Browsing by Author "Song, Qi"
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- Developing machine learning tools to understand transcriptional regulation in plantsSong, Qi (Virginia Tech, 2019-09-09)Abiotic stresses constitute a major category of stresses that negatively impact plant growth and development. It is important to understand how plants cope with environmental stresses and reprogram gene responses which in turn confers stress tolerance. Recent advances of genomic technologies have led to the generation of much genomic data for the model plant, Arabidopsis. To understand gene responses activated by specific external stress signals, these large-scale data sets need to be analyzed to generate new insight of gene functions in stress responses. This poses new computational challenges of mining gene associations and reconstructing regulatory interactions from large-scale data sets. In this dissertation, several computational tools were developed to address the challenges. In Chapter 2, ConSReg was developed to infer condition-specific regulatory interactions and prioritize transcription factors (TFs) that are likely to play condition specific regulatory roles. Comprehensive investigation was performed to optimize the performance of ConSReg and a systematic recovery of nitrogen response TFs was performed to evaluate ConSReg. In Chapter 3, CoReg was developed to infer co-regulation between genes, using only regulatory networks as input. CoReg was compared to other computational methods and the results showed that CoReg outperformed other methods. CoReg was further applied to identified modules in regulatory network generated from DAP-seq (DNA affinity purification sequencing). Using a large expression dataset generated under many abiotic stress treatments, many regulatory modules with common regulatory edges were found to be highly co-expressed, suggesting that target modules are structurally stable modules under abiotic stress conditions. In Chapter 4, exploratory analysis was performed to classify cell types for Arabidopsis root single cell RNA-seq data. This is a first step towards construction of a cell-type-specific regulatory network for Arabidopsis root cells, which is important for improving current understanding of stress response.
- Genome Wide Association Study and Genomic Selection of Amino Acid Concentrations in Soybean SeedsQin, Jun; Shi, Ainong; Song, Qijian; Li, Song; Wang, Fengmin; Cao, Yinghao; Ravelombola, Waltram; Song, Qi; Yang, Chunyan; Zhang, Mengchen (2019-11-15)Soybean is a major source of protein for human consumption and animal feed. Releasing new cultivars with high nutritional value is one of the major goals in soybean breeding. To achieve this goal, genome-wide association studies of seed amino acid contents were conducted based on 249 soybean accessions from China, US, Japan, and South Korea. The accessions were evaluated for 15 amino acids and genotyped by sequencing. Significant genetic variation was observed for amino acids among the accessions. Among the 231 single nucleotide polymorphisms (SNPs) significantly associated with variations in amino acid contents, fifteen SNPs localized near 14 candidate genes involving in amino acid metabolism. The amino acids were classified into two groups with five in one group and seven amino acids in the other. Correlation coefficients among the amino acids within each group were high and positive, but the correlation coefficients of amino acids between the two groups were negative. Twenty-five SNP markers associated with multiple amino acids can be used to simultaneously improve multi-amino acid concentration in soybean. Genomic selection analysis of amino acid concentration showed that selection efficiency of amino acids based on the markers significantly associated with all 15 amino acids was higher than that based on random markers or markers only associated with individual amino acid. The identified markers could facilitate selection of soybean varieties with improved seed quality.
- Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methodsHaidong, Yan; Lee, Jiyoung; Song, Qi; Li, Qi; Schiefelbein, John; Zhao, Bingyu; Li, Song (2022-02-24)An essential step in the analysis of single-cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single-cell predictive marker (SPmarker) to identify novel cell-type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single-cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non-Arabidopsis species, which expands the number of marker genes for this cell type by 35–154%. Our results represent a new approach to identifying cell-type marker genes from scRNA-seq data and pave the way for cross-species mapping of scRNA-seq data in plants.
- Identification of regulatory modules in genome scale transcription regulatory networksSong, Qi; Grene, Ruth; Heath, Lenwood S.; Li, Song (2017-12-15)Background In gene regulatory networks, transcription factors often function as co-regulators to synergistically induce or inhibit expression of their target genes. However, most existing module-finding algorithms can only identify densely connected genes but not co-regulators in regulatory networks. Methods We have developed a new computational method, CoReg, to identify transcription co-regulators in large-scale regulatory networks. CoReg calculates gene similarities based on number of common neighbors of any two genes. Using simulated and real networks, we compared the performance of different similarity indices and existing module-finding algorithms and we found CoReg outperforms other published methods in identifying co-regulatory genes. We applied CoReg to a large-scale network of Arabidopsis with more than 2.8 million edges and we analyzed more than 2,300 published gene expression profiles to charaterize co-expression patterns of gene moduled identified by CoReg. Results We identified three types of modules in the Arabidopsis network: regulator modules, target modules and intermediate modules. Regulator modules include genes with more than 90% edges as out-going edges; Target modules include genes with more than 90% edges as incoming edges. Other modules are classified as intermediate modules. We found that genes in target modules tend to be highly co-expressed under abiotic stress conditions, suggesting this network struture is robust against perturbation. Conclusions Our analysis shows that the CoReg is an accurate method in identifying co-regulatory genes in large-scale networks. We provide CoReg as an R package, which can be applied in finding co-regulators in any organisms with genome-scale regulatory network data.
- Prediction of condition-specific regulatory genes using machine learningSong, Qi; Lee, Jiyoung; Akter, Shamima; Rogers, Matthew; Grene, Ruth; Li, Song (Oxford University Press, 2020-06-19)Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.