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Rice Stress Transcriptome Analysis – A Network Biology ApproachAuthor: SANCHARI SIRCAR Date: 2020-06-03 Report no: IIIT/TH/2020/42 Advisor:Nita Parekh AbstractRice is a major staple food for half the world’s population. With the completion of rice genome sequencing projects and the availability of several high-throughput experimental platforms, a large amount of data is now available in the public domain. However, there still exists a gap between data generation and effective bioinformatic analyses of such data in the scientific community. In the era of Big Data, network-based approaches have made great strides in the field of biomedical research, personal therapeutics as well as in evo-devo studies. Taking cues from these studies, in this thesis, we use a network biology approach to get a systems-level understanding of the rice stress transcriptome, using microarray and RNA-seq data. In the first part of the study, we considered transcriptome data from a drought tolerant rice genotype and constructed a weighted condition-dependent gene co-expression network. Based on differential gene expression analysis, we identified tissue and stage-specific co-expressed modules harbouring differentially expressed genes that are induced or repressed due to stress. Network topological properties such as degree along with fold-change information is used to identify “hub” genes. Unannotated genes that are topologically important in the network and co-clustered with known stress-responsive genes are selected for functional characterization. Network-based concepts such as conserved network neighbourhood and guide-gene approach for the annotation of these novel drought-responsive genes together with promoter analysis led to the annotations of 26 uncharacterized genes. We also used alternate network construction methods to confirm the associations of the uncharacterized genes with their conserved neighbours. In the second part of the thesis we present a meta-analytic study combining gene-expression data from seven drought-tolerant genotypes to identify various drought-adaptive processes in leaf tissue using network-based approach. Here, we propose an integrated approach that incorporates protein-protein interactions with co-expression of genes to construct networks of up and down-regulated genes and identify tightly-coupled gene clusters. Based on the processes/pathways represented by these clusters, we identified some important drought adaptive processes exhibited by these genotypes. Key transcription factors, viz., bZIPs, ABA signalling machinery and interaction of its signalling components with metabolic pathways playing a role in stress adaptive pathways was highlighted. In tandem do this, stomatal regulation and photosynthesis were observed to be important among the down-regulated processes. In the final part of the thesis we present our analysis of RNAseq data to analyze stress responsive processes in rice under different abiotic stress conditions, namely, drought, cold, flood, salinity, etc. For this study, publicly available large-scale mRNA sequencing data of rice(Oryza sativa L. cv. Nipponbare) obtained under uniform experimental conditions for different abiotic stress conditions and time-points was considered. A weighted co-expression network was constructed across four stress conditions and two hormone treatments using differentially expressed genes (DEGs). Co-expressed modules enriched with DEGs with respect to different stress/treatment conditions were identified. Two type of analyses are presented: (i) module-based analysis to identify metabolic pathways that are affected under a given stress condition, and (ii) time-course analysis of the signalling pathways involved in stress response Full thesis: pdf Centre for Computational Natural Sciences and Bioinformatics |
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