the capability of the SVM classifier to effectively address noisy data. Therefore, the robustness of the SVM classifier was also investigated in this study. In summary, the presented work addresses several challenges through research on the following three issues: 1) the significant differences in gait and pastoral parameters between fallers and non-fallers; 2) a machine learning based framework for classification of fallers and non-fallers by using only one IMU located at the sternum; and 3) robustness of SVM classifier to classify fallers and non-fallers in a noisy environment. The machine learning based framework developed in this dissertation contribute to advancing the state-of-art in fall risk assessment by 1) classifying fallers and non-fallers from a single IMU located at the sternum; 2) developing machine learning method for classification of fallers and non-fallers; and 3) investigating the robustness of SVM classifier in a noisy environment.
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    Systems Integration of Biodefense Omics Data for Analysis of Pathogen-Host Interactions and Identification of Potential Targets
    McGarvey, Peter B.; Huang, Hongzhan; Mazumder, Raja; Zhang, Jian; Chen, Yongxing; Zhang, Chengdong; Cammer, Stephen; Will, Rebecca; Odle, Margie; Sobral, Bruno; Moore, Margaret; Wu, Cathy H. (Public Library of Science, 2009-09-25)
    The NIAID (National Institute for Allergy and Infectious Diseases) Biodefense Proteomics program aims to identify targets for potential vaccines, therapeutics, and diagnostics for agents of concern in bioterrorism, including bacterial, parasitic, and viral pathogens. The program includes seven Proteomics Research Centers, generating diverse types of pathogen-host data, including mass spectrometry, microarray transcriptional profiles, protein interactions, protein structures and biological reagents. The Biodefense Resource Center (www.proteomicsresource.org) has developed a bioinformatics framework, employing a protein-centric approach to integrate and support mining and analysis of the large and heterogeneous data. Underlying this approach is a data warehouse with comprehensive protein + gene identifier and name mappings and annotations extracted from over 100 molecular databases. Value-added annotations are provided for key proteins from experimental findings using controlled vocabulary. The availability of pathogen and host omics data in an integrated framework allows global analysis of the data and comparisons across different experiments and organisms, as illustrated in several case studies presented here. (1) The identification of a hypothetical protein with differential gene and protein expressions in two host systems (mouse macrophage and human HeLa cells) infected by different bacterial (Bacillus anthracis and Salmonella typhimurium) and viral (orthopox) pathogens suggesting that this protein can be prioritized for additional analysis and functional characterization. (2) The analysis of a vaccinia-human protein interaction network supplemented with protein accumulation levels led to the identification of human Keratin, type II cytoskeletal 4 protein as a potential therapeutic target. (3) Comparison of complete genomes from pathogenic variants coupled with experimental information on complete proteomes allowed the identification and prioritization of ten potential diagnostic targets from Bacillus anthracis. The integrative analysis across data sets from multiple centers can reveal potential functional significance and hidden relationships between pathogen and host proteins, thereby providing a systems approach to basic understanding of pathogenicity and target identification.
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    Transcriptional Changes in Pearl Millet Leaves under Heat Stress
    Huang, Dejun; Sun, Min; Zhang, Ailing; Chen, Jishan; Zhang, Jian; Lin, Chuang; Zhang, Huan; Lu, Xiaowen; Wang, Xiaoshan; Yan, Haidong; Tang, Jianan; Huang, Linkai (MDPI, 2021-10-28)
    High-temperature stress negatively affects the growth and development of plants, and therefore threatens global agricultural safety. Cultivating stress-tolerant plants is the current objective of plant breeding programs. Pearl millet is a multi-purpose plant, commonly used as a forage but also an important food staple. This crop is very heat-resistant and has a higher net assimilation rate than corn under high-temperature stress. However, the response of heat resistant pearl millet has so far not been studied at the transcriptional level. In this study, transcriptome sequencing of pearl millet leaves exposed to different lengths of heat treatment (1 h, 48 h and 96 h) was conducted in order to investigate the molecular mechanisms of the heat stress response and to identify key genes related to heat stress. The results showed that the amount of heat stress-induced DEGs in leaves differs with the length of exposure to high temperatures. The highest value of DEGs (8286) was observed for the group exposed to heat stress for 96 h, while the other two treatments showed lower DEGs values of 4659 DEGs after 1 h exposure and 3981 DEGs after 48 h exposure to heat stress. The DEGs were mainly synthesized in protein folding pathways under high-temperature stress after 1 h exposure. Moreover, a large number of genes encoding ROS scavenging enzymes were activated under heat stress for 1 h and 48 h treatments. The flavonoid synthesis pathway of pearl millet was enriched after heat stress for 96 h. This study analyzed the transcription dynamics under short to long-term heat stress to provide a theoretical basis for the heat resistance response of pearl millet.
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    Transcriptome analysis of heat stress and drought stress in pearl millet based on Pacbio full-length transcriptome sequencing
    Sun, Min; Huang, Dejun; Zhang, Ailing; Khan, Imran; Yan, Haidong; Wang, Xiaoshan; Zhang, Xinquan; Zhang, Jian; Huang, Linkai (2020-07-08)
    Background Heat and drought are serious threats for crop growth and development. As the sixth largest cereal crop in the world, pearl millet can not only be used for food and forage but also as a source of bioenergy. Pearl millet is highly tolerant to heat and drought. Given this, it is considered an ideal crop to study plant stress tolerance and can be used to identify heat-resistant genes. Results In this study, we used Pacbio sequencing data as a reference sequence to analyze the Illumina data of pearl millet that had been subjected to heat and drought stress for 48 h. By summarizing previous studies, we found 26,299 new genes and 63,090 new transcripts, and the number of gene annotations increased by 20.18%. We identified 2792 transcription factors and 1223 transcriptional regulators. There were 318 TFs and 149 TRs differentially expressed under heat stress, and 315 TFs and 128 TRs were differentially expressed under drought stress. We used RNA sequencing to identify 6920 genes and 6484 genes differentially expressed under heat stress and drought stress, respectively. Conclusions Through Pacbio sequencing, we have identified more new genes and new transcripts. On the other hand, comparing the differentially expressed genes under heat tolerance with the DEGs under drought stress, we found that even in the same pathway, pearl millet responds with a different protein.